List of Articles network

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Identify and assess the relative importance of knowledge management strategies by using ANN (Case study Knowledge base Software Companies)
  Saeedeh khabbazkar Mohsen Shafiei Nikabadi مائده  دهقان
  Abstract: Knowledge management is an important resource for any organization. Organizations to implement knowledge management strategies, improve innovation in processes, activities, products and services. The aim of this study is to identify the key strategies of knowl More

  Abstract: Knowledge management is an important resource for any organization. Organizations to implement knowledge management strategies, improve innovation in processes, activities, products and services. The aim of this study is to identify the key strategies of knowledge management by ANN .The innovative aspect of the research is, the use of artificial neural networks (ANN) to rank the strategies of knowledge management. The population consists of the all employees of the   knowledge based software companies in Tehran, that the total questionnaires were distributed, only 123 were usable. This study is practical from the objective aspect, and descriptive-survey from data collection method aspect. Data from the surveys and questionnaires obtained and then by using the ANN techniques h as been investigated the research objectives.  Results and ANN outputs indicated that sequencely, explicit knowledge startegy is the most important criteria of Knowledge management strategy and tacit khowledge, internal and external strategy are the next priorities  knowledge based  software companies  are located in Tehran.  Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Evaluating Indicators of Organizational Agility by Fuzzy Multi Criteria Decision Making: (Iran Power Development Organization)
  Zeinolabedin Akbarzadeh Mohammad valipourkhatir Mohammad mohammadpouromran
  Agility makes it possible for organizations in today's complex and constantly changing environment to ensure their survival with rapid, innovative and creative responses. In this regard, the present study attempts to study main indicators of agility in Iran Power Develo More

  Agility makes it possible for organizations in today's complex and constantly changing environment to ensure their survival with rapid, innovative and creative responses. In this regard, the present study attempts to study main indicators of agility in Iran Power Development Organization (IPDO). Firstly, four main criterions referring to the literature selected include of “leveraging impact of people and information”, “cooperate to enhance competitiveness”, “enriching customer”, “mastering change and uncertainty”. Subsequently, by designing questionnaire and determining it’s content validity by academic members, intuitive data from IPDO experts has been collected. Then, reliability has been evaluated using Gous method for consistency examination of pairwise comparisons matrix. Finally data were analyzed using fuzzy multi-criteria decision making technique. The findings shows that “cooperate to enhance competitiveness” with the importance degree (0.301) is the most importance of the main criterion for selecting the more agile organizational unit. While in Iran Power Development Organization it has third priority. The main criteria “leveraging impact of people and information” with the importance degree (0.277), “mastering change and uncertainty” with the importance degree (0.238), “enriching customer” with the importance degree (0.184), were three next most important criterion in the selection of organizational units are more agile. The results point out that IPDO should be more focus on the priority of indicators affecting the agility to minimize the gap between current and desired situation. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - The effect of online social networks on home business innovation performance: The mediating role of innovation capacity
  Morteza Akbari peyman dolatshah mojgan danesh
  Nowadays, activity in online social networks has become inclusive, and these new tools have a high potential for the acquisition of business information and the needs of customers through collective intelligence; the use of this information can stimulate the creation of More

  Nowadays, activity in online social networks has become inclusive, and these new tools have a high potential for the acquisition of business information and the needs of customers through collective intelligence; the use of this information can stimulate the creation of innovative products and processes in business. Also, the existence of high innovation capacity in business can facilitate the process of improving innovation performance. Therefore, in this research we have tried to examine the effect of using online social networks on improving the innovation performance of home business in Isfahan province and It will be clear how the innovation capacity of these businesses will affect this relationship. This study was conducted on 220 home-based businesses in Isfahan, and with  available  sampling were selected. uses a questionnaire for collecting data. The present research is an applied and descriptive analytical-survey method in terms of its purpose. For analyze the data structural equation modeling were used. The results showed that higher levels of online social networks could increase the likelihood of improving innovation performance. Also, innovation capacity can be a stimulus to use of online social networks and thus improve the innovation performance in the home business. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  4 - Effects of Network Structure, Knowledge Stock and Absorptive Capacity on Innovative Performance of Knowledge- Based Companies
  Morteza Akbari saheb imani roya mahmoudi hoda abedi hadi toloasl
  The innovative performance of companies has been studied quite extensively and for a long period of time. The aim of this study was to investigate the effects of network structure, knowledge stock and absorptive capacity on innovative performance of Tehran knowledge- ba More

  The innovative performance of companies has been studied quite extensively and for a long period of time. The aim of this study was to investigate the effects of network structure, knowledge stock and absorptive capacity on innovative performance of Tehran knowledge- based companies. About 132 companies were selected as the sample in Tehran province, Iran. In order to collect data, standard questionnaires of innovative performance with 10 questions, absorption capacity with 8 questions, network structure with 4 questions, and knowledge stock were also used with 7 questions. In all items were measured using a five-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree). Data collected through a questionnaire which its validity confirmed by experts and its reliability was confirmed by Cronbach's alpha coefficient. Data were analyzed using structural equation modeling (SEM) software Smart- PLS 2.0. The results showed that the network structure, knowledge stock and absorptive capacity effect on performance knowledge-based companies. Also, the absorptive capacity have had partial and complete (perfect) mediating role on the relationship between knowledge stock with innovative performance and network structure with innovative performance. In addition, the absorptive capacity (0.48) has the largest and network structure has the lowest rank in explaining performance of innovative companies. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  5 - Vehicle Navigation in Urban Environments based on Vehicular Communications
  Saleh Yousefi
    This paper deals with the problem of obtaining the optimum path for a vehicle to its destination. We propose architecture based on which vehicles are able to find the best path toward their destination using real-time traffic information thus, travel time is reduced More

    This paper deals with the problem of obtaining the optimum path for a vehicle to its destination. We propose architecture based on which vehicles are able to find the best path toward their destination using real-time traffic information thus, travel time is reduced and roads traffic capacity is increased. Pervious system, aiming at the same goal, use traffic flow prediction thus their information is not real-time and even precise. Moreover, some systems use GPS data but due to coverage limitations (in dense city environments, tunnels) permanent accessibility may not be provided. In this paper, thus, we propose architecture for such a system based on communication between vehicles and road side infrastructure. We first propose a scheme for Road Side Unit (RSU) placement so that the maximum coverage with minimum numbers of RSUs is achieved. Then an algorithm is proposed for information gathering though which real-time traffic information is circulated between all RSUs. Finally this information is used for navigation of vehicles toward their destination during city trips. Results of simulation study conducted by NTCUns shows the good performance of the proposed idea.    Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  6 - An Efficient Approach to Detect Faulty Readings: Applicability in Long-Thin Wireless Sensor Networks
  Seyyed Jalaleddin Dastgheib
  Wireless sensor networks (WSN) are composed of thousands small nodes (sensors), which work together and are associated with specific tasks to do. A long-thin network topology of wireless sensor can produce errors in network localization due to special deployment of node More

  Wireless sensor networks (WSN) are composed of thousands small nodes (sensors), which work together and are associated with specific tasks to do. A long-thin network topology of wireless sensor can produce errors in network localization due to special deployment of nodes also, In this structure, failure of some close together nodes may pull some parts of network into isolation, or in a worse case the entire network may stop working.. In this paper, we propose an optimized algorithm to detect faulty readings using Debraj de localization error detection algorithm. The proposed algorithm uses correlation of average readings of two nodes to detect nodes with faulty readings. This algorithm reduces computational complexity of the correlation algorithm and has high accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  7 - Content rating a nessissity for spam management in social networks
  Simin Ghesmati
  Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profi More

  Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profiles.The purpose of this research is to design a system to manage, diagnose and deal with spam attacks through social networks to reduce the attack effects as greatest extent as possible. In this study, the content rating system is to rate spam, phishing and over 18 contents in accordance with feedbacks from users so that the spam, phishing, or over 18 logos show and contents will not be displayed. Users are to click on the logo to reach the mentioned contents. To determine limit in comment senders is another ability of this social network system. The outcome of this study confirms that applying the content management system, as 95.22% percent of users claimed, was effective to avoid spam displaying and 99.61% percent of users were highly satisfied with the procedures in spam reduction in the implemented social network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  8 - Content rating for spam management in social networks a nessissity
  Simin Ghesmati
  Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profi More

  Nowadays using of social networks is growing very rapidly. Meanwhile, inappropriate and unintended contents like spam in these networks could be published. Forwarding inappropriate content by friends or spam senders caused that user receive these contents in their profiles.The purpose of this research is to design a system to manage, diagnose and deal with spam attacks through social networks to reduce the attack effects as greatest extent as possible. In this study, the content rating system is to rate spam, phishing and over 18 contents in accordance with feedbacks from users so that the spam, phishing, or over 18 logos show and contents will not be displayed. Users are to click on the logo to reach the mentioned contents. To determine limit in comment senders is another ability of this social network system. The outcome of this study confirms that applying the content management system, as 95.22% percent of users claimed, was effective to avoid spam displaying and 99.61% percent of users were highly satisfied with the procedures in spam reduction in the implemented social network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  9 - Wireless Sensor Network Based Vehicle Tracking System
  Ali Pourghaffari
  In this paper, a wireless sensor network based vehicle tracking system with its hardware and software components is introduced. In this system, the mica2 sensor node and MTS310 sensing board of Crossbow company products are used. In this system a GPS based localization More

  In this paper, a wireless sensor network based vehicle tracking system with its hardware and software components is introduced. In this system, the mica2 sensor node and MTS310 sensing board of Crossbow company products are used. In this system a GPS based localization is used and its purpose is to track passing vehicles. Performance of this system, as a pilot system in laboratory scale is approved in practical tests with different vehicles. Sensor nodes in this system, track passing around vehicles and report it to the base station. The tinyOS operating system is used for sensor nodes in this system and application program codes are written with NesC language, an open source language suitable for sensor network nodes the same as tinyOS. NesC language in syntax is much like C. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  10 - A System for Online Notification of Changes to Webblog Abstracts
  Mehdi Naghavi
  Exponential growth of information in the cyber space alongside rapid advancements in its related technologies has created a new mode of competition between societies to gain information domination in this critical and invaluable space. It has thus become quite critical More

  Exponential growth of information in the cyber space alongside rapid advancements in its related technologies has created a new mode of competition between societies to gain information domination in this critical and invaluable space. It has thus become quite critical to all stakeholders to play a leading and dominant role in generation of information and monitoring of voluminous information uploaded to this space. However, to achieve domination in monitoring large amount of information in cyberspace requires real-time monitoring using new techniques and approaches instead of traditional techniques. Concerned with the latter case, we limit our focus in this paper on Web blogs as an important part of the cyber space and propose a novel notification system for quick reporting of changes made to Web blogs. This is achieved by restricting the search for changes to high volumes of Web blogs only to changes to the abstracts of Web blogs derived from Web blogs. We show that this system works favorably compared to systems that require cooperation and synchronization between information providers. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  11 - Evaluation of SIP signaling implementation using QoS parameters
  mojtaba jahanbakhsh azharivs azharivs maryam homayooni Ahmad akbari
  Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 More

  Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 !mso]> st1:*{behavior:url(#ieooui) } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal" mso-tstyle-rowband-size:0 mso-tstyle-colband-size:0 mso-style-noshow:yes mso-style-priority:99 mso-style-qformat:yes mso-style-parent:"" mso-padding-alt:0cm 5.4pt 0cm 5.4pt mso-para-margin:0cm mso-para-margin-bottom:.0001pt mso-pagination:widow-orphan font-size:11.0pt font-family:"Calibri","sans-serif" mso-ascii-font-family:Calibri mso-ascii-theme-font:minor-latin mso-fareast-font-family:"Times New Roman" mso-fareast-theme-font:minor-fareast mso-hansi-font-family:Calibri mso-hansi-theme-font:minor-latin mso-bidi-font-family:Arial mso-bidi-theme-font:minor-bidi} Abstract The variety of services on IP networks and the need for network technology convergence have resulted in many access networks to adopt the IP technology. The Session Initiation Protocol (SIP) is an end to end application level protocol for establishing, terminating and modifying sessions and has experienced widespread use in IP networks due to its distinguished features such as being text based, independence from the underlying network, and more importantly supporting various types of mobility. In fact these features have lead SIP to be used as the core signaling protocol in the IP Multimedia Subsystem, which is the control plane proposed for next generation networks by the 3GPP community. Nevertheless, the performance of SIP servers when used by the millions of users of the next generation networks is not well established. In this paper we evaluate the performance of SIP servers using a test bed developed at the Iran University of Science & Technology. We consider eight different configurations for SIP server and also study the effect of using TCP and UDP as the transport protocol for SIP packets. We measure several parameters including call setup delay, call failure rate and SIP server throughput. Our results suggest that using SIP in large networks require using special techniques for balancing the load of SIP servers as well as mitigating temporary overloads.  Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  12 - Cross-layer Design for Congestion Control, Routing and Scheduling in Ad-hoc Wireless Networks with considering the Electrical Power of nodes
  Hooman Tahayori
  Abstract Ad hoc Wireless Networks, are networks formed by a collection of nodes through radio. In wireless networking environment, formidable challenges are presented. One important challenge is connection maintenance mechanism for power consumption. In this paper, a mu More

  Abstract Ad hoc Wireless Networks, are networks formed by a collection of nodes through radio. In wireless networking environment, formidable challenges are presented. One important challenge is connection maintenance mechanism for power consumption. In this paper, a multi-objective optimal design is considered for ad-hoc networks which address the electrical power of nodes effects on cross-layer congestion control, routing and scheduling. We first formulate the rate and scheduling constraints. In this way, the multi-commodity flow variables are used. Then, resource allocation in networks with fixed wireless channel and single-rate devices is formulated. Since the electrical power of nodes effects are included in the design problem, we formulate resource allocation as utility and cost function, together in a maximization problem with those constraints. By dual decomposition, the resource allocation problem vertically decomposes into three sub-problems: congestion control, routing and scheduling. These three sub-problems interact through congestion and link price. Simulation results are included to verify the effectiveness of the proposed approach. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  13 - A New Approach to Extract and Utilize Learners Social Relationships through Analyzing Forums Structure and Contents
  Somayeh Ahari
  Collaborative learning tools play important roles in communications and knowledge building, among learners in a virtual learning environment. They demand appropriate grouping algorithms as well as facilitating learners’ participations mechanisms. This paper has utilized More

  Collaborative learning tools play important roles in communications and knowledge building, among learners in a virtual learning environment. They demand appropriate grouping algorithms as well as facilitating learners’ participations mechanisms. This paper has utilized some information retrieval techniquesto investigate the relevance of discussion posts to their containing forums, and extract learners’ most frequent topics. Trying to explore students online interactions, researchers have applied social network analysis, which has led to a new representation of social networking. They have proposed a new grouping algorithm based on the provided representation of social relationships. The mentioned approaches have been evaluated in some academic courses in Department of Electrical and Computer Engineering, and ELearning Center, University of Tehran. The results have revealed some considerable improvements in comparison to the traditional approaches. Research outcomes may help tutors to create and guide groups of learners more effectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  14 - using clustering in AODV routing protocol for vehicular ad-hoc networks on highway scenario
  amin feyzi
  Vehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network More

  Vehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network are considered as a big challenge For routing in these networks, routing protocols must be robust and reliable. AODV Routing protocol is one of the known routing protocols in vehicular ad hoc networks. There are also some problems in applying this routing protocol on the vehicular ad hoc networks. The number of control massages increases with increasing the scale of the network and the number of nodes . One way to reduce the overhead in AODV routing protocol is clustering the nodes of the network. In this paper , the modified K-means algorithm has been used for clustering the nodes and particle swarm optimization has been used for selecting cluster head. The results of the proposed method improved normalized routing load and the increase of the packet delivery rate compared to AODV routing protocol. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  15 - An Approach to Agent- dependent Replicated in Fault-Tolerant Mobile Code Execution
  Hodjat Hamidi
  In this paper, we have presented the mobile code paradigm, which is a collection of remote evaluation, code on demand, and mobile agents, as an alternative to the conventional client/server paradigm. Security is a major problem of mobile agent systems, especially when m More

  In this paper, we have presented the mobile code paradigm, which is a collection of remote evaluation, code on demand, and mobile agents, as an alternative to the conventional client/server paradigm. Security is a major problem of mobile agent systems, especially when money transactions are concerned. Security for the partners involved is handled by encryption methods based on a public key authentication mechanism and by secret key encryption of the communication. In this paper, we examine qualitatively the security considerations and challenges in application development with the mobile code paradigm. We identify a simple but crucial security requirement for the general acceptance of the mobile code paradigm, and evaluate the current status of mobile code development in meeting this requirement. We find that the mobile agent approach is the most interesting and challenging branch of mobile code in the security context. Therefore, we built a simple agent based information retrieval application, the Traveling Information Agent system, and discuss the security issues of the system in particulars. This article discussed a mobile agent model for processing transactions, which manipulate object servers. An agent first moves to an object server and then manipulates objects. General possibilities for achieving fault tolerance in such cases were discussed and the respective advantages and disadvantages for mobile agent environments and the intended parallel and distributed application scenarios were shown. This leads to an approach based on warm standby and receiver side message logging. We have used dynamically changing agent domains to provide flexible, adaptive and robust operation. The performance measurement of Fault-Tolerant Mobile Agent System shows the overhead introduced by the replication mechanisms with respect to a non-replicated agent. Not surprisingly, it also shows that this overhead increases with the number of stages and the size of the agent. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  16 - Discovering spam in Facebook social network using data mining.
  amin nazari
  In recent years, by developing new technologies and communication facilities such as internet, new aspects named virtual social networks have been created. Rapid development of social networks and huge number of anonymous Users in these networks, created a suitable en More

  In recent years, by developing new technologies and communication facilities such as internet, new aspects named virtual social networks have been created. Rapid development of social networks and huge number of anonymous Users in these networks, created a suitable environment for scammers. Most of the times, scammers are trying to spread several types of spams into these high potential places. Hence, an effective method is required to detect the spams in order to increase the level of information security of people in the social networks. In this paper, a new method for discovering spammer in Facebook social network is proposed. Findings show 99.96% accuracy. In previous papers, users were divided into two groups of ordinary users and spammer users. The method of classification in these papers recognizes also as a spam the users which attached by spammer. So, in this paper by dividing users into three types of ordinary users, spammer and users attached by spammer, accuracy of spam detection has been increased. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  17 - Energy-efficient and Privacy preserving Data Aggreration in wireless sensor networks
  zahra zare
  Energy consumption is ranked among the major problems of research in wireless sensor networks(WSNs). The main reason for nodes failure is the discharge of batteries in sensor networks. Therefore, energy consumption plays an important role in wireless sensor networks. Da More

  Energy consumption is ranked among the major problems of research in wireless sensor networks(WSNs). The main reason for nodes failure is the discharge of batteries in sensor networks. Therefore, energy consumption plays an important role in wireless sensor networks. Data aggregation can greatly help to reduce this consumption by eliminating redundant data, and using clustering methods for data aggregation helps to further reduce energy consumption. Sensor nodes are prone to node compromise attacks which cause an adversary to change the aggregation result and inject false data into the WSN, hence security issues such as data confidentiality and integrity are extremely important. Because both data aggregation and security are critical for wireless sensor networks, achieving secure data aggregation that protects integrity is a critical issue. In this paper, we present a secure data aggregation method called Energy-Efficient and Privacy-Preserving Data Aggregation using our clustering algorithm which is an improvement on LEACH protocol and Homomorphic Encryption technique. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  18 - Automatic Sepration of Learnrs in Learning Groups Based on Identifying Learning Style from Their Behavior in Learning Environment
  mohammad sadegh rezaei gholamali montazer
  Automatic identification of learners groups based on similarity of learning style improves e-learning systems from the viewpoint of learning adaptation and collaboration among learners. In this paper, a new system is proposed for identifying groups of learners, who have More

  Automatic identification of learners groups based on similarity of learning style improves e-learning systems from the viewpoint of learning adaptation and collaboration among learners. In this paper, a new system is proposed for identifying groups of learners, who have similar learning style, by using learners’ behavior information in an e-learning environment. Proposed clustering method for separation of learners is developed based on ART neural network structure and Snap-Drift neural network learning process. This artificial network enables us to identify learners groups in uncertain group separation parameters, without knowing appropriate number of groups.  The results of an empirical evaluation of the proposed method, which are based on two criteria, “Davies-Bouldin” and “Purity and Gathering”, indicate that our proposed method outperforms other clustering methods in terms of accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  19 - Identifying Cloned Profiles on Online Social Networks by Identifying Nodes in Overlapping Communities and User Interactions
  Zahra Hamzehzadeh
  With the growing popularity of social networks, identifying Profile Cloning Attacks (PCAs) is an important challenge within the scope of privacy in the communications world. Until now, researchers have identified these attacks by using features such as profile informati More

  With the growing popularity of social networks, identifying Profile Cloning Attacks (PCAs) is an important challenge within the scope of privacy in the communications world. Until now, researchers have identified these attacks by using features such as profile information, link information, and interactions information that are based on methods like similarities and network structure. Previously suggested approaches lack specific routine and logic to track an attacker, and begin identifying PCAs with victim direct requests or according to the time of a friend request from an attacker. This research offers a new approach with a total of two major steps. Step one emphasizes that  legitimate users are attracted to interactions within their local communities; conversely, attackers are attracted to more dense areas. Step two was designed according to the analysis of the interactive behavior that is obtained from users 'earlier research. With this approach, according to a logic based on network structure, search cloned profiles can be identified.   Finally, a list of suspicious nodes to cloned nodes has been introduced with their scores that show the accuracy of selection. During the research, a logical relation between the average degrees of social network graph and the selection of the appropriate suspicious nodes with high priority was extracted. Finally, a general framework is proposed. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  20 - Multicast computer network routing using genetic algorithm and ant colony
  Mohammad Pourmahmood Aghababa
  Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from More

  Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from a sender to several receivers. To support such programs, there is a need to make an optimal multicast tree to indicate the optimal routes from the sending source to the corresponding sinks.  Providing an optimal tree for routing is a complicated problem. In this paper, we are looking forward a method for routing of multicast networks with considering some parameters such as the cost and delay. Also, this paper has emphasized the issue that every parameter in routing problem has different value for different packets. And in accordance to these parameters optimal routing multicast trees are proposed. To gain this end, the genetic algorithm and ant colony optimization approaches are adopted. The simulation results show that the presented algorithms are able to produce optimal multicast trees subject to the packets. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  21 - Determinants for entrepreneurial behavior among members of virtual agricultural social networks
  Pouria Ataei
  Entrepreneurship is an important issue that has been raised in all aspects of economic and social development. Now the most important question in the communities, especially in developing countries is how people can be entrepreneurs and how can they create entrepreneuri More

  Entrepreneurship is an important issue that has been raised in all aspects of economic and social development. Now the most important question in the communities, especially in developing countries is how people can be entrepreneurs and how can they create entrepreneurial opportunities. Nowadays because of technological development people can communicate with each other in many different ways, that virtual social network is one of them. Therefore, the aim of this study was to explain the determinants for entrepreneurial behavior of members of agricultural social networks. This study was among members of social networks "entrepreneurial knowledge exchange" The sample population was 126 people and population was 180 people. The assessment tool in this study was a questionnaire which its validity was confirmed by experts and its reliability was confirmed through pilot testing and estimated Cranach's alpha. Cranach's alpha was estimated between 0.76 - 0.93. Results showed that virtual social networking features, some entrepreneurial characteristics such as risk, identify and evaluate opportunities and incentives were entered in regression model and able to predict 76.6 percent of the variation in entrepreneurial behavior as a dependent variable. Finally, according to research findings, practical recommendations were presented. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  22 - Product purchasing prediction in an online store by designing an artificial neural network using clickstream data
  Mahbube Mottaghi
  One of the key capabilities of competitive online stores is the effective prediction of customer buying as much as possible to apply customer service strategies to convert users to buyers and to increase sales rates. Data mining and artificial intelligence techniques ar More

  One of the key capabilities of competitive online stores is the effective prediction of customer buying as much as possible to apply customer service strategies to convert users to buyers and to increase sales rates. Data mining and artificial intelligence techniques are successful in categorizing and forecasting. Work has been proven in timely systems, such as e-commerce sites. In this paper, we propose a non-post-error neural network model with the aim of predicting purchases at user active stages in an online store. The training and evaluation of the neural network was performed using a set of revised sessions from server logs. The accuracy and retrieval power of the proposed neural network is 8999.79% and 89.696%, which indicates the high ability of this network (about 90%) in predicting the purchase Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  23 - The impact of social networking ads on the desire to buy customers Through the moderating role of trust and image of perceived website (case study: aval market)
  maryam akhavan
  The present research studies the effect of social networking advertising on the desire to buy customers through the moderate role of trust and image of the perceived website in the first company. The research method is descriptive and survey type. The statistical popula More

  The present research studies the effect of social networking advertising on the desire to buy customers through the moderate role of trust and image of the perceived website in the first company. The research method is descriptive and survey type. The statistical population of this research includes all customers of the first market in Tehran. The sample size was determined using the unlimited population formula of 384 people. The method of sampling is cluster sampling. The research data were collected using library and field method and the tools used in the questionnaire. The reliability of the questionnaires was confirmed by Cronbach's alpha (0.884) and the validity of the tool was confirmed by content and structure. The research data were analyzed using SPSS and Laser software and analyzed by statistical, descriptive and inferential statistics. The results of this research indicate that social networking ads, the value of social networking ads, Social networking stimulants have a positive impact on the desire to buy customers. However, the impact and customization of social networks has been rejected by the desire to buy customers. Also, the results of moderating assumptions show that the trust and image of the perceived web site significantly affects the impact of social networking ads on the willingness to buy customers. Moreover, the results show that the interaction of social networking ads and gender after adding to the meaning model Not a dude     Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  24 - An Analysis of Customer Behavior in online shopping of electronic products
  Razieh Rouhi DEHKORDI
  Technology advance has had a great influence on commerce and business, as tools advance in present era has made a new type of human relations that help them perform macro and micro transactions and businesses without physical presence, and made businesses go toward elec More

  Technology advance has had a great influence on commerce and business, as tools advance in present era has made a new type of human relations that help them perform macro and micro transactions and businesses without physical presence, and made businesses go toward electronization. Current study involves identification and prioritization of factors affecting customer’s behavior of online purchase of electronic products. A method combined of attributive and survey methods was used.  Indeed, data analysis was performed based on survey method. All of Students of Islamic Azad University South Tehran Branch were used as respondents.  Based on Cochran formula, sample volume was 347 people. Sampling method was randomly. Questionnaire was prepared using experts comments and Delphi technique and was distributed among students. Spss software and AHP method were used in Statistical analysis. Results showed that 14 major factor affect customer’s behavior of online purchase of electronic products that Security, trust and price factors ranked as three first rank using AHP method which is confirmed by results of other researches. Finally, solutions were presented for any indicator.  Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  25 - The Psychological Empowerment; An Instrument for Controlling the Employees' Cyberloafing Behavior in the Healthcare and Treatment Sector
  Hossein Samadi-Miarkolaei
  Nowadays, the performance of healthcare and treatment sector, due to its nature and goal on the public health realm, is increasingly important and requires the excellent performance of its employees. Indeed, the strategic role of human resource, beside material and phys More

  Nowadays, the performance of healthcare and treatment sector, due to its nature and goal on the public health realm, is increasingly important and requires the excellent performance of its employees. Indeed, the strategic role of human resource, beside material and physical sources, always is taken into account as the most important competitive advantage toward development of organizational productivity in healthcare and treatment sector, and this sector is doomed to having psychological empowered and healthy human resources. To do so, paying attention to the psychological empowerment and cyberloafing variables is required. Thus, the main purpose of the present research is exploring the relationships between psychological empowerment and cyberloafing in the healthcare and treatment sector. Present research, in terms of purpose, is an applied study, and in terms of data gathering way, is an analytical-correlation study. The statistical population of this study includes all the employees of Babolsar Healthcare and Treatment Network in 1397 [2018]. A sample includes 165 persons selected by simple sampling method. In order to explore the psychological empowerment (PE) and cyberloafing (CL), the standard scales are used. Data analysis through Pearson's correlation coefficient test and the step by step multiple regression has been done by SPSS software. Research results showed that there is a negative and significant relation between psychological empowerment and cyberloafing (r= -0/243; P<0/001). Also, the regression analysis test showed that the psychological empowerment explains the 24 percent of changes in cyberloafing variable. On the other, among psychological empowerment dimensions, only the meaning variable is able to negatively and significantly predict and influence on the employees' cyberloafing behavior. On the basis of this results, it could be perceived that the cyberloafing is impressed by psychological empowerment and this subject is very important in organizations. Thus, it is suggested to the organization's managers to use the techniques of increasing psychological empowerment in employees to utilize their capacities toward positive side (i.e. reducing the cyberloafing level).     Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  26 - Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
  Payman Moallem mehrdad sadeghi hariri MAHDI hashemi
  Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthog More

  Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  27 - A Hybrid Neural Network Ensemble Model for Credit Risk Assessment
  shaban elahi Ahmad ghodselahi hamidreza naji
  Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble l More

  Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble learning for credit granting decisions is designed. Combining clustering and classification techniques resulted in system improvement. The German bank real dataset was used for neural network training. The proposed model implemented as credit risk evaluation multi agent system and the results showed the proposed model has higher accuracy, better performance and lesser cost in applicant classification when compared with other credit risk evaluation methods Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  28 - Iran paradigm in the Vision 2050 global electricity industry
  ali mighi mohsen masoumzade
  Today's technology has been recognized as an important lever for economic growth in the global arena. Successful industries in the world have now realized the importance of this which Effective application of science and technology to increase efficiency and competitive More

  Today's technology has been recognized as an important lever for economic growth in the global arena. Successful industries in the world have now realized the importance of this which Effective application of science and technology to increase efficiency and competitiveness in global markets will. It will no doubt come from countries that develop technologies based on their specific strategies and policies identified And so with the necessary mechanisms to keep pace with future developments are ready. Facing the technological challenges facing the electric power industry in the world and what is it? The share of new and renewable energy future Basket power source What is the world? What is the future direction of Automation and Intelligent power grid? The processes of electric vehicles, increasing urbanization, emissions, etc., What impact will the technology industry in the coming years? This paper studies the power industry developed country like South Korea and prospects of development of the technology industry by 2050 That is the appropriate response to achieve. Data and evidence presented in this report suggest that the technological development of the electricity industry to align with the future of the power industry in the world, should be the subject of such an intelligent network,Reduce greenhouse gas emissions through development of renewable energy and improve the efficiency of existing plants, the optimal combination of product portfolio, Consumption and improve the efficiency of transportation management and development will focus on electricity Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  29 - Designing a conceptual model of reverse logistics management network based on supply chain innovation approach
  mohammadreza khosravi reza homaee mansoureh hourali
  In recent years, rapid market changes have increased due to innovation in material supply chains and the adoption of a sustainable approach to consumption patterns has become more important. This issue has been especially pronounced in the field of supply and consequent More

  In recent years, rapid market changes have increased due to innovation in material supply chains and the adoption of a sustainable approach to consumption patterns has become more important. This issue has been especially pronounced in the field of supply and consequently the consumption of goods in the supply chain. The purpose of this paper is to design a conceptual model of reverse logistics network in order to better use raw materials and reduce waste in the supply chain. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  30 - A greedy new method based on the cascade model to calculate maximizing penetration in social networks
  Asgarali Bouyer Hamid Ahmadi
  In the case of penetration maximization, the goal is to find the minimum number of nodes that have the most propagation and penetration in the network. Studies on maximizing penetration and dissemination are becoming more widespread. In recent years, many algorithms hav More

  In the case of penetration maximization, the goal is to find the minimum number of nodes that have the most propagation and penetration in the network. Studies on maximizing penetration and dissemination are becoming more widespread. In recent years, many algorithms have been proposed to maximize the penetration of social networks. These studies include viral marketing, spreading rumors, innovating and spreading epidemics, and so on. Each of the previous studies has shortcomings in finding suitable nodes or high time complexity. In this article, we present a new method called ICIM-GREEDY to solve the problem of maximizing penetration. In the ICIM-GREEDY algorithm, we consider two important criteria that have not been considered in the previous work, one is penetration power and the other is penetration sensitivity. These two criteria are always present in human social life. The proposed method is evaluated on standard datasets. The obtained results show that this method has a better quality in finding penetrating nodes in 30 seed nodes than other compared algorithms. This method also performs better in terms of time compared to the comparative algorithms in terms of relatively fast convergence. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  31 - Routing of Multipartite Computer Networks Using Ant Genetic Algorithm
  Mohammad Pourmahmood Aghababa amin bahadorani baghbaderani
  With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple des More

  With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple destinations over a communication network. To support such programs, you need to create an optimal multipart tree , Which indicates the optimal routes to reach from one sender source to several desired destinations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  32 - Application of clustering in AODV routing protocol for intercity networks on the highway scenario
  amin feyzi Vahid Sattari-Naeini majid mohammadi
  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network More

  Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network topology are a major challenge for routing, for routing in these networks, routing protocols must be robust and reliable. One of the well-known routing protocols in intercity networks is the AODV routing protocol. The application of this routing protocol on intercity networks also has problems that increase the number of control messages in the network by increasing the scale of the network and the number of nodes. One way to reduce overhead in the AODV protocol is to cluster network nodes. In this paper, the modified K-Means algorithm is used to cluster the nodes and the particle swarm algorithm is used to select the cluster head. The results of the proposed method improve the normal routing load and increase the packet delivery rate compared to the AODV routing protocol. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  33 - Routing improvement to control congestion in software defined networks by using distributed controllers
  saied bakhtiyari Ardeshir Azarnejad
  Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way th More

  Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way that the delay between controllers and switches in wide area networks can be reduced. In this regard, most of the proposed methods have focused on reducing latency. But latency is just one factor in network efficiency and overall cost reduction between controllers and related switches. This article examines more factors to reduce the cost between controllers and switches, such as communication link traffic. In this regard, a cluster-based algorithm is provided for network segmentation. Using this algorithm, it can be ensured that each part of the network can reduce the maximum cost (including delays and traffic on links) between the controller and its related switches. In this paper, using Topology Zoo, extensive simulations have been performed under real network topologies. The results of the simulations show that when the probability of congestion in the network increases, the proposed algorithm has been able to control the congestion in the network by identifying the bottleneck links in the communication paths of each node with other nodes. Therefore, considering the two criteria of delay and the degree of busyness of the links, the process of placing and distributing the controllers in the clustering operation has been done with higher accuracy. By doing so, the maximum end-to-end cost between each controller and its related switches, in the topologies Chinanet of China, Uunet of the United States, DFN of Germany, and Rediris of Spain, is decreased 41.2694%, 29.2853%, 21.3805% and 46.2829% respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  34 - Investigating the effect of using various marketing strategies on social networks on gaining the trust of council customers Investigating ترجمه‌های investigate فعلفراوانی بررسی کردن investigate, check, peruse, survey, study رسیدگی کردن consider, attend, check, investigate, inspect, investigate استفسار کردن investigate وارسی کردن sift, investigate پژوهیدن investigate, inquire, research, search تحقیق کردن investigate, inquire, verify, assay, interrogate, question اطلاعات مقدماتی بدست اوردن investigate جستار کردن investigate تفتیش کردن inquire, inspect, investigate, revise باز جویی کردن examine, assay, inquire, interrogate, investigate, cross-examine تعریف‌های investigate فعل ۱ carry out a systematic or formal inquiry to discover and examine the facts of (an incident, allegation, etc.) so as to establish the truth. police are investigating the alleged beating مترادف‌ها: check outsuss outgive something the once-overscope outinquire intolook intogo intolook overprobeexplorescrutinizeconduct an investigation intoconduct an inquiry intomake inquiries abouttry to get to the bottom ofinspectanalyzestudyexamineconsiderresearchsearch/sift the evidence concerningpore overdelve intoauditevaluatefollow up مترادف investigate فعل check outsuss outgive something the once-overscope out inquire intolook intogo intolook overprobeexplorescrutinizeconduct an investigation intoconduct an inquiry intomake inquiries abouttry to get to the bottom ofinspectanalyzestudyexamineconsiderresearchsearch/sift the evidence concerningpore overdelve intoauditevaluatefollow up همچنین ببینید investigate
  farzaneh milani jafari zenouzi
  The aim of this study is to investigate the effect of using variety of marketing strategies in social networks to build customers’ trust. Marketing through social networks has made appropriate opportunities for companies to attract more customers. Building customers' tr More

  The aim of this study is to investigate the effect of using variety of marketing strategies in social networks to build customers’ trust. Marketing through social networks has made appropriate opportunities for companies to attract more customers. Building customers' trust and attracting the customers can be mentioned as marketing challenges on these networks. So, in order to create competitive advantages, companies need to use appropriate strategies of building trust. The population of this study consists of all Iranian users of social networking sites that affected by companies advertisements. Also the sample size by using snowball sampling method is 446. The research method is descriptive survey research and data collection tool is questionnaire. To test hypotheses the partial least squares (PLS) technique and SmartPLS 3 software has been used. The results show that all four variables include: transactional, relationship, database and knowledge-based marketing strategies in social networks have a significant impact to build customers’ trust. Indeed, transactional strategy has negative impact on trust so the relation between this variable and dependent variable is reverse. knowledge-based marketing strategy has the most positive impact on customers’ trust. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  35 - Comparison of support vector machine and artificial neural network classification methods to produce landuse maps (Case study: Bojagh National Park)
  Mahsa Abdoli Laktasaraei Maryam  Haghighi khomami
  10.29252/.5.8.47
  National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classi More

  National parks and wildlife shelter are the most important natural heritages; therefore, knowing of quantitative and qualitative changes in their land use plays an essential role in the quality of these areas' management. various algorithms have been developed to classify satellite imagery in remote sensing, selecting an appropriate classification algorithm is very important in achieving the accurate results. In this research, a more accurate algorithm was determined by comparing the classification accuracy of two artificial neural network and support vector machine algorithms, and it was used to examine the process of the land use changes. The present study was performed in Boujagh National Park, in the Guilan Province, during the years 2000 to 2017, using satellite imagery ETM and OLI of Landsat 7 and 8. The results of the research revealed that the support vector machine algorithm with overall accuracy and Kappa coefficient of 86.42 and 0.83 respectively for the year 2000 and, 90.65 and 0.88 for the year 2017, classified the satellite images more precisely, in comparison with the artificial neural network algorithm with overall accuracy and Kappa coefficient of 83.71 and 0.80 respectively for the year 2000 and overall accuracy and Kappa coefficient of 89.25 and 0.87 for the year 2017. Therefore, the land use maps of the support vector machine algorithm were used to determine the land use changes. The study of land use change by this method concluded that the areas of the waterbody, sea, grassland and agriculture have decreased and marshland, woody and bare lands classes showed an increase during the study period. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  36 - Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices
  Majid Abdolrazzagh-Nezhad mahdi kherad
  Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and compl More

  Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and complex. In this paper, two models are designed and implemented to identify the complex relationship between 10 economic factors on the stock prices of companies operating in the Tehran stock market. First, a Mamdani Fuzzy Inference System (MFIS) is designed that the fuzzy rules set of its inference engine is found by the Particle Swarm Optimization Algorithm (PSO). Then a Deep Learning model consisting of 26 neurons is designed wiht 5 hidden layers. The designed models are implemented to predict the stock prices of nine companies operating on the Tehran Stock Exchange. The experimental results show that the designed deep learning model can obtain better results than the hybridization of MFIS-PSO, the neural network and SVM, although the interperative ability of the obtained patterns, more consistent behavior with much less variance, as well as higher convergence speed than other models can be mentioned as significant competitive advantages of the MFIS-PSO model Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  37 - Introducing a genetic algorithm based Method for Community person's stance Detection in social media and news
  mehdi salkhordeh haghighi Seyyed Mohammad  ebrahimi
  News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the r More

  News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the readers to obtain a basic knowledge about the subject and the purpose of various documents. In the present paper, we introduce a method for detecting communities that includes the persons with the same stances and ideas. To do this, the persons referenced in different documents are clustered into communities that have related positions and stances. In the presented method. Community-based personalities are identified based on a friendship network as a base method. Then by using a genetic algorithm, the way that these communities are identified is improved. The criterion in the tests is rand index of detection of these communities. The experiments are designed based on real databases that published in Google News on a particular topic. The results indicate the efficiency and desirability of the proposed method Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  38 - An Improved Method Based on Label Propagation and Greedy Approaches for Community Detection in Dynamic Social Networks
  Mohammad ستاری kamran zamanifar
  Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is More

  Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is simple and fast approach. This approach consists of many methods such as LabelRankT is one with high speed and less complexity. Similar to most methods for detecting communities in dynamic social networks, this one is not trouble free. That is, it is not considered the internal connection of communities, when it expands communities of the previous snapshots in the current snapshot. This drawback decreases the accuracy of community detection in dynamic social networks. For solving the drawback, a greedy approach based on local modularity optimization is added to LabelRankT method. Here, the newly proposed GreedyLabelRankT, LabelRankT and non-overlapping version of Dominant Label Propagation Algorithm Evolutionary (DLPAE-Non Overlapping) on real and synthetic datasets are implemented. Experimental results on both real and synthetic network show that the proposed method detect communities more accurately compared to the benchmark methods. Moreover, the finding here show that running time of the proposed method is close to LabelRankT. Therefore, the proposed method increase the accuracy of community detection in dynamic social networks with no noticeable change in the running time of that. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  39 - Increasing the value of collected data and reducing energy consumption by using network coding and mobile sinks in wireless sensor networks
  ehsan kharati
  The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile More

  The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile sinks, which increases the life of wireless sensor networks. Using network coding, this paper presents a Mixed Integer Linear Programming Model to determine the optimal multicast routing of source sensor nodes to mobile sinks in wireless sensor networks, which determines the time and location of sinks to collect maximum coded data and reduces the delay in sink movement and energy consumption. Solving this problem in polynomial time is not possible due to the involvement of various parameters and the constrained resources of wireless sensor networks. Therefore, several exploratory and greedy and fully distributed algorithms are proposed to determine the movement of sinks and their residence location based on maximizing the value of coded data and the type of data dead time. By simulating, the optimal method and the use of coding and proposed algorithms, reduce the runtime and energy consumption and increase the value of collected data and network lifetime than non-coding methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  40 - The Role of Dairy Workshops in Formation of Spatial Flows The Case of Settlements of Hashtrud and Charoymagh Areas
  ِAbaas Saidi Mostafa Taleshi omid rafatkhah Amin Torkaman
  The spatial flows are based on rural-urban interactions and are defined through different flows of people, goods, products, capital, information and innovation. The formation of these flows requires structural-functional contexts. What is of particular importance in thi More

  The spatial flows are based on rural-urban interactions and are defined through different flows of people, goods, products, capital, information and innovation. The formation of these flows requires structural-functional contexts. What is of particular importance in this regard is to emphasize rural-urban socioeconomic integrity and to avoid approaches based on the separation of rural and urban settlements. In fact, what prevents the formation of these networks in developing countries is due to various factors and forces. However, state building and development facilitation, along with the serious involvement of villagers, can play an effective role. This article examines the function of dairy workshops in the areas of Hashtrud and Charoymagh (East Azarbaijan) to identify the role of these workshops in the shaping rural-urban relationships and linkages and formation of local and regional networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  41 - The Geo-discourse of Takfiri IS group and its Media Representation- with emphasis on digital media
         
  This essay would explore the media dimension of operations of takfiri-terrorist IS group. To do so we will study the discourse making process of IS throughout virtual space and Virtual Networks. This article argues that establishing a discoursive system and articulating More

  This essay would explore the media dimension of operations of takfiri-terrorist IS group. To do so we will study the discourse making process of IS throughout virtual space and Virtual Networks. This article argues that establishing a discoursive system and articulating ideational concepts that construct the positions of Self and Other and give them a hegemonic status is possible via virtual networks and with enjoying of media ploys. In this regard the main question of this essay is about the evaluation of the level of efficacy of these media arenas for IS and assesing the opportunities or by contrast the threats offered by them for IS’s activism. Our hypothesis is that virtual space is useful for IS and this group thanks to a professional approach to social networks and knowing the function of media, can establish a media terrorism by psychological operation and therefore complete its geopolitics actions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  42 - Comparison of the MLP and RBF Neural Networks for the Determination of Confined Aquifer Parameters
  Tahereh Azari Nozar Samani
  In this paper, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) are designed for the determination of confined aquifer parameters: transmissibility and storage coefficient. The networks are trained for the well function of c More

  In this paper, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) are designed for the determination of confined aquifer parameters: transmissibility and storage coefficient. The networks are trained for the well function of confined aquifers. By applying the principal component analysis (PCA) on the training data sets the topology of the MLP and RBF networks is reduced and fixed to [1×12×1] and [1×14×1], respectively regardless of number of records in the pumping test data. The networks generate the optimal match point coordinates for any individual real pumping test data set. The match point coordinates are then incorporated with Theis analytical solution (1935) and the aquifer parameter values are determined. The generalization ability and performance of the developed networks is evaluated with 100000 sets of synthetic data and their accuracy is compared with that of type curve matching technique by two sets of real pumping test data. The results showed that though both MLP and RBF networks are able to determine the confined aquifers parameters and eliminate graphical error inherent in the type curve matching technique but the MLP network is more accurate than the RBF network. Therefore, the proposed MLP network is recommended as an accurate automatic and fast procedure for the confined aquifer parameters estimation. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  43 - بررسی کارایی مدل هیبریدی هالت-وینترز موجکی (WHW)در شبیه¬سازی تراز سطح ایستابی آبخوان ساحلی ارومیه
  Mohammad Nakhaei Farshad Alijani Ali Mirarabi HamidReza Nasseri
  For management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for More

  For management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for groundwater forecasting. A monthly data set of 16 years consisting of groundwater level fluctuations was used in two observation wells of Urmieh coastal aquifer. In the WHW, the dataset was converted into several sub-dataset with different time scales. Then, the sub-series were used in the HW model as inputs. Subsequently, the performance of the WHW model was compared with ARIMA, HW, and SARIMA as linear models and neural network models (ANN) and Support Vector Regression (SVR) as nonlinear models. The results showed that the NSE and RMSE values of the WHW model were upgraded up to 30% and 60% respectively, in comparison with linear models. The WHW hybrid model also has the same performance compared to nonlinear models. This research reflects that if there are multiple seasonal fluctuations in the groundwater time series, the performance of the WHW model compared with linear models will be more accurate. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  44 - The Effect of Social Capital on Access to Political Position
  Qumars Ashtarian Tahereh Mehrvarzian
  In different societies, there have always been serious conflicts in getting power as the most important and influential element in social-political life, and individuals compete in a variety of ways to get power. The social capital of actors can be very effective in det More

  In different societies, there have always been serious conflicts in getting power as the most important and influential element in social-political life, and individuals compete in a variety of ways to get power. The social capital of actors can be very effective in determining the ultimate result of the competition, and the more the volunteers and actors use network communications in competition and, as a result, have the support of collective networks, the more they would be supported by the groups and networks that have influence and power, so they can likely achieve the more political position. The main issue of the article is how the social capital of the network affects the achievement of political status and the method of analysis is descriptive and analytical here. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  45 - Converting protein sequence to image for classification with convolutional neural network
  reza ahsan mansour ebrahimi dianat dianat
  Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study di More

  Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  46 - An access control model for online social networks using user-to-user relationships
  Mohamad Javad Piran mahmud deypir
  With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the manag More

  With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the management and privatization of access to their shared information by other users. In this article, using the concept of social graph, a new model of user access control was proposed to the user, which allows the expression of privacy policies and more accurate and professional access control in terms of pattern and depth of relationships between users in social networks. In this article, by using the regular index method, indirect relationships among users are examined and analyzed, and more precise policies than previous models are presented. The evaluation of the results showed that for 10 neighbors for each user, the probability accumulation of a qualified path for the first three counter loops is 1, 10.5 and 67.3%, respectively, and finally for the fourth counter it reaches 100%. As the defined counting characteristic increases, the average execution time of the proposed algorithm and previously proposed algorithms increases. However, for the higher limits of the counting characteristic, the proposed algorithm performs better than the previous ones. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  47 - The role of organizational factors on consumer buying behavior in social networks
  zohreh dehdashti shahrokh Mitra Daneshparvar vahid nasehifar vahid Khashei
  The clothing industry has seen great growth in social media in recent years. Many factors affect the purchase of clothing from social networks, and one of these important and influential factors is organizational factors. Therefore, the purpose of this study is to inves More

  The clothing industry has seen great growth in social media in recent years. Many factors affect the purchase of clothing from social networks, and one of these important and influential factors is organizational factors. Therefore, the purpose of this study is to investigate the organizational factors affecting the consumer in shopping through social networks. In this research, a combined method has been used. In the qualitative section, by reviewing the literature and interviewing vendors active in social networks, texts were prepared and coded. In the quantitative part, based on the initial model, a questionnaire was developed and distributed to 385 clothing buyers in networks. The structural equation method was used to analyze the data and the Sobel test was used to examine the mediating role. This study examines the independent and combined effect of organizational variables affecting people's trust in buying clothes through social networks. The results showed that information quality, transaction security, company reputation and company location have a significant effect on people's trust and consumer willingness to buy clothing through social networks.. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  48 - A Novel Method based on the Cocomo model to increase the accuracy of software projects effort estimates
  mahdieh salari vahid khatibi amid khatibi
  It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead t More

  It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead the project to failure. It is therefore a major task in efficient development of software projects to estimate software costs accurately. Therefore, two methods were presented in this research for effort estimation in software projects, where attempts were made to provide a way to increase accuracy through analysis of stimuli and application of metaheuristic algorithms in combination with neural networks. The first method examined the effect of the cuckoo search algorithm in optimization of the estimation coefficients in the COCOMO model, and the second method was presented as a combination of neural networks and the cuckoo search optimization algorithm to increase the accuracy of effort estimation in software development. The results obtained on two real-world datasets demonstrated the proper efficiency of the proposed methods as compared to that of similar methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  49 - Improving imperialist competitive algorithm for solving the nodes placement problem in three-dimensional grid wireless sensor networks
  Sayed Wafa Barkhoda Hemmat Sheikhi sudabeh mohammadi
  One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imp More

  One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imperialist competitive algorithm is used for solving the mentioned problem. In the proposed method, a colony can immigrate from a weak empire to more powerful empire. The idea of immigration is inspired from human society in which a human can emigrate from a country to another country. The network is supposed to be a three-dimensional grid network and the sensor nodes can be only placed at cross-points of the grids while the target points can be deployed at each point of three-dimensional space. The simulation results show that the proposed method uses fewer number of sensor nodes than other similar algorithms and has the less running time. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  50 - Increasing the lifetime of underwater acoustic sensor networks by optimal relay node placement
  zahra mihamadi mohadeseh soleimanpour daryush avasimoghaddam Siamak Talebi
  Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasi More

  Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasing the lifetime of UASNs is an important issue. Although relay nodes have an important role in reducing the transmission distance and energy consumption. But the efficient RNP (Relay Node Placement) to avoid the critical sensor nodes' elimination is the main problem, i.e., to preserve the connected network. For this aim this paper presents an innovative solution called an Efficient Relay node Setting (ERS) algorithm, which involves formulating the Relay Node Placement (RNP) as a non-convex optimization problem. Actually, due to the Difference Convex (DC) constraints the proposed RNP problem is a non-convex problem and finding an optimal solution is complicated. However, a novel transformation can be applied to DC constraints which converts the problem into its convex programming equivalent. Application of the convex programming offers the advantage of readily computing a global optimal solution. Simulation results confirm the superiority of the proposed scheme over the competing RA method in terms of network lifetime and efficiency. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  51 - An Intelligent Algorithm for the Process Section of Radar Surveillance Systems
  Habib Rasi
  10.7508/jist.2016.01.002
  In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intel More

  In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intelligence system cannot recognize the different devices of the same type or class. A very important role is played by Measurement and Signature Intelligence. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. The information such as Pulse Repetition Interval (PRI), Angle of Arrival (AoA), Pulse Width (PW), Radio Frequencies (RF), and Doppler shifts are not usable. In the proposed algorithm, for clustering of overlapping pulses received from self-organization neural network SOFM (due to its high accuracy in comparison with other neural networks, such as CLNN and neural networks (Fuzzy ART), and for detecting intra-pulse modulation type, matrix method, and for identifying the radar type, RBF neural network have been used. The simulation results of the proposed algorithm shows that in the presence 5% noise and 5% missing pulse, the accuracy of the clustering part of the proposed algorithm is equivalent to 91/8%, intra-pulse modulation recognition accuracy is 98%, the detection accuracy is 2/99%, and the total output of the algorithm precision is 89/244%, respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  52 - A Unicast Tree-Based Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks
  Zeynab Mottaginia Ali Ghaffari
  10.7508/jist.2016.01.008
  The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing proto More

  The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing protocols cannot be applied usefully in such challenging network conditions because of intermittent connections and/or long delays. Hence, this research is intended to propose a Unicast Tree-based Data Gathering protocol (UTDG) to resolve this problem. A UTDG includes 3 phases: tree formation phase, data collection and data transmission phase, and finally the updating phase. The proposed protocol constructs a tree in each community on the basis of transmission ranking, contact probability and the link expiration time. The selection of the next-hop node is based on the tree structure rather than forwarding the message to the neighbor node directly. Each node unicasts the data to its parent in the related community, and the root of the tree successively sends the data to the sink node. The authors contend, based on the simulation results of the study, that the proposed protocol can gain significantly higher message delivery rates with lower transmission overhead and also lower delay in data delivery than the other existing DTMSNs routing protocols in some applications. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  53 - High I/Q Imbalance Receiver Compensation and Decision Directed Frequency Selective Channel Estimation in an OFDM Receiver Employing Neural Network
  afalahati afalahati Sajjad Nasirpour
  10.7508/jist.2014.03.002
  The disparity introduced between In-phase and Quadrature components in a digital communication system receiver known as I/Q imbalance is a prime objective within the employment of direct conversion architectures. It reduces the performance of channel estimation and caus More

  The disparity introduced between In-phase and Quadrature components in a digital communication system receiver known as I/Q imbalance is a prime objective within the employment of direct conversion architectures. It reduces the performance of channel estimation and causes to receive the data symbol with errors. This imbalance phenomenon, at its lowest still can result very serious signal distortions at the reception of an OFDM multi-carrier system. In this manuscript, an algorithm based on neural network scenario, is proposed that deploys both Long Training Symbols (LTS) as well as data symbols, to jointly estimate the channel and to compensate parameters that are damaged by I/Q imbalanced receiver. In this algorithm, we have a tradeoff between these parameters. I.e. when the minimum CG mean value is required, the minimum CG mean value could be chosen without others noticing it, but in usual case we have to take into account other parameters too, the limited values for the aimed parameters must be known. It uses the first iterations to train the system to reach the suitable value of GC without error floor. In this present article, it is assumed that the correlation between subcarriers is low and a few numbers of training and data symbols are used. The simulation results show that the proposed algorithm can compensate the high I/Q imbalance values and estimate channel frequency response more accurately compared with to date existing methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  54 - A New Cooperative Approach for Cognitive Radio Networks with Correlated Wireless Channels
  Mehdi  Ghamari Adian Hassan Aghaeenia
  10.7508/jist.2013.03.003
  An effective cooperative cognitive radio system is proposed, when the wireless channels are highly correlated. The system model consists of two multi-antenna secondary users (SU TX and SU RX), constituting the desired link and some single-antenna primary and secondary u More

  An effective cooperative cognitive radio system is proposed, when the wireless channels are highly correlated. The system model consists of two multi-antenna secondary users (SU TX and SU RX), constituting the desired link and some single-antenna primary and secondary users. The objective is the maximization of the data rates of the desired SU link subject to the interference constraints on the primary users. An effective system, exploiting Transmit Beamforming (TB) at SU TX, cooperation of some single-antenna SUs and Cooperative Beamforming (CB) at them and the antenna selection at SU RX to reduce the costs associated with RF-chains at the radio front end at SU RX, is proposed. Due to the issue of MIMO channels with correlated fading, some problems arise such as inapplicability of the well-known Grassmanian Beamforming as TB scheme at SU TX. We then propose a method to overcome this problem. After formulating the problem, a novel iterative scheme is proposed to find the best TB weight vector in SU TX and best subset of antennas at SU RX, considering the correlated channel. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  55 - Multiple Antenna Relay Beamforming for Wireless Peer to Peer Communications
  Mohammad Hossein Golbon Haghighi Behrad Mahboobi Mehrdad  Ardebilipour
  10.7508/jist.2013.04.001
  This paper deals with optimal beamforming in wireless multiple-input-multiple-output (MIMO) relay networks that involves multiple concurrent source-destination pairs with imperfect channel state information (CSI) at the relays. Our aim is the optimization of the MIMO re More

  This paper deals with optimal beamforming in wireless multiple-input-multiple-output (MIMO) relay networks that involves multiple concurrent source-destination pairs with imperfect channel state information (CSI) at the relays. Our aim is the optimization of the MIMO relay weights that minimize the total relay transmit power subject to signal-to-interference-plus-noise ratio (SINR) of all destinations to be kept above a certain threshold. Since power minimization is a non-convex quadratically constrained quadratic programming (QCQP), we use semi-definite programming (SDP) relaxation of above mentioned problem by using a randomization technique. Numerical Monte Carlo simulations verify the performance gain of our proposed multiple antenna relay system in terms of transmit power and symbol error probability. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  56 - Load Balanced Spanning Tree in Metro Ethernet Networks
  Ghasem Mirjalily Samira Samadi
  10.7508/jist.2014.02.007
  Spanning Tree Protocol (STP) is a link management standard that provides loop free paths in Ethernet networks. Deploying STP in metro area networks is inadequate because it does not meet the requirements of these networks. STP blocks redundant links, causing the risk of More

  Spanning Tree Protocol (STP) is a link management standard that provides loop free paths in Ethernet networks. Deploying STP in metro area networks is inadequate because it does not meet the requirements of these networks. STP blocks redundant links, causing the risk of congestion close to the root. As a result, STP provides poor support for load balancing in metro Ethernet networks. A solution for this problem is using multi-criteria spanning tree by considering criterions related to load balancing over links and switches. In our previous work, an algorithm named Best Spanning Tree (BST) is proposed to find the best spanning tree in a metro Ethernet network. BST is based on the computation of total cost for each possible spanning tree; therefore, it is very time consuming especially when the network is large. In this paper, two heuristic algorithms named Load Balanced Spanning Tree (LBST) and Modified LBST (MLBST) will be proposed to find the near-optimal balanced spanning tree in metro Ethernet networks. The computational complexity of the proposed algorithms is much less than BST algorithm. Furthermore, simulation results show that the spanning tree obtained by proposed algorithms is the same or similar to the spanning tree obtained by BST algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  57 - Node to Node Watermarking in Wireless Sensor Networks for Authentication of Self Nodes
  Hassan Farsi Seyed Morteza Nourian
  10.7508/jist.2014.02.008
  In order to solve some security issues in Wireless Sensor Networks (WSNs), node to node authentication method based on digital watermarking technique for verification of relative nodes is proposed. In the proposed method, some algorithms with low computational for gener More

  In order to solve some security issues in Wireless Sensor Networks (WSNs), node to node authentication method based on digital watermarking technique for verification of relative nodes is proposed. In the proposed method, some algorithms with low computational for generation, embedding and detection of security ID are designed. The collected data packets by the nodes are marked using security ID. In the proposed method, header is used to mark the packets. Since the nature of the sensor networks is cooperative, using the head of the packets is proposed for authentication. Also using the marked head can prevent from sending and receiving fake data in the other nodes. Simulations have been performed in environments with imposing unrealistic data and having a probability from 1% to 10%. Comparing the proposed method with other methods shows that the proposed method in term of security, reducing traffic and increasing network lifetime is more effective. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  58 - Joint Relay Selection and Power Allocation in MIMO Cooperative Cognitive Radio Networks
  Mehdi  Ghamari Adian Hassan Aghaeenia
  10.7508/jist.2015.01.005
  In this work, the issue of joint relay selection and power allocation in Underlay MIMO Cooperative Cognitive Radio Networks (U-MIMO-CCRN) is addressed. The system consists of a number of secondary users (SUs) in the secondary network and a primary user (PU) in the prima More

  In this work, the issue of joint relay selection and power allocation in Underlay MIMO Cooperative Cognitive Radio Networks (U-MIMO-CCRN) is addressed. The system consists of a number of secondary users (SUs) in the secondary network and a primary user (PU) in the primary network. We consider the communications in the link between two selected SUs, referred to as the desired link which is enhanced using the cooperation of one of the existing SUs. The core aim of this work is to maximize the achievable data rate in the desired link, using the cooperation of one of the SUs which is chosen opportunistically out of existing SUs. Meanwhile, the interference due to the secondary transmission on the PU should not exceed the tolerable amount. The approach to determine the optimal power allocation, i.e. the optimal transmits covariance and amplification matrices of the SUs, and also the optimal cooperating SU is proposed. Since the proposed optimal approach is a highly complex method, a low complexity approach is further proposed and its performance is evaluated using simulations. The simulation results reveal that the performance loss due to the low complexity approach is only about 14%, while the complexity of the algorithm is greatly reduced. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  59 - Ant Colony Scheduling for Network On Chip
  Neda  Dousttalab Mohammad Ali Jabraeil Jamali Ali Ghaffari
  10.7508/jist.2015.02.004
  The operation scheduling problem in network on chip is NP-hard; therefore effective heuristic methods are needful to provide modal solutions. This paper introduces ant colony scheduling, a simple and effective method to increase allocator matching efficiency and hence n More

  The operation scheduling problem in network on chip is NP-hard; therefore effective heuristic methods are needful to provide modal solutions. This paper introduces ant colony scheduling, a simple and effective method to increase allocator matching efficiency and hence network performance, particularly suited to networks with complex topology and asymmetric traffic patterns. Proposed algorithm has been studied in torus and flattened-butterfly topologies with multiple types of traffic pattern. Evaluation results show that this algorithm in many causes has showed positive effects on reducing network delays and increased chip performance in comparison with other algorithms. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  60 - Trust evaluation in unsupervised network: A fuzzy logic approach
  Golnar Assadat  Afzali Monireh Hosseini
  10.7508/jist.2014.04.005
  Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and secu More

  Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and security of users. In this research, by using social networks as supervised networks, trust level of users is evaluated and by identifying these users in unsupervised networks, appropriate trust level is assigned to them. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  61 - Scalable Community Detection through Content and Link Analysis in Social Networks
  Zahra  Arefian Mohammad Reza  Khayyam Bashi
  10.7508/jist.2015.04.003
  Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. More

  Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. Using features that are available in the social networks, first discovering a complete and comprehensive communication should be done. Many methods have been proposed to explore the community, which are community detections through link analysis and nodes content. Most of the research exploring the social communication network only focuses on the one method, while attention to only one of the methods would be a confusion and incomplete exploration. Community detections is generally associated with graph clustering, most clustering methods rely on analyzing links, and no attention to regarding the content that improves the clustering quality. In this paper, to scalable community detections, an integral algorithm is proposed to cluster graphs according to link structure and nodes content, and it aims finding clusters in the groups with similar features. To implement the Integral Algorithm, first a graph is weighted by the algorithm according to the node content, and then network graph is analyzed using Markov Clustering Algorithm, in other word, strong relationships are distinguished from weak ones. Markov Clustering Algorithm is proposed as a Multi-Level one to be scalable. The proposed Integral Algorithm was tested on real datasets, and the effectiveness of the proposed method is evaluated. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  62 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
  Hamideh Sadat Cheraghchi Ali Zakerolhossieni
  10.7508/jist.2016.02.002
  The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social More

  The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  63 - Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM
  hojatallah hamidi Atefeh Daraei
  10.7508/jist.2016.02.003
  Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effe More

  Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by Support Vector Machine. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, K-Nearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  64 - Coverage Improving with Energy Efficient in Wireless Sensor Networks
  Amir Pakmehr Ali Ghaffari
  10.7508/jist.2017.17.008
  Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service qua More

  Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service quality in WSNs. In most of the applications, the sensor nodes are scattered in the environment randomly that causes the density of the nodes to be high in some regions and low in some other regions. In this case, some regions are not covered with any nodes of the network that are called covering holes. Moreover, creating some regions with high density causes extra overlapping and consequently the consumption of energy increases in the network and life of the network decreases. The proposed approach causes an increase in life of the network and an increase in it through careful selection of the most appropriate approach as cluster head node and form clusters with a maximum length of two steps and selecting some nodes as redundancy nodes in order to cover the created holes in the network. The proposed scheme is simulated using MATLAB software. The function of the suggested approach will be compared with Learning Automata based Energy Efficient Coverage protocol (LAEEC) approach either. Simulation results shows that the function of the suggested approach is better than LAEEC considering the parameters such as average of the active nodes, average remaining energy in nodes, percent of network coverage and number of control packets. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  65 - Analysis of expert finding algorithms in social network in order to rank the top algorithms
  AhmadAgha kardan Behnam Bozorgi
  10.7508/jist.2017.20.002
  The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed f More

  The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed for knowledge-sharing aim to improve user knowledge, making it imperative to have a mechanism that can evaluate users’ knowledge level or in other words “to find experts”. There is a need for expert-finding algorithms in social networks or any other knowledge sharing environment like question and answer communities. There are various content analysis and link analysis methods for expert-finding in social networks. This paper aims to challenge four algorithms by applying them to our dataset and analyze the results in order to compare the algorithms. The algorithms suitable for expert finding has been found and ranked. Based on the results and tests it is concluded that the Z-score algorithm has a better performance than others. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  66 - Node Classification in Social Network by Distributed Learning Automata
  Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda
  10.7508/jist.2017.18.005
  The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitio More

  The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  67 - A Novel Resource Allocation Algorithm for Heterogeneous Cooperative Cognitive Radio Networks
  Mehdi Ghamari Adian
  10.7508/jist.2017.18.008
  In cognitive radio networks (CRN), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary us More

  In cognitive radio networks (CRN), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users (PUs). Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity is a current research focus and in this work, cooperative diversity is investigated as a promising solution for this problem. Cooperative diversity has the capability to achieve diversity gain for wireless networks. Thus, in this work, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied carefully. To accomplish this, a resource allocation (RA) model is developed and analyzed for the heterogeneous, cooperative CRN. In the considered model, during cooperation, a best relay is selected to assist the secondary users (SUs) that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the SUs to improve their transmission rates while still causing minimal harm to the PUs. The results show a remarkable improvement in the RA performance of the CRN when cooperation is employed in contrast to when the CRN operates only by direct communication. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  68 - Data Aggregation Tree Structure in Wireless Sensor Networks Using Cuckoo Optimization Algorithm
  Elham Mohsenifard Behnam Talebi
  10.7508/jist.2016.03.006
  Wireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be rech More

  Wireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be recharged. Certain approaches are needed so that the power of the sensors can be efficiently and optimally utilized. One of the notable approaches for reducing energy consumption in WSNs is to decrease the number of packets to be transmitted in the network. Using data aggregation method, the mass of data which should be transmitted can be remarkably reduced. One of the related methods in this approach is the data aggregation tree. However, it should be noted that finding the optimization tree for data aggregation in networks with one working-station is an NP-Hard problem. In this paper, using cuckoo optimization algorithm (COA), a data aggregation tree was proposed which can optimize energy consumption in the network. The proposed method in this study was compared with genetic algorithm (GA), Power Efficient Data gathering and Aggregation Protocol- Power Aware (PEDAPPA) and energy efficient spanning tree (EESR). The results of simulations which were conducted in matlab indicated that the proposed method had better performance than GA, PEDAPPA and EESR algorithm in terms of energy consumption. Consequently, the proposed method was able to enhance network lifetime. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  69 - A Hybrid Cuckoo Search for Direct Blockmodeling
  Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour
  10.7508/jist.2017.18.001
  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit More

  As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  70 - Crisis management using spatial query processing in wireless sensor networks
  mohammad shakeri seyyed majid mazinani
  10.7508/jist.2017.18.004
  Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real chal More

  Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real challenge in crisis management is the inability to timely receive the information from the stricken areas. Technology has come to the aid of crisis management programs to help find an answer to the problem. One of these technologies is wireless sensor network. With recent advances in this field, sensor nodes can independently respond to the queries from the users. This has transformed the processing of the queries into one of the most useful chapters in sensor networks. Without requiring any infrastructure, the sensor network can easily be deployed in the stricken area. And with the help of spatial query processing, it can easily provide managers with the latest information. The main problem, however, is the irregular shape of the area. Since these areas require many points to present them, the transmission of the coordinates by sensor nodes necessitates an increase in the number of data packet transmissions in the sensor network. The high number of packets considerably increases energy consumption. In related previous works, to solve this problem, line simplification algorithm s, such as Ramer-Douglas-Peucker (RDP), were used. These algorithms could lessen energy consumption by reducing the number of points in the shape of the area. In this article, we present a new algorithm to simplify packet shapes which can reduce more points with more accuracy. This results in decreasing the number of transmitted packets in the network, the concomitant reduction of energy consumption, and, finally, increasing network lifetime. Our proposed method was implemented in different scenarios and could on average reduce network’s energy consumption by 72.3%, while it caused only 4.5% carelessness which, when compared to previous methods, showed a far better performance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  71 - A Model for Mobile Code Computing Paradigms in Computer Networks
  Hojatallah Hamidi Maryam Parvini
  10.7508/jist.2017.17.002
  This paper presents a reliable model for mobile codes in distributed networks, which represents reliable mobile agent execution. The model ensures non-blocking mobile agent execution and forces the once property without relying on correct fault detection. A mobile agent More

  This paper presents a reliable model for mobile codes in distributed networks, which represents reliable mobile agent execution. The model ensures non-blocking mobile agent execution and forces the once property without relying on correct fault detection. A mobile agent execution is blocking if a fault of agent prevents the agent from continuing in its execution. The once problem is related to non-blocking in the sense that solutions to the latter may lead to multiple executions of the mobile agent. A solution to reliable mobile agent execution needs to ensure both the non-blocking and once properties. The analytical results show new theoretical perceptions into the statistical behaviors of mobile agents and provide useful tools for executing mobile agents in networks. The results show that agents' behavior is influenced by places' characteristics and the agents' behavior can be managed to network. In this paper, we analyzed the average time consuming of mobile agents between two places. The approach, Fault-Tolerant approach for mobile codes offers a user-transparent fault tolerance which can be selected by the user for every single application given to the environment. Thereby, the user can decide for every application weather it has to be treated fault-tolerant or not. We proposed a reliable execution model of mobile codes and analyzed the life expectancy, including the average time consuming of mobile agents between two places, the average number of places agents will visit, and the agents' life expectancy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  72 - The Separation of Radar Clutters using Multi-Layer Perceptron
  Mohammad Akhondi Darzikolaei Ataollah Ebrahimzadeh Elahe Gholami
  10.7508/jist.2017.17.006
  Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complica More

  Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complicated issues. Therefore, in this paper aims to classify radar clutters. The novel proposed MLP-based classifier for separating radar clutters is introduced. This classifier is designed with different hidden layers and five training algorithms. These training algorithms consist of Levenberg-Marquardt, conjugate gradient, resilient back-propagation, BFGS and one step secant algorithms. Statistical distributions are established models which widely used in the performance calculations of radar clutters. Hence In this research, Rayleigh, Log normal, Weibull and K-distribution clutters are utilized as input data. Then Burg’s reflection coefficients, skewness and kurtosis are three features which applied to extract the best characteristics of input data. In the next step, the proposed classifier is tested in different conditions and the results represent that the proposed MLP-based classifier is very successful and can distinguish clutters with high accuracy. Comparing the results of proposed technique and RBF-based classifier show that proposed method is more efficient. The results of simulations prove that the validity of MLP-based method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  73 - Toward Energy-Aware Traffic Engineering in Intra-Domain IP Networks Using Heuristic and Meta-Heuristics Approaches
  Muharram Mansoorizadeh
  10.7508/jist.2018.02.005
  Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization i More

  Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization in internet service providers is about 30-40%. Energy-aware traffic engineering aims to change routing algorithms so that low utilized links would be deactivated and their load would be distributed over other routes. As a consequence, by turning off these links and their respective devices and ports, network energy consumption is significantly decreased. In this paper, we propose four algorithms for energy-aware traffic engineering in intra-domain networks. Sequential Link Elimination (SLE) removes links based on their role in maximum network utilization. As a heuristic method, Extended Minimum Spanning Tree (EMST) uses minimum spanning trees to eliminate redundant links and nodes. Energy-aware DAMOTE (EAD) is another heuristic method that turns off links with low utilization. The fourth approach is based on genetic algorithms that randomly search for feasible network architectures in a potentially huge solution space. Evaluation results on Abilene network with real traffic matrix indicate that about 35% saving can be obtained by turning off underutilized links and routers on off-peak hours with respect to QoS. Furthermore, experiments with GA confirm that a subset of links and core nodes with respect to QoS can be switched off when traffic is in its off-peak periods, and hence energy can be saved up to 37%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  74 - Representing a Content-based link Prediction Algorithm in Scientific Social Networks
  Hosna Solaimannezhad omid fatemi
  10.7508/jist.2017.19.001
  Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. A More

  Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as coauthor dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  75 - Analysis of Business Customers’ Value Network Using Data Mining Techniques
  Forough Farazzmanesh (Isvand) Monireh Hosseini
  10.7508/jist.2017.19.003
  In today's competitive environment, customers are the most important asset to any company. Therefore companies should understand what the retention and value drivers are for each customer. An approach that can help consider customers‘ different value dimensions is the More

  In today's competitive environment, customers are the most important asset to any company. Therefore companies should understand what the retention and value drivers are for each customer. An approach that can help consider customers‘ different value dimensions is the value network. This paper aims to introduce a new approach using data mining techniques for mapping and analyzing customers‘ value network. Besides, this approach is applied in a real case study. This research contributes to develop and implement a methodology to identify and define network entities of a value network in the context of B2B relationships. To conduct this work, we use a combination of methods and techniques designed to analyze customer data-sets (e.g. RFM and customer migration) and to analyze value network. As a result, this paper develops a new strategic network view of customers and discusses how a company can add value to its customers. The proposed approach provides an opportunity for marketing managers to gain a deep understanding of their business customers, the characteristics and structure of their customers‘ value network. This paper is the first contribution of its kind to focus exclusively on large data-set analytics to analyze value network. This new approach indicates that future research of value network can further gain the data mining tools. In this case study, we identify the value entities of the network and its value flows in the telecommunication organization using the available data in order to show that it can improve the value in the network by continuous monitoring. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  76 - Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
  Ramin Safa Seyed Abolghassem Mirroshandel Soroush Javadi Mohammad Azizi
  10.7508/jist.2018.21.005
  Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying More

  Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  77 - Lifetime Maximization by Dynamic Threshold and Sensor Selection in Multi-channel Cognitive Sensor Network
  Asma Bagheri Ataollah Ebrahimzadeh maryam najimi
  10.7508/jist.2017.20.003
  The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor netw More

  The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  78 - Clustering for Reduction of Energy Consumption in Wireless Sensor Networks by AHP Method
  Mohammad Reza  Taghva Robab  Hamlbarani Haghi Aziz Hanifi Kamran  feizi
  10.7508/jist.2018.21.002
  Due to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of t More

  Due to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of these networks. Due to the energy limitation of nodes, energy efficiency is considered as a key objective in the design of sensor networks. In this paper, we present a method in which, in the first phase, nodes obtain their position by using the position of the base station and two other two nodes informed geographic position and are out of covered environment. In the second phase, the optimal location of the base station is determined. In the third phase, we determine the cluster heads based on the criteria such as the remaining energy, the distance (the distance from the cluster head and the distance from the base station), the number of neighbors (the one-step neighbors and the two-step neighbors) and the centrality. Using the multi-as criteria to select optimally cluster heads by decision making method. We implement the proposed method in the NS2 environment and evaluate its effect and compare it with the NEECP E-LEACH protocols. Simulation results show that by reducing energy consumption, the proposed method enhances the network life time expectancy. In addition it improves average packet delivery and the average delay. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  79 - A New Set Covering Controller Placement Problem Model for Large Scale SDNs
  احمد جلیلی رضا اکبری منیژه  کشتگری
  10.7508/jist.2018.21.004
  Software Defined Network (SDN) is an emerging architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler and manageable network. New SDN paradigms are encouraged to deploy multiple (rather than centra More

  Software Defined Network (SDN) is an emerging architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler and manageable network. New SDN paradigms are encouraged to deploy multiple (rather than centralized) controllers to monitor the entire system. The Controller Placement Problem (CPP) is one of the key issues in SDN that affects every aspect of it such as scalability, convergence time, fault tolerance and node to controller latency. This problem has been investigated in diverse papers with their major attention paid on optimizing the location of an arbitrary number of controllers. The related works in this area get less attention to two following important issues. i) Bidirectional end-to-end latency between switch and its controller instead of propagation latency, ii) finding the minimal number of controllers that even is a prerequisite for locating them. In this paper, a Set Covering Controller Placement Problem Model (SCCPPM) to find the least number of required controllers with regard to carrier grade latency requirement is proposed. The new model is carried out on a set of 124 graphs from the Internet Topology Zoo and solve them with IBM ILOG CPLEX Optimization package. As expected, our results indicate that the number of required controllers for high resiliency is dependent on topology and network size. As well, in order to achieve carrier grade requirement, 86 percent of topologies must have more than one controller. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  80 - Security Enhancement of Wireless Sensor Networks: A Hybrid Efficient Encryption Algorithm Approach
  Omid Mahdi Ebadati Farshad Eshghi Amin Zamani
  10.7508/jist.2018.03.007
  Wireless sensor networks are new technologies that are used for various purposes such as environmental monitoring, home security, industrial process monitoring, healthcare programs and etc. Wireless sensor networks are vulnerable to various attacks. Cryptography is one More

  Wireless sensor networks are new technologies that are used for various purposes such as environmental monitoring, home security, industrial process monitoring, healthcare programs and etc. Wireless sensor networks are vulnerable to various attacks. Cryptography is one of the methods for secure transmission of information between sensors in wireless sensor networks. A complete and secure encryption system must establish three principles of confidentiality, authentication and integrity. An encryption algorithm alone cannot provide all the principles of encryption. A hybrid encryption algorithm, consisting of symmetric and asymmetric encryption algorithms, provides complete security for a cryptographic system. The papers presented in this area over the last few years, and a new secure algorithm present with regard to the limitations of wireless sensor networks, which establishes three principles of cryptography. The details of the algorithm and basic concepts are presented in such a way that the algorithm can be operational and showed a very high efficiency in compare to the current proposed methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  81 - Lifetime Improvement Using Cluster Head Selection and Base Station Localization in Wireless Sensor Networks
  maryam najimi Sajjad  Nankhoshki
  10.7508/jist.2018.02.006
  The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most eff More

  The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the cluster heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy- efficient algorithm, cluster heads distance from the base station and their residual energy of the sensors nodes are important parameters for cluster head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  82 - An SRN Based Approach for Performance Evaluation of Network Layer in Mobile Ad hoc Networks
  meisam Yadollahzadeh tabari Ali A Pouyan
  10.7508/jist.2018.04.006
  The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing More

  The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing high level performance metrics. Also there is no theoretical background for mentioned simulators, too. In this research, we propose a framework for performance evaluation of mobile ad hoc networks. The presented framework points to the network layer of MANETs using SRN (Stochastic Reward Nets) modeling tool as variation of generalized stochastic Petri net (GSPN). Based on decomposition technique it encompasses two separate models: one for analysis of data flowing process and the other for modeling routing process ; supposing AODV as a routing protocol that is worked out. To verify the presented model, an equivalence-based method is applied. The proposed SRN model has been quantified by deriving two performances metrics as Packet Delivery Ratio (PDR) and End-to-end Delay. Both metrics are also compared to the value obtained from NS-2 simulator versus different number of nodes and four packet generation rates. The results show the obtained values from presented SRN model well matched to the values generated from NS-2 simulator with a considerable lesser execution time. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  83 - Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder
  Hanieh Zamanian Hassan Farsi Sajad Mohammadzadeh
  10.7508/jist.2018.03.002
  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predic More

  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  84 - A Multi-objective Multi-agent Optimization Algorithm for the Community Detection Problem
  Amirhossein Hosseinian Vahid Baradaran
  10.7508/jist.2018.03.006
  This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, whil More

  This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, while they have a lower density of between-group connections. Due to high practical usage of community detection in scientific fields, many researchers developed different algorithms to meet various scientific requirements. However, single-objective optimization algorithms may fail to detect high quality communities of complex networks. In this paper, a novel multi-objective Multi-agent Optimization Algorithm, named the MAOA is proposed to detect communities of complex networks. The MAOA aims to optimize modularity and community score as objective functions, simultaneously. In the proposed algorithm, each feasible solution is considered as an agent and the MAOA organizes agents in multiple groups. The MAOA uses new search operators based on social, autonomous and self-learning behaviors of agents. Moreover, the MAOA uses the weighted sum method (WSM) in finding the global best agent and leader agent of each group. The Pareto solutions obtained by the MAOA is evaluated in terms of several performance measures. The results of the proposed method are compared with the outputs of three meta-heuristics. Experiments results based on five real-world networks show that the MAOA is more efficient in finding better communities than other methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  85 - A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
  Abbas Mirzaei Amir Rahimi
  10.7508/jist.2019.01.005
  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided More

  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  86 - SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications
  Morteza Jaderyan Hassan Khotanlou
  10.7508/jist.2019.02.004
  The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core component More

  The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core components of the system. Semantic, lexical, syntactical and structural features are identified by the pre-processing module. The enrichment module is introduced to identify contextually similar concepts and concept maps for improving the representation. The information content of documents and the enriched contents are fused (merged) into the graphical structure of semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 dataset are used for evaluation. The evaluation results suggest that the proposed method exhibits a high level of accuracy, recall and precision. The results also indicate that even when a small portion of information content is available, the proposed method performs well in standard text mining applications. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  87 - Investigate Network Simulation Tools in Designing and Managing Intelligent Systems
  fatemeh fakhar
  10.7508/jist.2019.04.004
  Network simulation is a technique that models network behavior by performing transaction calculations between different network entities and using mathematical formulas and taking observations of network products. A network simulator is a software program have been appl More

  Network simulation is a technique that models network behavior by performing transaction calculations between different network entities and using mathematical formulas and taking observations of network products. A network simulator is a software program have been applied to analyze the performance of a computer network without the presence of a real network. Hardware equipment, equipment configuration, communication, and routing protocols and network traffic modeled in simulation software and the behavior of the network and its components examined from different dimensions. The user can also customize the simulation software according to their needs. Simulation software has different uses, and the user can use these tools to model their network by recognizing this software. In terms of research, it is difficult to create a network, especially large networks, in a real-time scenario, and it is not easily possible to carry out it in the real world, and it is very costly. So, simulators help network developers to control whether the network can work in real-time or not, or whether it is efficient enough. This reduces the time and cost of network application testing.Today, simulation technology is successfully used to model, design and manage a variety of intelligent systems. Numerous tools have been created in this regard. In this article, we review and compare important network simulators such as CloudSim, GloMoSim, GNS3, NS-2, Opnet, OMNet ++, NetSim, NS-3, AVRORA, Packet Tracer, QualNet, J-Sim, REAL and OptSim and their results. These comparisons express from several perspectives in the tables. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  88 - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
  Shima Mehrabi Seyed Abolghassem Mirroshandel Hamidreza  Ahmadifar
  10.7508/jist.2019.03.005
  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language pro More

  With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  89 - BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers
  Hasibeh Naseri Sadoon Azizi Alireza Abdollahpouri
  10.7508/jist.2019.03.002
  To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data cent More

  To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data center design. Flow-based mechanisms usually suffer from collision between elephant flows; while, packet-based mechanisms encounter packet re-ordering phenomenon. Both of these challenges lead to severe performance degradation in a data center network. To address these problems, in this paper, we propose an efficient mechanism for the flow scheduling problem in cloud data center networks. The proposed mechanism, on one hand, makes decisions per flow, thus preventing the necessity for rearrangement of packets. On the other hand, thanks do SDN technology and utilizing bidirectional search algorithm, our proposed method is able to distribute elephant flows across the entire network smoothly and with a high speed. Simulation results confirm the outperformance of our proposed method with the comparison of state-of-the-art algorithms under different traffic patterns. In particular, compared to the second-best result, the proposed mechanism provides about 20% higher throughput for random traffic pattern. In addition, with regard to flow completion time, the percentage of improvement is 12% for random traffic pattern Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  90 - Using Residual Design for Key Management in Hierarchical Wireless Sensor Networks
  Vahid Modiri Hamid Haj Seyyed Javadi Amir Masoud  Rahmani Mohaddese Anzani
  10.7508/jist.2020.01.006
  Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial stru More

  Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial structures in which key-rings, are pre-distributed to each sensor node before deployment in a real environment. Regarding the restricted resources, key distribution is a great engagement and challenging issue in providing sufficient security in wireless sensor networks. To provide secure communication, a unique key should be found from their stored key-rings. Most of the key pre-distribution protocols based on public-key mechanisms could not support highly scalable networks due to their key storage overhead and communication cost that linearly increasing. In this paper, we introduce a new key distribution approach for hierarchical clustered wireless sensor networks. Each cluster has a construction that contains new points or that reinforces and builds upon similar ideas of their head clusters. Based on Residual Design as a powerful algebraic combinatorial architecture and hierarchical network model, our approach guarantees good connectivity between sensor nodes and also cluster heads. Compared with similar existing schemes, our approach can provide sufficient security no matter if the cluster head or normal sensor node is compromised Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  91 - Energy Efficient Clustering Algorithm for Wireless Sensor Networks
  Maryam Bavaghar Amin Mohajer Sarah Taghavi Motlagh
  10.7508/jist.2019.04.001
  In Wireless Sensor Networks (WSNs), sensor nodes are usually deployed with limited energy reserves in remote environments for a long period of time with less or no human intervention. It makes energy efficiency as a challenging issue both for the design and deployment o More

  In Wireless Sensor Networks (WSNs), sensor nodes are usually deployed with limited energy reserves in remote environments for a long period of time with less or no human intervention. It makes energy efficiency as a challenging issue both for the design and deployment of sensor networks. This paper presents a novel approach named Energy Efficient Clustering Algorithm (EECA) for Wireless Sensor Networks which is based on two phases clustering model and provides maximum network coverage in an energy efficient way. In this framework, an effective resource-aware load balancing approach applied for autonomous methods of configuring the parameters in accordance with the signaling patterns in which approximately the same bit rate data is provided for each sensor. This resource-efficient clustering model can also form energy balanced clusters which results in increasing network life time and ensuring better network coverage. Simulation results prove that EECA is better than LEACH, LEA2C and EECS with respect to network lifetime and at the same time achieving more network coverage. In addition to obtained an optimal cluster size with minimum energy loss, the proposed approach also suggests new and better way for selecting cluster heads to reduce energy consumption of the distributed nodes resulting in increased operational reliability of sensor networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  92 - Drone Detection by Neural Network Using GLCM and SURF Features
  Tanzia  Ahmed Tanvir  Rahman Bir  Ballav Roy Jia Uddin
  10.52547/jist.9.33.15
  This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extrac More

  This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  93 - Recognizing Transliterated English Words in Persian Texts
  Ali Hoseinmardy Saeedeh Momtazi
  10.29252/jist.8.30.84
  One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classific More

  One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classification and clustering. In this case, if the text-processing engine does not use similar/related words in the same sense, it may not be able to guide you to the appropriate result. Various statistical techniques have been proposed to bridge the vocabulary gap problem; e.g., if two words are used in similar contexts frequently, they have similar/related meanings. Synonym and similar words, however, are only one of the categories of related words that are expected to be captured by statistical approaches. Another category of related words is the pair of an original word in one language and its transliteration from another language. This kind of related words is common in non-English languages. In non-English texts, instead of using the original word from the target language, the writer may borrow the English word and only transliterate it to the target language. Since this kind of writing style is used in limited texts, the frequency of transliterated words is not as high as original words. As a result, available corpus-based techniques are not able to capture their concept. In this article, we propose two different approaches to overcome this problem: (1) using neural network-based transliteration, (2) using available tools that are used for machine translation/transliteration, such as Google Translate and Behnevis. Our experiments on a dataset, which is provided for this purpose, shows that the combination of the two approaches can detect English words with 89.39% accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  94 - Energy Efficient Cross Layer MAC Protocol for Wireless Sensor Networks in Remote Area Monitoring Applications
  R Rathna L Mary Gladence J Sybi Cynthia V Maria Anu
  10.52547/jist.9.35.207
  Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless d More

  Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  95 - Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory
  Nazbanoo Farzaneh Reyhaneh Hoseini
  10.52547/jist.9.33.25
  DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a secur More

  DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a security unit, is placed on the control plane of the software-defined wireless sensor network aiming at detecting the suspicious traffic. The main purpose of this paper is detection of the DDoS attack using the central controller of the software-defined network and entropy approach as an effective light-weight and quick solution in the early stages of the detection and, also, Dempster-Shafer theory in order to do a more exact detection with longer time. Evaluation of the attacks including integration of data from the evidence obtained using Dempster-Shafer and entropy modules has been done with the purpose of increasing the rate of detection of the DDoS attack, maximizing the true positive, decreasing the false negative, and confronting the attack. The results of the paper show that providing a security unit on the control plane in a software-defined wireless sensor network is an efficient method for detecting and evaluating the probability of DDoS attacks and increasing the rate of detection of an attacker. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  96 - Providing a Network for Measuring the Dynamics Volatility Connectedness of Oil and Financial Markets
  Nasser Gholami Teymor Mohammadi Hamid Amadeh Morteza  Khorsandi
  10.29252/jist.8.31.149
  Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure t More

  Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure the dynamics connectedness of selected stock markets in the Middle East, oil markets, gold, the dollar index, and euro-dollar and pound-dollar exchange rates during the period February 2007 to August 2019 in networks with different weekly horizons. In this paper, we intend to evaluate the pairwise impact of crude oil and the Middle East stock markets, in particular on the Tehran Stock Exchange, and to analyze this variance using different time horizons. The results show that in all time horizons the variance of forecast error in most markets is due to the shocks themselves. The Saudi Arabian Stock Exchange has the most impact on other Middle Eastern stocks. The dynamics connectedness of the oil markets is remarkable, however, as the time horizon increases, dynamic connectedness between the two markets decreases and they are mostly affected by other markets, especially the Middle East stock exchanges except for Iran. Moreover, Iran stock market is an isolated market. About the gold market, there is a significant connectedness with the pound-dollar exchange rate and gold market; however, the dynamics connectedness of this market with other markets are not significant. Therefore, this market and Iran stock exchange can be used as a tool to hedge risk for investors. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  97 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
  Mohammad Pouya Salvati Jamshid  Bagherzadeh Mohasefi Sadegh Sulaimany
  10.52547/jist.9.35.183
  Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj More

  Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  98 - Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification
  Nidhi Chandra Laxmi  Ahuja Sunil Kumar Khatri Himanshu Monga
  10.52547/jist.9.34.89
  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time a More

  The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow are – sentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  99 - Sailor Localization in Oceans Beds using Genetic and Firefly Algorithm
  Shruti  Gupta Dr Ajay  Rana Vineet  Kansal
  10.29252/jist.8.31.175
  The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the More

  The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the position of relative nodes in the network allowing a better and effective network for increasing the efficiency and thus increasing the lifeline of the network. Determining the current limitations in FA that are applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinity of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. This paper presents GEFIR (GenFire) algorithm to calculate position of unknown nodes for the fishermen in the ocean. The proposed approach calculates the position of unknown nodes, the proposed method effectively selects the anchor node in the cluster head to reduce the energy dissipation. Major benefits over other similar localization algorithms are a better positioning of nodes is provided and average localization error is reduced which eventually leads to better efficiency thus optimize the lifetime of the network for sailors. The obtained results depict that the proposed model surpasses the previous generation of localization algorithm in terms of energy dispersion and location estimation which is suitable for fishermen on the ocean bed. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  100 - Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)
  Mohammad Nejadeh Peyman Bayat Jalal Kheirkhah Hassan Moladoust
  10.29252/jist.8.31.197
  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the progno More

  Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  101 - Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
  Elham Gholami Seyed Reza Kamel Tabbakh Maryam khairabadi
  10.52547/jist.9.35.191
  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting nu More

  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  102 - A New Game Theory-Based Algorithm for Target Coverage in Directional Sensor Networks
  Elham Golrasan marzieh varposhti
  10.52547/jist.9.34.103
  One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage q More

  One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage quality by turning off redundant sensors and adjusting the direction of the active sensor nodes. In this paper, we address the problem of maximizing network lifetime with adjustable ranges (MNLAR) and propose a new game theory-based algorithm in which sensor nodes try to adjust their working direction and sensing range in a distributed manner to achieve the desired coverage. For this purpose, we formulate this problem as a multiplayer repeated game in which each sensor as a player tries to maximize its utility function which is designed to capture the tradeoff between target coverage and energy consumption. To achieve an efficient action profile, we present a distributed payoff-based learning algorithm. The performance of the proposed algorithm is evaluated via simulations and compared to some existing methods. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of network lifetime. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  103 - Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks
  Ali Sedighimanesh Hessam  Zandhessami Mahmood  Alborzi mohammadsadegh Khayyatian
  10.52547/jist.15638.10.37.37
  Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sen More

  Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teaching-learning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion: Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  104 - Self-Organization Map (SOM) Algorithm for DDoS Attack Detection in Distributed Software Defined Network (D-SDN)
  Mohsen Rafiee Alireza  shirmarz
  10.52547/jist.15644.10.38.120
  The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the netw More

  The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the network as if the nodes collaborated. This attack is called distributed denial-of-service (DDoS). There is offered a novel architecture for the future networks to make them more agile, programmable and flexible. This architecture is called software defined network (SDN) that the main idea is data and control network flows separation. This architecture allows the network administrator to resist DDoS attacks in the centralized controller. The main issue is to detect DDoS flows in the controller. In this paper, the Self-Organizing Map (SOM) method and Learning Vector Quantization (LVQ) are used for DDoS attack detection in SDN with distributed architecture in the control layer. To evaluate the proposed model, we use a labelled data set to prove the proposed model that has improved the DDoS attack flow detection by 99.56%. This research can be used by the researchers working on SDN-based DDoS attack detection improvement. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  105 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
  Ali Ghaffari Seyyed Keyvan  Mousavi
  10.52547/jist.9.35.197
  Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d More

  Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  106 - Providing a New Smart Camera Architecture for Intrusion Detection in Wireless Visual Sensor Network
  Meisam Sharifi Sani Amid Khatibi
  10.52547/jist.15672.11.41.31
  The wireless Visual sensor network is a highly functional domain of high-potential network generations in unpredictable and dynamic environments that have been deployed from a large number of uniform or non-uniform groups within the desired area, cause the realization o More

  The wireless Visual sensor network is a highly functional domain of high-potential network generations in unpredictable and dynamic environments that have been deployed from a large number of uniform or non-uniform groups within the desired area, cause the realization of large regulatory applications from the military and industrial domain to hospital and environment. Therefore, security is one of the most important challenges in these networks. In this research, a new method of routing smart cameras with the help of cloud computing technology has been provided. The framework in the cloud computing management layer increases security, routing, inter interaction, and other features required by wireless sensor networks. Systematic attacks are simulated by a series of standard data collected at the CTU University related to the Czech Republic with RapidMiner software. Finally, the accuracy of detection of attacks and error rates with the suggested NN-SVM algorithm, which is a combination of vector machines and neural networks, is provided in the smart cameras based on the visual wireless sensor networks in MATLAB software. The results show that different components of the proposed architecture meet the quality characteristics of visual wireless sensor networks. Detection of attacks in this method is in the range of 99.24% and 99.35% in the worst and best conditions, respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  107 - Secure Key Management Scheme for Hierarchical Network Using Combinatorial Design
  Siddiq Iqbal B R  Sujatha
  10.52547/jist.15691.10.37.20
  The wireless sensor network (WSN) signifies to a gathering of spatially spread and committed sensors for observing and logging the physical states of the environment and for organizing the information gathered at the central Base station. Many security threats may affec More

  The wireless sensor network (WSN) signifies to a gathering of spatially spread and committed sensors for observing and logging the physical states of the environment and for organizing the information gathered at the central Base station. Many security threats may affect the functioning of these networks. Security of the data in the system depends on the cryptographic procedure and the methods where encryption and decryption keys are developed among the sensors. Symmetric key foundation is one of the best applicable ideal models for safe exchanges in WSNs. The main goal is to improve and evaluate certain issues, such as node attack, to provide better key strength, connectivity, security for node interaction, and throughput. Uniform Balanced Incomplete Block Design (UBIBD) is used to generate the keys allocated by the base station to the cluster head. The cluster head distributes keys to its members using Symmetric Balanced Incomplete Block Design (SBIBD), and the keys are refreshed on a regular basis to avoid out-of-date entries. In wireless sensor networks, compromised nodes can be used to inject false reports. The concept of interacting between sensor nodes using keys and establishing a secure connection aids in ensuring the network's security. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  108 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
  Alireza Mansouri Fattaneh Taghiyareh
  10.52547/jist.9.33.1
  People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke More

  People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  109 - Deep Learning Approach for Cardiac MRI Images
  Afshin Sandooghdar Farzin Yaghmaee
  10.52547/jist.16121.10.37.61
  Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an o More

  Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an open question for scholars in the field. There are so many types of function approximators like Decision Tree, Gaussian Processes and Deep Learning, used in multi-layered Neural Networks (NNs), which should be evaluated to determine their effectiveness. Therefore, this study aimed to assess an approach based on DL techniques for modern medical imaging methods according to Magnetic Resonance Imaging (MRI) segmentation. To do so, an experiment with a random sampling approach was conducted. One hundred patient cases were used in this study for training, validation, and testing. The method used in this study was based on full automatic processing of segmentation and disease classification based on MRI images. U-Net structure was used for the segmentation process, with the use of cardiac Right Ventricular Cavity (RVC), Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and information extracted from the segmentation step. With train and using random forest classifier, and Multilayer Perceptron (MLP), the task of predicting the pathologic target class was conducted. Segmentation extracted information was in the form of comprehensive features handcrafted to reflect demonstrative clinical strategies. Our study suggests 92% test accuracy for cardiac MRI image segmentation and classification. As for the MLP ensemble, and for the random forest, test accuracy was equal to 91% and 90%, respectively. This study has implications for scholars in the field of medical image processing. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  110 - A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques
  Abbas Mirzaei Shahram Zandian
  10.52547/jist.16302.10.40.300
  Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomen More

  Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  111 - Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO
  Mohammad  Nazarpour navid nezafati Sajjad  Shokouhyar
  10.52547/jist.16307.10.40.270
  Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detect More

  Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  112 - An Intelligent Model for Multidimensional Personality Recognition of Users using Deep Learning Methods
  Hossein Sadr fatemeh mohades deilami morteza tarkhan
  Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opini More

  Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  113 - Sentiment analysis for stock market predection with deep neural network: A case study for international corporate stock database
  hakimeh mansour Saeedeh Momtazi Kamran Layeghi
  Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting impo More

  Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting important features between words by convolutional layers, we use LSTM layers to establish the relationship behind the sequence of words and extract the important features of the text. With discovery of new features extracted by LSTM, the ability of the proposed model to classify the stock values of companies increases. This article is based on the data of Nguyen et al. (2015) and uses only the emotional information of people in social networks to predict stocks. Given that we categorize each user's message into one of the emotional classes "Strong Buy", "Buy", "Hold", "Sell", "Strong Sell", this model can predict the stock value of the next day, whether it will be high or low. The proposed structure consisted of 21 layers of neural networks consisting of convolutional neural networks and long short-term memory network. These networks were implemented to predict the stock markets of 18 companies. Although some of the previously presented models have used for emotion analysis to predict the capital markets, the advanced hybrid methods have not been performed in deep networks with a good forecasting accuracy. The results were compared with 8 baseline methods and indicate that the performance of the proposed method is significantly better than other baselines. For daily forecasts of stocks changes, it resulted in 19.80% improvement in the prediction accuracy, compared with the deep CNN, and 24.50% and 23.94% improvement compared with the models developed by Nguyen et al. (2015) and Derakhshan et al. (2019), respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  114 - Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news
  arefeh esmaili Saeed Farzi
  For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has bec More

  For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  115 - A RPL-based Routing Algorithm for Multimedia Traffic for the Internet of Things
  Mohammad Khansari Farzaneh Mortazavi
  According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new More

  According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new objective function for the RPL routing protocol is proposed which addresses the characteristics of multimedia data in the routing process. In the objective function, node’s remaining energy and the remaining buffer capacity of nodes measures are combined using a weighted pair. In order to evaluate this method, input data is generated based on a video trace. Packet delivery ratio, network lifetime, nodes availability over the lifetime of the network, node energy distribution, and end-to-end delay are used to evaluate the proposed method. The evaluation results show that the proposed method increases the package delivery ratio compared to the standard RPL. This method also improves the lifetime of the nodes by distributing energy between the nodes in comparison with standard RPL and extends the node's availability over the lifetime of the network. Finally, it reduces the network congestion which led to a lower end-to-end delay. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  116 - Edge Detection and Identification using Deep Learning to Identify Vehicles
  Zohreh Dorrani Hassan Farsi Sajad Mohammadzadeh
  10.52547/jist.16385.10.39.201
  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract informati More

  A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  117 - Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs)
  Sara Motamed Elham Askari
  10.52547/jist.16399.10.39.222
  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might More

  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might interfere with your own learning, work, and communication with others, it could be controlled by early diagnosis and treatment. Because the automatic recognition and classification of electroencephalography (EEG) signals is challenging due to the large variation in time features and signal frequency, the present study attempts to provide an efficient method for diagnosing hyperactive patients. The proposed method is that first, the recorded brain signals of hyperactive subjects are read from the input and in order to the signals to be converted from time range to frequency range, Fast Fourier Transform (FFT) is used. Also, to select an effective feature to check hyperactive subjects from healthy ones, the peak frequency (PF) is applied. Then, to select the features, principal component analysis and without principal component analysis will be used. In the final step, convolutional neural networks (CNNs) will be utilized to calculate the recognition rate of individuals with hyperactivity. For model efficiency, this model is compared to the models of K- nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the best method is to use feature selection by principal component analysis and classification of CNNs and the recognition rate of individuals with ADHD from healthy ones is equal to 91%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  118 - Hierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
  Nagesh Jadhav
  10.52547/jist.16499.10.38.89
  Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distres More

  Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distress in humans. Personalized affective cues provide a non-invasive approach considering visual, vocal, and verbal cues to recognize the affective state. In this paper, we are proposing a multimodal hierarchical weighted framework to recognize emotional distress. We are utilizing negative emotions to detect the unapparent behavior of the person. To capture facial cues, we have employed hybrid models consisting of a transfer learned residual network and CNN models. Extracted facial cue features are processed and fused at decision using a weighted approach. For audio cues, we employed two different models exploiting the LSTM and CNN capabilities fusing the results at the decision level. For textual cues, we used a BERT transformer to learn extracted features. We have proposed a novel decision level adaptive hierarchical weighted algorithm to fuse the results of the different modalities. The proposed algorithm has been used to detect the emotional distress of a person. Hence, we have proposed a novel algorithm for the detection of emotional distress based on visual, verbal, and vocal cues. Experiments on multiple datasets like FER2013, JAFFE, CK+, RAVDESS, TESS, ISEAR, Emotion Stimulus dataset, and Daily-Dialog dataset demonstrates the effectiveness and usability of the proposed architecture. Experiments on the enterface'05 dataset for distress detection has demonstrated significant results. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  119 - The Causes and Consequences of Ethical Delinquency of Children and Adolescents in Telegram and Instagram Social Networksg
  mohamad sadeq chavooshi Hadi  Karamati Moez
  Children and teenagers are the most vulnerable in social networking telegrams and instagrams; social networks expose under-age users to a huge amount of information, partly because of their inappropriateness of age and rational growth and the rate The child's knowledge More

  Children and teenagers are the most vulnerable in social networking telegrams and instagrams; social networks expose under-age users to a huge amount of information, partly because of their inappropriateness of age and rational growth and the rate The child's knowledge can have harmful effects. Therefore, it is essential that the skills needed to be present on the social networks of telegrams and instagrams are taught to children and their parents, as well as the causes and effects of the moral delinquency of this vulnerable stratum in social networks. Prevent possible damage. In this paper, through analytical-descriptive research method, a library is used to identify the causes and effects of moral delinquency of children and adolescents in the social networks of Telegram and Instagram. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  120 - Investigating the Impact of Ethical Attitudes on Digital Marketing Strategies On the performance of the hotel industry
  Ali  Nakhchian Ali   Hosseinzadeh Hossein momenimahmouei Mohammad Ghasemi Namaghi
  Work ethic is the most important factor in the success of the hotel industry. The contemporary world is evolving at an Amazing speed Organizations, as one of the most prominent characteristics of today's societies, are rapidly changing and evolving, and in the current s More

  Work ethic is the most important factor in the success of the hotel industry. The contemporary world is evolving at an Amazing speed Organizations, as one of the most prominent characteristics of today's societies, are rapidly changing and evolving, and in the current system, improving performance is one of the main goals of any living and active organization. So it is clear that examining the variables that affect it can be a guide for managers to improve the company. In this regard, in this study, we seek to investigate the effect of digital marketing strategies On the performance of the hotel industry. In terms of type and nature, this research is a descriptive-analytical research and in terms of purpose, it is a type of applied research. In this study, the introduced model was tested using 125 specimens and using paltial least squares (PLS) method. The findings show a positive and significant effect of email marketing and network marketing, content marketing on the dimensions of hotel performance (from all 4 financial perspectives, customer, internal and ethical processes). But the impact of viral marketing on performance was not confirmed. This research clarifies for managers the fact that by investing in digital marketing strategies, they not only do no harm, but also achieve a competitive advantage by improving performance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  121 - The role of ethics in the effective marketing process of the home show network
  Seyed Nasser  Kamali Esmail  Hasanpour Ghoroghchi Sirajuddin  Mohebbi
  marketing managers, and marketing consultants in the film and serial industry and were interviewed in depth. This selection and interviewing continued until the theoretical saturation was reached and then stopped. In this study, the snowball sampling method was used and More

  marketing managers, and marketing consultants in the film and serial industry and were interviewed in depth. This selection and interviewing continued until the theoretical saturation was reached and then stopped. In this study, the snowball sampling method was used and this process continued until the researcher reached theoretical saturation. Finally, this method was an interview with 9 experts. In this study, since the data theory theory method was used, the main tool for data collection was in-depth and unstructured interviews with experts. Finally, after open, axial and selective triple coding, the conceptual model of the research was designed based on a paradigm model. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  122 - Membership in social network son the level of identification and social security adolescents
  Abbas Ali  Shahidi
  Social networks are consequence of Internet and ICT applications. The study aimed to investigate the effect of membership in a social network virtual on the level of Identification and sense of security adolescents. The research was descriptive and causal- comparative. More

  Social networks are consequence of Internet and ICT applications. The study aimed to investigate the effect of membership in a social network virtual on the level of Identification and sense of security adolescents. The research was descriptive and causal- comparative. The population was consist of all boys and girls in the city of Arak that selected 200 people with using purposive sampling. To collect data was used Question naira sordid gentrification (ISI)and social security questionnaire .The data were analyzed with T- test and analysis of variance and using SPSS software. Result showed that between levels of identification and social security in the member sandman- members adolescents،there was no significant difference. Based on the findings of this study seem that membership in social networks alone do not explain the levels ofidenti ficationor social security Ado lessens and other factors such as family, school environment and educational environment and other condition straining and develop end processing the adolescents can influence the form at ion fid entity and sense of security of them. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  123 - A dairy Products Green Supply Chain model with Emphasis on Customer Satisfaction: Combining Interpretive Structural Modeling Approach and Analytical Network Process
  Ali  Yousef Mahdi Homayounfar abdolaziz pagheh amir akhavanfar
  The purpose of this study is to present a green supply chain model for dairy products with emphasis on customer satisfaction. The statistical population of the study consists of university professors and experts in the field of marketing and food and dairy industries of More

  The purpose of this study is to present a green supply chain model for dairy products with emphasis on customer satisfaction. The statistical population of the study consists of university professors and experts in the field of marketing and food and dairy industries of Iran who have been selected by purposive sampling. Sampling continued until the theoretical saturation stage. The value of ICC coefficient was also approved to determine the reliability of the measuring instrument. Finally, 10 questionnaires and interviews with experts were used. The interpretive structural modeling approach is used to present the model and the network analysis process is used to prioritize the criteria. Findings showed that the factor of customer satisfaction in relation to the research topic and the presentation of the green supply chain model of dairy products is more effective In contrast, the factors of green innovation have the most, green entrepreneurship, green performance, internal green actions and external green participation have the most impact and the least impact. Also, the results of Mick Mac analysis showed that 7 factors related to the presentation of the green supply chain pattern of dairy products in terms of permeability and dependence are divided into three categories of infiltrators, dependent and autonomous. Finally, the prioritization of criteria showed that customer satisfaction has the highest weight among the criteria of the green supply chain of dairy products Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  124 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
  hadi shakibian Jalaledin Nasiri
  10.52547/jist.19789.10.40.259
  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More

  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  125 - Mathematical Modeling of Flow Control Mechanism in Wireless Network-on-Chip
  Fardad Rad Marzieh Gerami
  10.52547/jist.19923.11.41.48
  Network-on-chip (NoC) is an effective interconnection solution of multicore chips. In recent years, wireless interfaces (WIs) are used in NoCs to reduce the delay and power consumption between long-distance cores. This new communication structure is called wireless netw More

  Network-on-chip (NoC) is an effective interconnection solution of multicore chips. In recent years, wireless interfaces (WIs) are used in NoCs to reduce the delay and power consumption between long-distance cores. This new communication structure is called wireless network-on-chip (WiNoC). Compared to the wired links, demand to use the shared wireless links leads to congestion in WiNoCs. This problem increases the average packet latency as well as the network latency. However, using an efficient control mechanism will have a great impact on the efficiency and performance of the WiNoCs. In this paper, a mathematical modeling-based flow control mechanism in WiNoCs has been investigated. At first, the flow control problem has been modeled as a utility-based optimization problem with the wireless bandwidth capacity constraints and flow rate of processing cores. Next, the initial problem has been transformed into a dual problem without limitations and the best solution of the dual problem is obtained by the gradient projection method. Finally, an iterative algorithm is proposed in a WiNoC to control the flow rate of each core. The simulation results of synthetic traffic patterns show that the proposed algorithm can control and regulate the flow rate of each core with an acceptable convergence. Hence, the network throughput will be significantly improved. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  126 - Open Innovation; A Comprehensive View on Concepts, Approaches, Trends and Key Success Factors
  Mostafa Safdari Ranjbar Manochehr Manteghi Gholamreza Tavakoli
  In the past, the development and commercialization of an innovation process used to depend on intra-organizational think-tank and efforts. Today, however, Open Innovation paradigm calls companies to also tap into outside ideas and technologies and yet mutually allow oth More

  In the past, the development and commercialization of an innovation process used to depend on intra-organizational think-tank and efforts. Today, however, Open Innovation paradigm calls companies to also tap into outside ideas and technologies and yet mutually allow other companies to exploit their innovative ideas. Open Innovation is a hot topic which has outstandingly drawn researchers' and managers' attention from various perspectives in the past few years. Therefore, reviewing on a case study basis, the topic's entire literature that comprises 70 papers, the current research paper aims to cover its concepts, approaches, major trends, and key success factors. To point to a few findings of the research, the paper dramatizes new and emerging perspectives and trends in the realm of Open Innovation, successes like external networking, innovation brokers, technology intelligence, absorption capacity, open business model, and human factors such as culture and motivation. The paper concludes that managers of companies and organizations should exploit Open Innovation as a solution to confront fast changing technological trends, short technology life cycles, high internal R&D costs, and sever global competition. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  127 - The Role of Open Innovation in Gaining Technology Intelligence
  Kamran Feyzi Seyed kamal Tabatabaeiyan Hosein Khosropour
  High speed changes and evolutions in technological environment are among the important subjects in business which has made the ability to plan and decide in this area impossible without a proper perception of the present and future situation of technology. Therefore, de More

  High speed changes and evolutions in technological environment are among the important subjects in business which has made the ability to plan and decide in this area impossible without a proper perception of the present and future situation of technology. Therefore, determination, creation and development of the industrial and research co-workers network using the open innovation and employing the technology intelligence in order to observe the technological evolutions for technology-based organizations and also the technology and science progress that has a direct effect on the business area of these organizations are very important. On the other hand, since the increasing growth of the internet in increasing the input resources for technology intelligence has a key effect, the appropriate employment and use of information technology tools for accessing and analyzing these inputs has also been changed into a key point in technology intelligence creation. Thus, the organization`s strategy in determination and acquisition of the appropriate process of technology intelligence has become very important so that organization requires to merger the innovation and idea from out of the organization with main advantages from inside the organization. As a result, establishing a systematic framework, the organizations, creating a connection between the technology intelligence and open innovation, can help their purpose and increase the technology intelligence value. In other words, technology intelligence as an open innovation approach tool creates a relation between knowledge and idea from out of the organization and main advantages from inside the organization results in creating a competitive advantage for the organization. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  128 - The Role and Status of Upgrading Techniques in Global Value Chains
  Zahra Ayagh Mahsa Farkhondeh Esmaeil Malek Akhlagh
  In the current era, with joining developing country to the global markets, companies in these countries are facing with competition pressure. Studies show that the best way for producing qualified products is moving toward skillful and smart activities. Since modern int More

  In the current era, with joining developing country to the global markets, companies in these countries are facing with competition pressure. Studies show that the best way for producing qualified products is moving toward skillful and smart activities. Since modern international commerce has provided the possibility of purchasing wide range of products, production and consumption are both taking place within far geographical distances. Therefore, in order to permanently exist in dynamic world markets, companies and industries do not have other chances except implementing upgraded techniques along the production process. Value chain analysis has important role in perception of effective implementation of such technique to successfully protect companies in global economy, because in global value chain framework, company can enhance added value and gain competitive advantages by using appropriate techniques through operations and production process improvement. The purpose of this study is the explanation of the role and status of upgrading techniques in global value chain. So at first the definition of upgrading concepts and existence technique is presented, then the role of upgrading technique in global value chain management is investigated through expressing global value chain basics and its models. Studies indicate that, product and process upgrading in hierarchical and quasi-hierarchical global value chain, and functional upgrading technique in market-driven global value chain model, act more efficiently; whereas in the chain of network–based relationship, producers and consumers use their competency and competitiveness complementarily for upgrading and innovation. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  129 - Managing the Stakeholders Network in Technology Business Incubators
  Hasan KIhakbaz Jafar Eyvazzpour
  The Technology Business Incubators (TBIs) provide an effective means to link technology, capital and know-how in order to accelerate the development of new technology-based companies, leverage entrepreneurial talent, and thus speed the exploitation of technology. The TB More

  The Technology Business Incubators (TBIs) provide an effective means to link technology, capital and know-how in order to accelerate the development of new technology-based companies, leverage entrepreneurial talent, and thus speed the exploitation of technology. The TBIs assist emerging businesses through an array of business support services, developed by incubator management such as assistance in developing business and marketing plans, building management teams, obtaining capital, and ease of access to a range of other more specialized professional services. In addition, incubators provide flexible space, shared equipment, and administrative services. Therefore, the TBI goal is to produce successful firms that will leave the incubator financially viable and freestanding. To address this goal, involvement and support of stakeholders, consisting of sponsors drawn from the local business community, government, the broader community, venture capital providers, technopreneurs, incubator managers and staff are vital. The stakeholders in an incubator will help sustain its financing, offer assistance to its client companies, serve on governing boards, and play generally a key role in the incubator's success. The purpose of this paper is to explain the reason for involving stakeholders in TBI management and necessity of establishing a healthy relationship with them, based on an appropriate set of expectations regarding the nature of the relationship. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  130 - Perspectives of Innovation Management In Networks
  Fatemeh Abdi
  Networks of collaborative relationships among firms are an important form of organization with innovative activities. Especially in innovative and technology intensive industries, firms increasingly realize that, in order to tap into new technologies and know- how, inte More

  Networks of collaborative relationships among firms are an important form of organization with innovative activities. Especially in innovative and technology intensive industries, firms increasingly realize that, in order to tap into new technologies and know- how, internal development needs to be complemented with strategic collaborations. Strategic networks potentially: - Provide firms with (a) access to information, resources, markets, and technologies, and (b) advantages from learning, scale, and scope economies; - Allow firms to achieve strategic objectives such as sharing risks and outsourcing value-chain stages and organizational functions. On the other hand, innovation which is the corner stone for any company to be sustainable is often identified as a key to inter-companies competitiveness and is promoted through various policy initiatives in many countries. Today, innovation is no longer carried out within individual companies, but often crosses borders in the form of innovation networks. But according to Damanpour, current developed theories which have been studied and suggested during three last decades, dont sufficiently clear and explain the innovative processes and suitable and required conditions for them to become successful. The changed operation settings in companies require a rethinking of appropriate approaches, that is the scope of this paper. In spite of great efforts of companies in managing innovation and connecting and communicating with supplier, partners and research institutions to do this management more properly, information and statistic data show that suitable and coherent approaches have not yet been established. Hence this paper addresses the existing perspectives for managing innovations in networks and their value for practitioners. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  131 - A Pyramid Model for Networking of knowledge-based companies
  Ehsan Golshiri
  In this article discussion how is to create the Cooperative network among knowledge-based companies to get best rivalry. With a pyramid model describes the Cooperative Network relation such as management, technology and economical conditions. Each company or proficiency More

  In this article discussion how is to create the Cooperative network among knowledge-based companies to get best rivalry. With a pyramid model describes the Cooperative Network relation such as management, technology and economical conditions. Each company or proficiency set on one face of pyramid, and relation in network simulated with pyramids sides. This model has some profits in work distribution and covers all of the dimensions of the project. We used principles of geometric and simulation for pyramid model. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  132 - Identify and Clustering Challenges of knowledge-based Enterprises using ANN and BPMS Approaches; Case study: Yazd KBEs
  Mojtaba GholiPour Mohammad Ali Vahdat Zad Mohammad Saleh Oliua Hasan Khademi Zareu
  10.7508/jstpi.2015.04.006
  Knowledge always is a powerful tool in stabilizing position of individual/community service to the public and excellence approach in current autonomous communities. Value of knowledge has been more necessary if it capable for transfer to the High-Tec and needed Technolo More

  Knowledge always is a powerful tool in stabilizing position of individual/community service to the public and excellence approach in current autonomous communities. Value of knowledge has been more necessary if it capable for transfer to the High-Tec and needed Technologies of humanity societies. Knowledge Based Enterprise (KBE) is a real-law enterprise such as factory that transfer Knowledge to production/services. However KBEs are causing for sustainable knowledge economy and development native knowledge in more countries, but these enterprises havnt optimize occasion in view of quantity, production quality and service extensive according to the 20 years growth view of Iran. Purpose of this study is to identifying encounter challenges of KBEs that located on Yazds Science and Technology Park (STP) and clustering these challenges with ANN method exactly. The Samples contains 137 person such as manager and top employees of these enterprises. Number of reached challenges have been 59 that were attained from literature and experts guidelines were designed and distributed between samples suddenly. According to the PB artificial neural network, reliabilities of samples were confirmed with MSE=2.0332 and priority done with Multilayer Perceptron (MLP) artificial neural network and with inspiration of Business Process Management System (BPMS) approach. According to the BPMS approach and MLP method, Result show that challenges did cluster in three factions known as: management activities, operational activities and support activities. Thus, number of management, operational and support activities in order were 27, 15 and 17 items exactly. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  133 - Designing Model For Prioritize And Selection Projects Portfolio in a Joint Stock Company Based on Analytic Network Process (ANP)
  کبری يزدانی Hossein Ali  Hassan pour
  10.7508/jstpi.2018.01.006
  Established large companies always need a lot of costs that are not met, therefore, the creation of joint stock companies is an effective method for doing business. In addition, changes in the business environment makes companies to move forward and requires that they b More

  Established large companies always need a lot of costs that are not met, therefore, the creation of joint stock companies is an effective method for doing business. In addition, changes in the business environment makes companies to move forward and requires that they become associated with these changes. This requires an effective strategy and considering the fact that many of the strategies are not entered into the implementation phase, portfolio management as a new model of management to implement strategies to help companies comes to mind. In this paper, using literature review and study framework and using interviews and questionnaires, the model is designed for a Joint Stock Company. This includes : strategic plan portfolio, defined portfolio, strategic change management, network structure portfolio, prioritize and select projects based on ANP, measuring earned value project portfolio, collection and delivery information stakeholders confirmed the basket by stakeholders, ratification and implementation of the project portfolio, portfolio monitoring and feedback ". The validity and reliability of the proposed model by collecting 14 questionnaires and Cronbach's alpha coefficient formula and the carcass is proved. In this paper, a field study is conducted in a Joint Stock Company. The criteria necessary for prioritizing and selecting projects that include criteria of economic profit and human resources as well as criteria related to social aspects were extracted and using network analysis process, the selected projects were prioritized based on criteria. The reliability and validity research using Cronbach's alpha formula and carcass were examined. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  134 - Identification and Priorities KBEs Challenges using ANN Method (Case study: Yazds KBEs)
  Mojtaba GholiPour Mohammad Ali Vahdat Zad Mohammad Saleh Oliua Hasan Khademi Zareu
  10.7508/jstpi.2016.01.003
  The role of knowledge and science are paid attention in national development because creating expertise and improving the Total factor productivity of production. Furthermore, the position of technology and knowledge is considered vital as endogenous factors in this dev More

  The role of knowledge and science are paid attention in national development because creating expertise and improving the Total factor productivity of production. Furthermore, the position of technology and knowledge is considered vital as endogenous factors in this development process. On the other hand, the conversion of knowledge into product and service will meet the needs of different communities. Knowledge-based companies are included as institutions that work as factories converting knowledge into product and service. Although, knowledge-based companies create sustainable knowledge-based economy and development of indigenous knowledge in many countries, but in accordance with the country's 20-year outlook and in terms of quality and quantity, they are not in ideal situation and are faced with challenges in their own development process. This research aims at identifying challenges facing knowledge-based companies located in Yazd's Science and Technology Park of Eghbal and prioritizing these challenges using Artificial Neural Network. The case study includes 137 managers and senior staff of these companies. Fifty nine challenges obtained from related literatures and experts’ opinions have been compiled in a questionnare and distributed among target population. One hundred and twenty eight collected questionnairs were validated by BP Artificial Neural Network and confirmed with MSE=2.0332 and challenges were prioritized with Artificial Neural Networks Radial Basis Function. The research proved that 19 of 59 challenges were priority among which economic problems of the country, industry and domestic markets recession and government’s unbalanced support policies can be pointed out. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  135 - The Role of CrowdFunding to Financing Capital for Startups Entrepreneur
  Sajad Asakere Saeed Zarandi Mohsen Afsharpour
  10.7508/jstpi.2015.03.001
  Financing capital for startups, always had been propounded as one of the challenges for entrepreneurs. On the other hand, the risk of venture capital investment, financial institutions and banks often seem less willing to invest in this activity. By developing Internet More

  Financing capital for startups, always had been propounded as one of the challenges for entrepreneurs. On the other hand, the risk of venture capital investment, financial institutions and banks often seem less willing to invest in this activity. By developing Internet and social networks, limitation in communications have been removed and new concepts such as networking and utilization of crowdfunding has been raised. This paper introduces a new method of collective investment fund over the Internet to exploit social capital rather than bank loans, business angels and venture capital. Due to the lack of scientific research in this field, by using inductive method we discuss about the crowdfunding phenomenon concept, development of literature in this field, goals and motivations of the participants, expressing the models, potential, obstacles and limitations of the crowdfunfing. The results indicate that the rate of this concept in the world especially in developing countries has been increasing and this model can be operated by providing cultural, technological, legal and social contexts. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  136 - Identify the Technological Risks of the New Product Development Process and Prioritize by the Analytical Network Process (ANP)
  Abolfazl mirzaramazani Sayed Mahdi Golestan Hashemi Seyed Mohammad Massoud naserian
  10.7508/jstpi.2018.01.003
  Nowadays, Organizational survival depends on the tendency toward new products and the use of new ways to create them. Developing new product is a process through which a new product or a service is offered to the customers. More precisely, the new product development pr More

  Nowadays, Organizational survival depends on the tendency toward new products and the use of new ways to create them. Developing new product is a process through which a new product or a service is offered to the customers. More precisely, the new product development process involves using resources and capabilities to create a new product or improve an existing product. Given the shorter life cycle of products and technology developments, new product development process in the growth, survival, and competitiveness of the utmost importance. There are several risks during new product development process; that in general, are divided into three major categories: technological risks, organizational risks, and marketing risks. According to studies, the technological risks are one of the most important risks. Perceived technological risk refers to a firm’s inability to completely understand or accurately predict some aspects of the technological environment as it relates to NPD projects. Hence identifying and reducing these risks helps to increase the success rate of the new product development process. In this research, by reviewing the literature on the areas of the new product development process, risk and risk management, 20 types of common technological risks in new product development process are extracted from the literature and using a number of experts from Malek Ashtar University of Isfahan who are specialized in this field, they are prioritized by the Analytical Network Process (ANP). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  137 - New Applications of Vehicular Ad-Hoc Network Technology in the Railway Industry: New Platforms for the Emergence of Knowledge based Companies in the Rail Transport Industry
  Ahmad Reza Jafarian-Moghaddam
  10.7508/roshdefanavari.2018.14.008
  Advances in technology have provided Intelligent Transport Systems (ITS) for urban managers as a comprehensive and practical solution in order to overcome transportation problems. One of the most important ITS subsystems, which plays a major role in controlling traffic More

  Advances in technology have provided Intelligent Transport Systems (ITS) for urban managers as a comprehensive and practical solution in order to overcome transportation problems. One of the most important ITS subsystems, which plays a major role in controlling traffic and accidents, is the Vehicular Ad-Hoc Network (VANET) technology. VANET, which aims to provide security and comfort for passengers, is a network of cars in which cars are interconnected as network nodes using wireless technologies. In this paper, introducing the VANET network technology, its new applications in the railway industry have been discussed and Rolling stock Ad-Hoc Network (RANET) has been proposed. In RANET, the rolling stocks are network nodes and have a wireless and dynamic connection with each other. Increasing revenue, reducing costs and accidents, increasing efficiency, increasing cargo owners and passengers’ satisfaction are the most important goals of the railway industry, which RANET technology can play a very significant role in achieving these goals and providing the basis for railway transport without driver. RANET can also provide platforms for the emergence of knowledge based companies in the field of modern and technology-oriented rail transport and can be a source of innovation and creativity. The results of the paper show that knowledge based companies will be able to use RANET technology in areas such as train planning and scheduling, dynamic blocking, multimodal transport planning, intersection and switches management. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  138 - Systematic Literature Review of Crowdfunding
  Ali  Haji Gholam Saryazdi Ali Rajabzadeh Ghatari Alinaghi Mashayekhi Alireza hassanzadeh
  10.7508/jstpi.2019.01.005
  In recent years crowdfunding is created as a web 2.0 and social network -based on financing for business startups and creative projects. This method has grown in many aspects, such as the number of platforms, the number of campaigns and their success rate, the volume of More

  In recent years crowdfunding is created as a web 2.0 and social network -based on financing for business startups and creative projects. This method has grown in many aspects, such as the number of platforms, the number of campaigns and their success rate, the volume of funding and the number of models offered. This growth has led researchers to investigate this phenomenon of the different dimensions and aspects. So this article is followed by a comprehensive review of the literature associated with this method. In this study, we have done a systematic review of 60 published articles until 2017 about the concept of crowdfunding. The research findings have been summarized. Moreover, the research orientation pattern has been determined by identifying the focus of great attention. Also the existing research gaps and suggestions for future research in the field of crowdfunding identified and explained. The results of the survey showed that the number of studies carried out on this new method are not very high but are increasing. It is necessary for the researchers to study this method (phenomenon) with a comprehensive and holism approach, using a review of the views of its various actors, stackholders and costumers and extracting the factors influencing the success and failure of this method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  139 - Multi-period Multi-level Supply Chain Network Design in Agile Manufacturing with Tabu Search Algorithm
  elahe salari mohammadreza shahraki abdollah sharifi
  10.7508/jstpi.2019.02.003
  Supply chain network design includes key decisions that have a major impact on the supply chain operational structure. Efficient supply chain design improves performance in organizations. This has led to the emergence of new concepts in the supply chain issue in the pas More

  Supply chain network design includes key decisions that have a major impact on the supply chain operational structure. Efficient supply chain design improves performance in organizations. This has led to the emergence of new concepts in the supply chain issue in the past decade. In this study, the supply chain network design problem in agile organizations has been taken into account with multi-level and multi-period. This problem is considered under conditions of having multiple customers with a high demand volume. The decisions include the selection of companies at each level, the amount of production, storage and transportation of each company. The problem has been modeled to integrate all decision variables with the goal of minimizing overall operating costs across the entire supply chain and Satisfaction of customers' complete demand and Satisfaction with them. Since multi-period multi-level supply chain design problem solving is one of the NP-Hard issues in uncertainty conditions, it is better to use innovative and meta-algorithms to reduce problem solving time. For this reason, the algorithm for banning search algorithms, which is one of the meta-algorithms, has been used to solve the model. The results of this research show that as the number of problem-solving repetitions increases, answers with less than 3% of the difference between the optimal answer are achieved. The search algorithm is forbidden to get the optimal response compared to the Lagrange algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  140 - Requirment of Native Inclusive Social Networks Development
  Sahar Kousari ali  sharifi Alireza Yari
  10.52547/jstpi.20675.15.59.44
  Social networks with more than a decade of experience in providing services and attracting countless user globally have reached a significant economic profitability. Entrance into the social networking market requires creating an innovative, entrepreneurial and secure e More

  Social networks with more than a decade of experience in providing services and attracting countless user globally have reached a significant economic profitability. Entrance into the social networking market requires creating an innovative, entrepreneurial and secure environment for business. From the viewpoint of native social networking, in addition to the above requirements, this requires financial and infrastructures support as well as a comprehensive strategy. In Iran, along with the global trend of technology, numerous communication and social networks have been launched, which in most cases, their experience has not been successful. In this paper, by studying successful experiences in this field, the existential and obligatory requirements of the coverage of these networks in the country are determined and successful factors of the social network identified. Finally, based on the results obtained and modeling the successful factors of social networks from the perspective of business and users, the vast challenges of native social networks have been extracted and policies and solutions proposed for each of challenges. Attention to the needs of young people, using existing on-line social network analysis, transparency of related lows and regulations, providing incentive and infrastructure supports, considering criteria and cultural and social norms, innovating in business models and creating free-to-access; are suggested strategies for developing native social networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  141 - Identifying and Prioritizing the Influencing Factors on the Development of Incubators of University Technology Units (Case Study: Incubator of Technology Units in Guilan University)
  Roozbeh Habibi Zahra Asghari Kamran Jafari
  10.7508/jstpi.2018.04.001
  Today, incubators play a very important role in the economic and industrial development of developed and developing countries. Incubators are designed to support educated entrepreneurs and provide the growth opportunities for new companies by providing them general faci More

  Today, incubators play a very important role in the economic and industrial development of developed and developing countries. Incubators are designed to support educated entrepreneurs and provide the growth opportunities for new companies by providing them general facilities. Identifying and studying the various factors that are affecting the development of entrepreneurship and incubators can be effective in policy making for the promotion of entrepreneurial activities. The present study sought to identify the factors affecting the development of incubators, identifying 36 factors and categorizing them in six categories containing company general characteristics, manpower characteristics, infrastructures, market and competition, financial affairs and environmental factors. The statistical population of the study consists of 6 experts from the incubator of Technology Units of the University of Guilan. Prioritizing 36 factors using the analytic network process methodology indicates that five factors: supporting mechanisms (government and university), the existence of experienced consulting teams, organizational structure of companies based in incubators, the field of activity of these companies and their age have the highest priorities than other factors, in other words, they have the greatest impact on the development of incubators. Of course, given that the prioritization of these factors has been used by experts in the incubator of Technology Units of the University of Guilan, it can not be fully generalized to other Centers. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  142 - Evaluation of the Efficacy of the Combined Viral Marketing Method with the Network Clustering Method and Comparing the Results
  fereydoun ohadi mehrnoosh mohammadi Mohammad Jafar Tarokh
  10.7508/jstpi.2019.02.008
  In a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketi More

  In a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketing methods and models is of great importance. One of the newest marketing methods is viral marketing that is based on mouth-to-mouth advertising and has a lot of power. Viral marketing relies on the principle that on any social network, a number of users have high power and influence on others, and by identifying them and creating good advertising messages, They can be used to effectively marketing. Therefore, The identification of important users is considered the most important activity in viral marketing. In this regard, various studies have been conducted to identify users using a variety of graph-based and publish-based methods. In this research, the capabilities of both methods have been used and by Using a semi-localized centrality criterion based on graph-based methods and Markov clustering model based on propagation methods, a new hybrid model for user clustering and identification of key users presented. The results show higher correlation between the proposed method and the SIR standard and, therefore, its higher efficiency than other methods used in the research. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  143 - Smart Farming Based on Internet of Things
  GholamReza  Farrokhi Mahboobeh Gapeleh
  10.52547/jstpi.20750.15.59.29
  Nowadays, with the increasing population of the world and the need for food on the one hand and the scarcity of water, energy and arable land on the other hand, traditional agriculture is no longer responsive to the food demand of the world population, so smart farming More

  Nowadays, with the increasing population of the world and the need for food on the one hand and the scarcity of water, energy and arable land on the other hand, traditional agriculture is no longer responsive to the food demand of the world population, so smart farming has become more and more popular. The IoT is a new technology that is capable of providing many solutions for modernizing agriculture. IoT provides reliable information on the seed to be sown, the seed rate, the best time to plant and harvest, as well as the prediction of the amount of crop to be harvested. In this article, by studying research and research through library data collection and the study of scientific resources in the field of IoT, as well as the infrastructure required for deployment, we discuss the potentials and capabilities of this technology In the field of agriculture. The present study reviews the benefits of using IoT in smart agriculture such as increasing crop yields due to proper planning of seed planting time, crop harvesting, water use optimization. Planning to apply appropriate irrigation methods according to the amount of water required for each plant and each climatic zone, identifying pests and plant diseases at the real time, and alerting the farmer to the necessary decisions and actions to be taken. Pest utilization of information obtained from sensors established in the field is another benefit that has been addressed in this study. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  144 - The Effects of Entrepreneur’s Characteristic on Entrepreneurial Marketing (Case Study: Fars Science and Technology Park)
  mohammad javad naeiji یدالهی yadollahi
  10.61186/jstpi.20953.19.76.30
  Despite entrepreneurial marketing being cited as one of the most important requirements for success of SMEs and startups, literature reveals few academic studies dealing with antecedents of entrepreneurial marketing. To contribute to the research gap, the purpose of thi More

  Despite entrepreneurial marketing being cited as one of the most important requirements for success of SMEs and startups, literature reveals few academic studies dealing with antecedents of entrepreneurial marketing. To contribute to the research gap, the purpose of this paper is to investigate the effect of entrepreneur’s characteristic on entrepreneurial marketing with considering the mediator role of network structure and communication. The study is descriptive-correlation and in terms of purpose is an applied research. The statistical population of this study is companies in Fars Science and Technology Park. Data are collected from 110 companies and are analyzed by using structural equation model (SEM). The results show that entrepreneur characteristics not positively direct influence entrepreneurial marketing. However, findings support the indirect effect of entrepreneur characteristics through mediating path of network structure and communication. In other words, if entrepreneur characteristics facilitate networks and communication, they will improve entrepreneurial marketing. SMEs and knowledge based companies that interested in improving entrepreneurial marketing practices should enhance individual characteristics that can be help developing network structure and communication. The study contributes to the existing research about the role the entrepreneur can have in the firm’s ability to develop entrepreneurial marketing activities. Also, these results could be used for understanding entrepreneurial marketing dynamics and structure based on the interactive relations between antecedents to improve entrepreneurial marketing. From a practical viewpoint, the study has found that entrepreneurial marketing is based on networking and communicating. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  145 - Presenting a new framework of viral marketing in online business: qualitative analysis via projection techniques
  Elham Fazeli veisari Mohammad Javad Taghipouryan Reza Tavoli
  10.52547/jstpi.20994.17.68.23
  Abstract: Viral marketing is a marketing policy that motivates people to tell your marketing message to others. The advantage of viral marketing is that marketers can create customers at costs almost equivalent to zero, and move from the marketing-consumer-to-consumer-t More

  Abstract: Viral marketing is a marketing policy that motivates people to tell your marketing message to others. The advantage of viral marketing is that marketers can create customers at costs almost equivalent to zero, and move from the marketing-consumer-to-consumer-to-consumer mode, accordingly, a new economy has emerged as businesses use information technology. The purpose of the present study is to conceptualize the components of viral marketing in online business. For this purpose of word association, sentence completion and dream exercises Used as projection techniques in an in-depth semi-structured interview with 15 people in the three generations (X, Y and Z) through content analysis and with the help of MaxQDA software, 76 open source, 21 core and 6 new viral components have been identified in the field of viral marketing such as online services, online attractiveness, online risk taking, online persuasion, online trust and online support. The results can help online business owners develop low-cost activities Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  146 - Dynamic Tree- Based Routing: Applied in Wireless Sensor Network and IOT
  Mehdi Khazaei
  10.52547/jist.22504.10.39.191
  The Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some app More

  The Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some applications, the IOT is converted into WSN with the same descriptions and limitations. Working with WSN is limited to energy, memory and computational ability of the sensor nodes. This makes the energy consumption to be wise if protection of network reliability is sought. The newly developed and effective hierarchical and clustering techniques are to overcome these limitations. The method proposed in this article, regarding energy consumption reduction is tree-based hierarchical technique, used clustering based on dynamic structure. In this method, the location-based and time-based properties of the sensor nodes are applied leading to provision of a greedy method as to form the subtree leaves. The rest of the tree structure up to the root, would be formed by applying the centrality concept in the network theory by the base station. The simulation reveals that the scalability and fairness parameter in energy consumption compare to the similar method has improved, thus, prolonged network lifetime and reliability. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  147 - Evaluating of Economic Sustainability Indicators in District 15 of Tehran(
  batoul hoseinpour hoshang zahiri  
  Currently, the spatial distribution of services and economic indicators is one of the problems of urban management in responding to citizens. the aim of this project is to evaluate and analyze the levels of neighborhoods in District 15 in terms of having economic indica More

  Currently, the spatial distribution of services and economic indicators is one of the problems of urban management in responding to citizens. the aim of this project is to evaluate and analyze the levels of neighborhoods in District 15 in terms of having economic indicators and determining the ranking of neighborhoods.The present study is in the field of applied research in terms of purpose and in terms of data collection in the field of survey research. In the present study, a semi-structured questionnaire and interview were used to collect data.The ANP network analysis method was used for data analysis and the Typsis method was used for ranking the neighborhoods.The findings indicate that among the economic indicators, the ability to pay living expenses with a weight of 0.154 ranks first, the amount of monthly savings with a weight of 0.153 ranks second and the ability to provide housing with a weight of 0.131 ranks third. It has gained economic stability among the indicators.The Taipei Method ranked the 15th District and found that Abuzar District 2 ranked first, South Afsaria District 5 ranked second, and District 4 Moshiria ranked third, according to economic criteria.Dimensions of Economic Sustainability Considering that economic development is the process of total growth on income, this is the first step and shows that economy is the main key for developing.Economic policies should be such that a kind of balance and coherence is established in different parts of Tehran.Therefore, the three main components of economic infrastructure, economic activity and economic facilities have been selected. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  148 - Sociological analysis of the effect of cyberspace on students' academic achievement
  sayyed atollah sinaee sara mousavi mashhadi
  The growing use of digital technologies has made cyberspace and social networks an important part of people's daily lives. In addition, digital technologies and cyberspace have revolutionized the tools, methods, and content of learning in education systems. At the same More

  The growing use of digital technologies has made cyberspace and social networks an important part of people's daily lives. In addition, digital technologies and cyberspace have revolutionized the tools, methods, and content of learning in education systems. At the same time, the study of the dimensions and effects of the widespread use of communication and information technologies has been the focus of researchers from the beginning and the present study pursues such a goal. This research was conducted by correlation research method and a questionnaire. The statistical population includes all female students in the first year of high school, district two of education in Mashhad, in the academic year 1300-1400. The sample size was selected based on Morgan formula 380 people and information was collected using available sampling method. Findings of this study show that the motivation for the development of the user group in cyberspace and social networks was higher than the average motivation for the development of the non-user group. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  149 - Investigating the structure and evolution of the innovation network of catching-up countries in the field of solar energy using patent citations analysis
  Mahboubeh Nourizadeh Tahereh Saheb Shaghayegh Sahraee Ali Maleki
  10.52547/jstpi.24265.18.72.52
  Over the past three decades, research has focused on how and under what conditions latecomers have been able to become major producers of new technologies, such as renewable energy, and make technological catch-up in this area. This is emphasized for developing countrie More

  Over the past three decades, research has focused on how and under what conditions latecomers have been able to become major producers of new technologies, such as renewable energy, and make technological catch-up in this area. This is emphasized for developing countries because, on the one hand, the greatest growth in energy demand in the coming years will be in these countries, and on the other hand, it is a green window of opportunity for other developing countries to learn and work towards catching-up in these areas. In this research, from the perspective of global innovation networks, the technological approach was investigated by analyzing the structure and evolution of the solar energy innovation network using patent citation analysis. For this purpose, patents in this field were extracted and cleared from the Derwent Innovation over a period 1980-2017. Then, a citation network was formed and the topological structure and country-level indicator were examined using social network analysis methods. The results showed that countries that were able to gradually increase their absorption capacity by joining the global innovation network and moving in the path of technology of leading countries by increasing citations to their patents, could able to be producers of knowledge and granted high quality patents which have been increasingly cited by other countries. These countries found a special place in the network as intermediaries for the diffusion of technology and succeeded in technological catch-up. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  150 - Assessment Model for Implementing a Lean Transformation in Enterprise Based on the Fuzzy Anp, Fuzzy Dematel and Fuzzy Vikor
  Kazem Nasiri Kashani محمود  مدیری gholamreza hashemzade khorasegani
  A lot of methodologies for evaluation of the lean transformation have been proposed in the literature, but just a few of them explicit relations among the components of the lean enterprise transformation, with focusing on the priority and identification of the relative More

  A lot of methodologies for evaluation of the lean transformation have been proposed in the literature, but just a few of them explicit relations among the components of the lean enterprise transformation, with focusing on the priority and identification of the relative weight of each element on the lean transformation have been considered. Therefore, we try to quantify those relations in this study. So, to structural investigate the relationships among the elements, a model is proposed that presents a novel comprehensive insight to the lean transformation process. This study proposed an analytic modeling approach in the lean enterprise transformation to identify the weights of the elements. The goal is to operationalize the highly qualitative relationships among the lean transformation components. In this paper 32 effective factors in lean enterprise transformation, identified as "sub sub criterion" were grouped into eight categories classified as "sub criterion". These eight sub criteria classified through 3 criteria in the higher level which are “organizational asset development”, “quick adaptation to markets and competitive superiority acquisition” and “supply and distribution network optimization These three criteria were proposed as the major criteria involved in "delivery value maximizing", and ultimately, the value maximizing recognized as the main target of the lean transformation in companies. Fuzzy DEMATEL technique and fuzzy analytic network process modeling are used to determine the relationships and the relative weights of each component and then with fuzzy VIKOR method, 25 identified solutions related to conceptual model were ranked for implementing the lean transformation which regard the organizational purposes and the weights of the end level parameters of the conceptual model in a proposal framework. The present evaluating lean enterprise transformation is a comprehensive model that can be applied to different production industries. This model also was investigated in automative parts industry. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  151 - Assessment Model for Implementing a Lean Transformation in Enterprise Based on the Fuzzy Anp, Fuzzy Dematel and Fuzzy Vikor
  Kazem Nasiri Kashani محمود  مدیری gholamreza hashemzade khorasegani
  A lot of methodologies for evaluation of the lean transformation have been proposed in the literature, but just a few of them explicit relations among the components of the lean enterprise transformation, with focusing on the priority and identification of the relative More

  A lot of methodologies for evaluation of the lean transformation have been proposed in the literature, but just a few of them explicit relations among the components of the lean enterprise transformation, with focusing on the priority and identification of the relative weight of each element on the lean transformation have been considered. Therefore, we try to quantify those relations in this study. So, to structural investigate the relationships among the elements, a model is proposed that presents a novel comprehensive insight to the lean transformation process. This study proposed an analytic modeling approach in the lean enterprise transformation to identify the weights of the elements. The goal is to operationalize the highly qualitative relationships among the lean transformation components. In this paper 32 effective factors in lean enterprise transformation, identified as "sub sub criterion" were grouped into eight categories classified as "sub criterion". These eight sub criteria classified through 3 criteria in the higher level which are “organizational asset development”, “quick adaptation to markets and competitive superiority acquisition” and “supply and distribution network optimization These three criteria were proposed as the major criteria involved in "delivery value maximizing", and ultimately, the value maximizing recognized as the main target of the lean transformation in companies. Fuzzy DEMATEL technique and fuzzy analytic network process modeling are used to determine the relationships and the relative weights of each component and then with fuzzy VIKOR method, 25 identified solutions related to conceptual model were ranked for implementing the lean transformation which regard the organizational purposes and the weights of the end level parameters of the conceptual model in a proposal framework. The present evaluating lean enterprise transformation is a comprehensive model that can be applied to different production industries. This model also was investigated in automative parts industry. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  152 - The business model designed for a network of partner organizations (collaborative network organization
    Alireza Aliahmadi
  Abstract The development of networked organizations usually done by a focal firm (the mother). For this purpose focal firm in addition to defining the strategy of the partner firms and the nature and mechanisms of the network must develop a business model strategy to b More

  Abstract The development of networked organizations usually done by a focal firm (the mother). For this purpose focal firm in addition to defining the strategy of the partner firms and the nature and mechanisms of the network must develop a business model strategy to be implemented at the network. Significance of business model in a network is important as many experts as Zott and Amit realize business model as an engine for network base strategy. In the present study, we look at the issue of collaboration networks business model designed , to reach the goal of this study we consider focal firm as a symbol of collaborative network then we design a business model for focal firm. In this regard, after the literature review, the model presented by Osterwalder (2004) (BMO) was chosen as the Meta model, after that we adding several factors extracted from the literature on design requirements and the formation of networks. Then the model in Delphi process was judged and developed by experts. After confirming the final model by experts to further validate we design questionnaires that given to Academic experts and managers who working in two network, the result of that questionnaires approved the applicability of the model Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  153 - Develop a business model with a focus on central enterprise in a collaboration network
   
  The development of networked organizations usually done by a focal firm (the mother). For this purpose, the focal firm as the main center of network must implement her strategies with an action plan. Significance of business model in a network is important as many exper More

  The development of networked organizations usually done by a focal firm (the mother). For this purpose, the focal firm as the main center of network must implement her strategies with an action plan. Significance of business model in a network is important as many experts realize business model as an engine for network base strategy. In the present study, we look at the subject of collaboration networks , to review, identify and prioritize the role of focal firm in the business model of collaboration networks. In this direction, by reviewing existing literature on maternal advantage we extract the key functions of focal firm in her mothering strategy and business model. Then the extracted factors in Delphi process was judged and developed by experts. After confirming the final factors by experts to further validation and extract the Priority of factors we design questionnaires that given to Academic experts and managers who working in two network, the result of that questionnaires approved the applicability of the model Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  154 - An Integrated DEMATEL and Fuzzy ANP Techniques Based Assessment Model For Intrapreneurship with Approach EFQM
  Kazem Nasiri Kashani Fazlolah Jamalou Leila Kiani
  In today's world, quick changes have varied the competition and also have faced organizational stakeholders with world’s challenges. In this turbulence environment, Intrapreneurship is functionality that can save today's systems as a new phenomenon in the economy at thi More

  In today's world, quick changes have varied the competition and also have faced organizational stakeholders with world’s challenges. In this turbulence environment, Intrapreneurship is functionality that can save today's systems as a new phenomenon in the economy at this critical period and play a more active role in economic development. Today, business excellence models also have an important role to deploy the key factors of organizational success and make performance improvements. These models provide the assessment criteria in both enablers and results and make guidelines for organizations to measure their progress and performance in the field of quality and organizational excellence. So it allows creating the needed infrastructure and capabilities for Intrapreneurship based on the EFQM Excellence Model. Therefore, after identifying the effective factors of implementation Intrapreneurship, these features are classified due to their nature at four main criteria include "leadership", "Policy and Strategy", "employees" and "partnerships, resources and processes" that all of them are the enablers of the EFQM model. This paper presents an analytical modeling method that makes practicable a qualitative relationship between the elements of Intrapreneurship. We used the integrated approach of fuzzy Dematel and fuzzy modeling network analysis to determine the relationship and weight relative importance of each element in model. The results indicated “leadership” at the main criteria level has the most highly effect and “employees” has the most important coefficient. Also "senior management support" has the highest importance coefficient and highly effect in the sub-criteria. In this study we presented individual entrepreneurs and Intrapreneurship assessment model that is a comprehensive model and can be used in different organizations and finally we evaluated this model in Iran Power Development Company. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  155 - An Integrated DEMATEL and Fuzzy ANP Techniques Based Assessment Model For Intrapreneurship with Approach EFQM
   
  In today's world, quick changes have varied the competition and also have faced organizational stakeholders with world’s challenges. In this turbulence environment, Intrapreneurship is functionality that can save today's systems as a new phenomenon in the economy at thi More

  In today's world, quick changes have varied the competition and also have faced organizational stakeholders with world’s challenges. In this turbulence environment, Intrapreneurship is functionality that can save today's systems as a new phenomenon in the economy at this critical period and play a more active role in economic development. Today, business excellence models also have an important role to deploy the key factors of organizational success and make performance improvements. These models provide the assessment criteria in both enablers and results and make guidelines for organizations to measure their progress and performance in the field of quality and organizational excellence. So it allows creating the needed infrastructure and capabilities for Intrapreneurship based on the EFQM Excellence Model. Therefore, after identifying the effective factors of implementation Intrapreneurship, these features are classified due to their nature at four main criteria include "leadership", "Policy and Strategy", "employees" and "partnerships, resources and processes" that all of them are the enablers of the EFQM model. This paper presents an analytical modeling method that makes practicable a qualitative relationship between the elements of Intrapreneurship. We used the integrated approach of fuzzy Dematel and fuzzy modeling network analysis to determine the relationship and weight relative importance of each element in model. The results indicated “leadership” at the main criteria level has the most highly effect and “employees” has the most important coefficient. Also "senior management support" has the highest importance coefficient and highly effect in the sub-criteria. In this study we presented individual entrepreneurs and Intrapreneurship assessment model that is a comprehensive model and can be used in different organizations and finally we evaluated this model in Iran Power Development Company. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  156 - Structural Modeling of strengthening purchase intention in the Instagram as a social network
  یاسر سبحانی فرد
  The effect of social networks on consumer behavior in cyberspace is growing. The popular social network Instagram as one of the networks are strengthening their role in purchasing intention. This article seeks to examine know how advertising on the social network Instag More

  The effect of social networks on consumer behavior in cyberspace is growing. The popular social network Instagram as one of the networks are strengthening their role in purchasing intention. This article seeks to examine know how advertising on the social network Instagram and its role on the consumer`s shopping intention. In this regard, the study has tested effect of six advertising factors in Instagram on the shopping intention. Population of this study is Instagram users in the city of Qazvin. To generalize the results to the community infinite by random sampling, 384 samples were elected. To answer hypotheses and test the study model, structural equation modeling was used. The results confirm the study's model and majority of hypotheses. Test results showed that the informativeness and advertising creativity, have a positive effect on attitudes toward empathy expression and and this will also affect the intention to express empathy and this reality have positive affect on Purchase intention. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  157 - Investigating the effect of the internet marketing on the growth of the exports market in Iran (Case study: Dried fruit exporters)
    MOHAMMADREZA ARDEHALI
  Abstract In recent years, for companies of different industries, The internet and Internet marketing has become a way of earning income, interacting with the customers and shareholders, providing products and services, and electronic sale. The current research is aim More

  Abstract In recent years, for companies of different industries, The internet and Internet marketing has become a way of earning income, interacting with the customers and shareholders, providing products and services, and electronic sale. The current research is aimed at studying the effects of the performance of Internet marketing on growth of export markets. In terms of purpose, this research is an applied one and in terms of data collection method, it is a descriptive study of correlation type. The research population consists of the marketing experts and marketing and sale managers of the Iranian dried fruit export companies. After the initial confirmation of reliability and validity of the questionnaire, it was distributed among 94 experts. First, the respondents and questions of the questionnaire were analyzed by SPSS software. Then, the hypotheses were studies by structural equation techniques, Smart Pls, and using T-value test, path coefficient, and correlation coefficient. The results show that the capability of Internet marketing has a significant and positive effect on growth of export market. Also, other research hypotheses were approved with the probability of 95 percent. Keywords: Internet Marketing, Export Market Growth, Availability of Export Information, Business Network Relationships, Dried Fruit Exporters Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  158 - Prediction of Growth of Small and Medium Enterprises with the Combination of Artificial Neural Networks and Meta-Heuristic Algorithm
  حامد ابراهیم خانی مصطفی کاظمی Alireza Pooya Amir Mohammad  Fakoor Saghih
  The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting More

  The growth of a company is considered to be an important economic goal. Given that many small and medium enterprises do not grow into growth and fail in the early years of their operations, a predictive system of corporate growth can be offset by the huge costs Starting businesses, entrepreneurs and companies to pay. Accordingly, the purpose of this study was to predict the growth of small and medium enterprises with the combination of neural network and meta-heuristic algorithms. The purpose of this research was applied and based on the method of doing descriptive-modeling work. Statistical population of this research was all small and medium enterprises of Zanjan province. Statistical sample size According to the growth of companies, 158 companies has been designated. In order to collect data in this study, interviews, questionnaires and documents of companies have been used. Validity and reliability of the questionnaire were verified and and using Cronbach's alpha coefficient. In order to analyze the research data using confirmatory factor analysis methods, the neural network of multilayer perceptron, neural network combined with genetic algorithm and neural network combined with particle swarm algorithm have been used. The results show that all three methods are able to predict the growth of the company. Among these three methods, the best predictive method for growth of the company is the neural network combined with the particle swarm algorithm with the least error rate compared to the other two methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  159 - Evaluation efficiency of the internal structure of decision making units in the past, present and future using dynamic network data envelopment analysis and artificial neural network
  javad niknafs mphammadali keramati jalal haghighatmonfared
  Network data envelopment analysis models and dynamic network data envelopment analysis models cannot evaluated the future performance of the internal structure of decision-making units .In other words, all NDEA and DNDEA models evaluate the past performance of their DMU More

  Network data envelopment analysis models and dynamic network data envelopment analysis models cannot evaluated the future performance of the internal structure of decision-making units .In other words, all NDEA and DNDEA models evaluate the past performance of their DMUs and their internal structure, and measure their efficiency and inefficiency, and ultimately rank them based on that assessment .In this paper, we are going to evaluation the future efficiency of deposit and lending sections in bank branches. In order to notified inefficiencies in the internal structure of a unit before the occurrence, we will prevent it.This approach can change the role of managers from the evaluator to the planner .First, using the literature of the subject and opinion of the experts, the structure of the bank branches and the network variables were determined .Then, the values of variables are forecasted using the artificial neural network for the next two periods.Finally, a DNDEA model is formulated using the values of past periods and predicted values.Using its efficiency, its branches and its internal structure have been evaluated in the past, present and future. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  160 - طراحی شبکه زنجیره تأمین با در نظر گرفتن هماهنگی سطوح با استفاده از تبلیغات مشارکتی در بستر فروش اینترنتی
  Ali asghar Emadabadi Ebrahim Teimoury mir saman pishvaee
  The decisions about designing the supply chain network are considered as strategic decisions of supply chain management, which have long-term impacts on overall performances of the chain. However, one of the most important challenges of problems of designing the supply More

  The decisions about designing the supply chain network are considered as strategic decisions of supply chain management, which have long-term impacts on overall performances of the chain. However, one of the most important challenges of problems of designing the supply chain network is coordination of its parts that can lead to several restrictions in tactical and operational levels. One of the increasing approaches of the coordination in supply chain is using Collaborative Advertisements. In this article, using the collaborative advertisement in designing the multi-level supply chain, multi-period and multi-product, have been considered in order to make coordination. The chain is also looking forward to fulfill the requirements of establishing new facilities. Advertisements are provided in two sections; supplying advertisements and chain advertisements. The chain advertisements and the information resource of the chain that customers purchases and orders registration are registered within it, is an internet website, which gets some percent as website’s costs per purchase from the chain’s parts. The demand of each customer is considered dependent on price and advertisement function. In case study the plan of publication subscription has been specified that for all of the variances in the impact factor of the advertisement supplier, summation of supplier’s benefits in presented model are better than the present situations; however, this matter is not compatible with all of the suppliers and some of them would have less benefits. But by means of sharing benefits of this coordination, this problem would be satisfied Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  161 - A dynamic capability approach on designing governance models in health care networks
  Alireza Aliahmadi Mohammadreza Rasouli
  The use of network business models to provide health services is a growing trend in the world's successful health systems. Forming and sustaining successful service delivery networks requires using the structures and mechanisms that address the health system characteris More

  The use of network business models to provide health services is a growing trend in the world's successful health systems. Forming and sustaining successful service delivery networks requires using the structures and mechanisms that address the health system characteristics, such as the asymmetry of the health market, as well as the need to guarantee the quality of services. In this paper, based on the dynamic capability approach, relevant structures and mechanisms have been identified that can make the ability to successfully governing service delivery networks in health systems. The effectiveness of identified structures and mechanisms have been evaluated using a case study within a clinical laboratory network in Tehran. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  162 - Designing a sustainable supply chain network under sanction conditions
  Ezzatollah Asgharizadeh S. Ali Torabi Ali Mohaghar Mohammad Ali Zare Shourijeh
  Nowadays, with the advent of the concepts of risk management and sustainability and increasing of social pressures to reduce the negative impacts of industries on the environment and society, it is almost impossible to make strategic decisions such as the supply chain n More

  Nowadays, with the advent of the concepts of risk management and sustainability and increasing of social pressures to reduce the negative impacts of industries on the environment and society, it is almost impossible to make strategic decisions such as the supply chain network design (SCND) without considering these concerns. Accordingly, in this paper a general mixed-integer linear programming (MILP) was developed for designing a closed-loop supply chain network, under disruptions conditions and sustainability considerations (focusing on social aspects). This is a multi-echelon, multi-product and multi-modal supply chain network. The sanctions on raw materials (as disruption events) were considered in the mathematical model through planning some scenarios. The most important decision variables in this model are related to: supplier selection, quantity of each raw material, facilities location, products flow between different facilities and transportation modes. Finally, designing a closed-loop supply chain network in Iran's tire industry was taken as a real world case study to demonstrate the applicability and effectiveness of the proposed model. In addition, the sensitivity analysis on some of the important parameters was carried out. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  163 - A systematic review of artificial neural network applications in supply chain management
    Aref  Toghroljerdi pooria malekinejad
  Nowadays, the success rate of companies/organizations in the competitive market is the performance of their supply chain managment. Various techniques have been utilized to improve it, which one of the most widely used methods to solve these problems is artificial neura More

  Nowadays, the success rate of companies/organizations in the competitive market is the performance of their supply chain managment. Various techniques have been utilized to improve it, which one of the most widely used methods to solve these problems is artificial neural network. The purpose of this study is to systematically review the various applications of artificial neural networks in solving the problems of different parts of the supply chain. Hence, by using the literature review, the key vocabulary of the link between the two domains was identified. Using the keywords extracted from the research literature, a search was made between the Scopus databases and Web-based Science. By searching in these databases, articles related to the application of artificial neural network in different areas of supply chain have been extracted. Finally, the articles were filtered using a variety of tools and then high-ranking papers were identified. Using important articles identified, various categories of artificial neural network applications were implemented in supply chain management. The results of this study indicate that artificial neural networks have been most used in solving engineering, computer science and business issues Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  164 - Internal structural and operational factors influencing on designing network governance model in health care systems: a systematic literature review
  Reza Aalikhani Alireza Aliahmadi Mohammadreza Rasouli
  Introduction: todays, with regard to competitive market in health system collaboration networked clllaboration has become an essensial strategy for the survival of organizations. Network governance that refers to coordination and collaboration in the network is a fundam More

  Introduction: todays, with regard to competitive market in health system collaboration networked clllaboration has become an essensial strategy for the survival of organizations. Network governance that refers to coordination and collaboration in the network is a fundamental concept that affect collaboration network success. There are various factors that affect designing successful model of network governance that considering this factors is critical for designing successful model of network governance. The aim of doing this research is identifying internal structural and operational factors that affects designing proper model of network governance in health care delivery systems. Methodology: to identify and extract desired factors used of the mentioned evidence in the literature related to research question. For this porpous we need a research methodology that comprehensively and systematically consider the literature. For achieving this aim a systematic literature review will consider as a comprehensive and systematic method for identifying valid evidence and related to the research question. Also, Grounded Theory method was used to synthesis the extracted evidence. Results: by searching in the WEB OF SCIENCE as a comprehensive and credible database for identifying related studies, the 2350 scientific publication resulted. By screening these results basis of title fitness with studies inclusion and exclusion criteria lead to select 150 articles. Finally, through screening described the 150 articles basis of their abstract and conclusion fitness lead to the 53 final articles that during the reviewing them the 78 pieces of evidences extracted from the 37 sources and did not identify any evidence from other studies. For creating extensive view and easy access to extracted evidences in the synthesizing step using granded theory, 2 categories and 16 classes emerged. Conclusion: designing governance model in the network will be successful if only internal structural and operational factors influencing on designing it have been considered. Structural and operational factors in the network include network characteristics and network administration characteristics, are influencing factors that considering these factors on designing proper model of governance lead to fitness of the goernance model with network characteristics that finally lead to collaboration network success. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  165 - Identifying and prioritizing key abilities affecting on successful technology transfer process in petrochemical downstream industries
  abbas khamseh Yadollah sadeghi Mehrdad Hosseini Shkaib taqi torabi
  Despite the importance of developing petrochemical industries, most of the country's exports in the petrochemical industry have been limited to raw materials and semi-industries, which is due to the incapability to acquire appropriate technology in the downstream indust More

  Despite the importance of developing petrochemical industries, most of the country's exports in the petrochemical industry have been limited to raw materials and semi-industries, which is due to the incapability to acquire appropriate technology in the downstream industries. Technology transfer is one of the most important shortcut methods for acquiring and developing technologies in these industries, but one of the most important challenges is the managers' little familiarity with the technology transfer process and the abilities it requires. The aim of this study is to identify and prioritize the effective abilities in the success of the technology transfer process in the downstream, petrochemical industries, and for this purpose a combined method of Structural Equation Modeling (SEM) and Analytic Network Process (ANP) has been used. In this regard, the content validity was confirmed by CVR and CVI forms and the final questionnaire was distributed and collected by the Porsline system among the statistical population and in order to investigate the accuracy of the theoretical model of the research, factor analysis with Structural Equation Modeling (SEM) and AMOS software was used. The results of this stage of the study indicate that 82 indicators in 8 ability factors affect the success of the technology transfer process in petrochemical downstream industries and the effectiveness of all 8 factors is ability at the semantic level. On the other hand, prioritizing the abilities that affect the success of the technology transfer process was done through the Analytic Network Process and Super Decisions software. The results show that technology dissemination abilities are the first priority, and technology adaptation abilities as well as development and improvement abilities are the second and third priorities. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  166 - Investigating a model of implementation and development business intelligence in organization with the purpose of improving decision-making
  Payam Yaghli    
  This article firstly tries to identify the various dimensions of successful implementation and development business intelligence as a set of technologies and processes that promote the procedure of the decision-making in the organization. Next, the effect of these dimen More

  This article firstly tries to identify the various dimensions of successful implementation and development business intelligence as a set of technologies and processes that promote the procedure of the decision-making in the organization. Next, the effect of these dimensions on the process of decision-making in Eghtesad-e-Novin Bank, as a financial organization in which the speed of decision-making is of importance will be discussed. The criteria are the cultural, strategic and environmental factors, human resources and business intelligence tools the effects of which are discussed on the variable of "the latency in decision- making" in four divisions of organizational decision environment. The research methodology is mixed in this research and for this purpose in the qualitative part, the different dimensions of success in implementation and development business intelligence were extracted on the quality of decision-making with the Delphi technique by using the method of interviews and open questions in three stages and with the snowball method with the opinions of 18 experts. In the quantitative part, the priorities were determined with the help of the Analysis Network Process and Super Decision software. Finally, the main questionnaire has been finalized for the distribution among 90 personnel of different levels of the development and implement of business intelligence systems and the direct users of these systems in the organization. With the help of Smart Plus software and structural equations, as well as the effect of research variables on the reduction in the latency in decision-making, the relevant assumptions have been examined in a descriptive and survey manner with factor analysis. The proposed factors and models for evaluating business intelligence presented in this article help organizations, especially financial organizations in which speed in decision-making is of particular importance, to promote decision-making and minimize possible latencies in decision-making. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  167 - analysis of co-authorship network of Iranian regenerative medicine scholars;
  Atieh bozorgipour Soroosh Ghazinoori Mohammad Ghazinoori
  Co-authorship is one of the most common manifestations of scientific collaboration between researchers and scholars of scientific disciplines. The significant growth of this phenomenon in various scientific disciplines in the last two decades has made the study of diffe More

  Co-authorship is one of the most common manifestations of scientific collaboration between researchers and scholars of scientific disciplines. The significant growth of this phenomenon in various scientific disciplines in the last two decades has made the study of different features of co-authorship networks become an interesting issue for researchers. The study of these networks in various scientific disciplines such as mathematics, physics and information sciences using existing tools in the field of social network analysis has been considered. Due to the growing phenomenon of co-authorship in various fields of medical sciences, in this article, the co-authorship network of Iranian researchers in the field of reconstructive medicine has been analyzed. In addition, the network of scientific cooperation between Iranian institutions active in the field of reconstructive medicine has been analyzed and studied. Finally, based on the analysis, suggestions are made to improve the quantitative and qualitative level of research. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  168 - Scientometrics and content analysis of research in the field of organizational entrepreneurship from the perspective of individual and group entrepreneurship in the organization
  Hossain Sahebi Hamideh Reshadatjoo Mohammad Aboie Ardakan
  Addressing corporate entrepreneurship has grown significantly in recent years and is known to be an important factor affecting success of an organisation. Nevertheless, the importance of the subject and research in this field, especially in domestic research, is neglect More

  Addressing corporate entrepreneurship has grown significantly in recent years and is known to be an important factor affecting success of an organisation. Nevertheless, the importance of the subject and research in this field, especially in domestic research, is neglected and there is a wide disagreement in this field. This research has been done by scientometric method, and by using the method of citation analysis, evaluation technique and content analysis of documents; It intends to review studies in the field of corporate entrepreneurship and systematize past research to provide a conceptual model using content analysis of documents. The statistical cumulation of this research includes all the documents presented in the Scopus database with 1325 documents, including articles, books, etc. until 2019. Which is done using Bibexcle and VOSviewer software evaluation and illustration. Based on the content analysis of the documents; Research in the field of "corporate entrepreneurship from the perspective of individual and team entrepreneurship in the organization" is divided into four general sections. The first part of the research related to the pillars of corporate entrepreneurship includes innovation, strategic renewing, and business within the organization. The second part is related to individual entrepreneurship in the organization that concerns entrepreneurial orientation, motivation, ideation and creativity, work experience, acquisition Knowledge and skills development are highlited. The third part is related to team entrepreneurship in the organization, topics such as organizational recruiting capacity, independence, awarding system and effective behavior were identified in the process. The fourth and final part deals with motivating corporate entrepreneurship. It includes factors such as institutional, structural, managerial and environmental Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  169 - Managing the formation of Habermas public domain theory in Instagram and Telegram social networks
  Ali asadnzhad Ali Jabari Nahid  Kordi
  The aim of the present study is to manage the formation of Habermas public domain theory in Instagram and Telegram social networks. The research method is field survey and survey. This research is a descriptive research in terms of the purpose of applied research. In th More

  The aim of the present study is to manage the formation of Habermas public domain theory in Instagram and Telegram social networks. The research method is field survey and survey. This research is a descriptive research in terms of the purpose of applied research. In this research, after conducting library studies and complete and comprehensive compilation of theoretical literature and research background, a questionnaire has been designed and compiled in line with research related theories and research questions and hypotheses and distributed among the respondents. The statistical population of this research consists of the audience (cyberspace users) whose number is estimated at 8,674.621 million and according to Cochran's formula, its statistical sample is equal to 384 people. Therefore, in this research, by following this formula, 384 people have completed a questionnaire as a statistical sample. Using purposive non-random sampling method, the study sample was selected and the research data were analyzed by Pearson correlation method. The results showed that the highest impact in the first place of Internet access (β = 0.42), the second time of logical and rational critique of political, economic, social and cultural issues in social networks (Telegram and Instagram), respectively (= 0.39 b) The third time, the audience had a two-way and free conversation (all) with government officials on social networks (Telegram and Instagram) (β = 0.38). The fourth time, they used social networks (Telegram and Instagram) (β = 0.17). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  170 - Identifying the effective actors of forex trading network in the NIMA system using social network analysis
  Mehrdad Agha Mohammad Ali Kermani mohammad javad Rokhsat Talab Saeed Mirzamohammadi
  Nima system is an integrated system of foreign exchange transactions. Nima is a platform system that has been designed and implemented with the aim of managing the foreign exchange market. In this system, on the supply side, exporters can sell the currency from their ex More

  Nima system is an integrated system of foreign exchange transactions. Nima is a platform system that has been designed and implemented with the aim of managing the foreign exchange market. In this system, on the supply side, exporters can sell the currency from their exports, and on the demand side, importers can request to buy foreign currency. Identifying effective actors in each of these markets can have a positive impact on the policies of the main market maker of this system, the central bank.Using social networks analysis (SNA) tools can be a good way to achieve this. Since in these networks each of the actors can only have one of the roles of "buyer" or "seller" and there is only the possibility of trading and communication with the opposite role, these networks can be called bipartite networks. As a result, the usual approaches to identifying effective actors for these networks will not be usable. In contrast to standard approaches, we used a weighted projection algorithm to solve this problem. After projectting each of the two networks of foreign exchange supply and demand, four new networks are created, including the network of seller-exporters, buyer exchange offices, buyer importers, and seller exchange offices. Then We will try to make a method to score and rank the nodes. As a result of the implementation of the algorithm, a ranking was provided for the nodes, based on which the node with the highest rank will be the most important node in our network. Finally, in order to make suggestions to the policymaker, by analyzing the results of the ranking, questions about effective market players were answered. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  171 - Cache Point Selection and Transmissions Reduction using LSTM Neural Network
  Malihe  Bahekmat Mohammad Hossein  Yaghmaee Moghaddam
  10.52547/jist.27279.11.42.123
  Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance More

  Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  172 - A Method Based on Learning Automata for Adaptation of the Vigilance factor in Fuzzy ARTMAP Network
  M. Anjidani M. R. Meybodi
  In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzz More

  In this paper, a method based on learning automata for adaptation of the vigilance factor in Fuzzy ARTMAP network when used for classification problems is proposed. The performance of the proposed algorithm is independent of the initial value for vigilance factor. Fuzzy ARTMAP network in which the vigilance factor adapted using learning automata generates smaller structure with higher recognition rate. To study the performance of the proposed method it has been applied to several problems: circle in square, spirals and square in square problems. The results of experiments show the effectiveness of the proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  173 - Robust Recognition of Direct and Telephony Speech Using Proper Extraction of Feature Vectors and Their Modification by Neural Networks Inversion
  M. Vali S. A. Seyed Salehi
  A vast amount of research is going on for design of robust speech recognition in to alleviate speech variability conditions. One of the variability aspects is the difference between telephony speech and direct speech (recorded in noise free conditions). In this paper by More

  A vast amount of research is going on for design of robust speech recognition in to alleviate speech variability conditions. One of the variability aspects is the difference between telephony speech and direct speech (recorded in noise free conditions). In this paper by using a set of experiments, it is shown that LHCB parameters are superior to traditional MFCCs for speech recognition applications when they are used in a neural network based speech recognition system for both direct and telephony speech. Then by extraction of LHCBs from direct and telephony speech, and training of a MLP based speech recognition model, a direct and telephony speech recognition system is developed. Using a neural network inversion based on gradient descent method, the telephony speech feature vectors are modified toward to the direct speech feature vectors and by training a second network on modified telephony and direct speech feature vectors a 1.4% enhancement on speech recognition was achieved. Later, using general inversion method of neural networks both telephony and direct speech feature vectors are modified in a manner which mainly contains phonetic information and not other speech variations. Then by the training of the second neural network on this dataset, the system achieved 2.98% and 1.68% higher recognition rate for direct and telephony speech, respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  174 - A New Ensemble Learning Method for Improvement of Classification Performance
  S. H. Nabavi-Kerizi E. Kabir
  The combination of multiple classifiers is shown to be suitable for improving the performance of pattern recognition systems. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. The methods have been proposed for dive More

  The combination of multiple classifiers is shown to be suitable for improving the performance of pattern recognition systems. Combining multiple classifiers is only effective if the individual classifiers are accurate and diverse. The methods have been proposed for diversity creation can be classified into implicit and explicit methods. In this paper, we propose a new explicit method for diversity creation. Our method adds a new penalty term in learning algorithm of neural network ensembles. This term for each network is the product of its error and the sum of other networks errors. Experimental results on different data sets show that proposed method outperforms the independent training and the negative correlation learning methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  175 - Solving Multi-Criteria Decision Making Problems Using Artificial Neural Networks
  M. abdoos N. Mozayani
  Decision making is finding the best compromised solution from all feasible alternatives. Multi-criteria decision making is one of the most applied branches of decision making. Many methods have been presented for solving MCDM problems ever since. Among these methods, si More

  Decision making is finding the best compromised solution from all feasible alternatives. Multi-criteria decision making is one of the most applied branches of decision making. Many methods have been presented for solving MCDM problems ever since. Among these methods, simple additive weighting, SAW, is the most commonly used method. In this paper, two methods are proposed for solving MCDM problems based on artificial neural networks. This paper shows an application of soft computing techniques in classic problems, such as decision making. Herein, two methods are presented based on both supervised and unsupervised neural networks. The results of the methods have been compared with SAW. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  176 - Letter to Sound Conversion for Persian Language Using Multi Layer Perceptrons
  M. Namnabat M. M. Homayounpour
  Construction of letter to sound (LTS) conversion systems in Persian is a difficult task. Because of the omission of some vowels in Farsi orthography, these systems in general have low efficiencies. In this paper, the structure of a letter to sound system, having three-l More

  Construction of letter to sound (LTS) conversion systems in Persian is a difficult task. Because of the omission of some vowels in Farsi orthography, these systems in general have low efficiencies. In this paper, the structure of a letter to sound system, having three-layer architecture, was presented. The first layer is rule-based, and the second layer consists of five multi layer perceptron (MLP) neural networks and a controller section for pronunciations determination. The third layer has a MLP network for detection of geminated letters by using results obtained from the previous steps. The proposed system is designed to produce rational pronunciations for every word, where the rational pronunciation means a phonetic transcription, which follows the correct Farsi syllabification structure and the obvious rules of phonetics. The authors have achieved 88% and 61% correct letters and words performance respectively, which is quite satisfactory for a Farsi language LTS system. The correct letter criterion is the percentage of letters for which the pronunciations have been determined correctly and the correct word criterion is the percentage of words for which the pronunciations of the constituting letters have been determined correctly. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  177 - A New Method for Ripple Reduction of DC Voltage Using Active Filter
  S. M. Dehghan A. Yazdian Varjani M. Mohamadian
  Fluctuations and ripples in voltage or current of DC power systems cause different malfunctions in operation of equipments and systems which are supplied by low quality distribution power systems. Therefore ripple reduction of voltage or current in DC power systems is v More

  Fluctuations and ripples in voltage or current of DC power systems cause different malfunctions in operation of equipments and systems which are supplied by low quality distribution power systems. Therefore ripple reduction of voltage or current in DC power systems is very important. In this paper a new method is proposed to reduce ripple of DC voltage in high power system using an active power noise cancellation filter (APNCF). In the proposed method a hybrid system including series and parallel active filters for ripple reduction of load voltage and source current is used. Simulation and experimental results show the performance of the proposed method in dynamic and static states. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  178 - An Adaptive Wavelet-Based Signal Denoising Schem
  M. nasri H. Nezamabadi-pour S. Saryazdi
  In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, bo More

  In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, both the shape and the threshold parameters are tuned simultaneously using LMS rule. This permits us to consider the effects of both the threshold and the shape parameters on denoising. The proposed functions are tested in both universal-threshold and subband-adaptive denoising and compared with conventional functions. In addition, to evaluate the proposed training method, several numerical examples are performed. The experimental results obtained from denoising of several standard benchmark signals confirm the efficiency and effectiveness of the proposed methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  179 - Determining of Classifiers Behavior Using Hidden Markov Model Based Decision Template
  H. Sadoghi Yazdi
  Studying of classifier behavior is interested from viewpoint of error checking and presentation of suitable solution for decreasing error rates and decreasing performance. Weakness operation of recognition system is because of small number of training samples, noisy sam More

  Studying of classifier behavior is interested from viewpoint of error checking and presentation of suitable solution for decreasing error rates and decreasing performance. Weakness operation of recognition system is because of small number of training samples, noisy samples, unsuitable extracted features, method of determining of system response. Presentation of suitable model for behavior or response of recognition system, we can improve operation of recognition system. In this paper, a new hidden Markov model based decision template is generated for modeling of neurons behavior in neural network. In existing methods, relation of neurons and interaction between them is not studied whereas; response of neural network includes response value of all neurons. So, relations of neurons are modeled using new hidden Markov decision templates. This method is used into three applications include recognition of Farsi number images, normal traffic in internet network, and recognition of types of vehicles. Increasing performance of neural network indicates to superiority of the proposed system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  180 - On the Stability of Primal-Dual Congestion Control Algorithm in the Presence of Exogenous Disturbances
  A. moarefianpour V. johari majd
  In this paper, we consider the effects of exogenous disturbances on the closed-loop system of the congestion control problem in a network with general structure. This investigation is important since many of data flows in internet network are considered as unmodeled flo More

  In this paper, we consider the effects of exogenous disturbances on the closed-loop system of the congestion control problem in a network with general structure. This investigation is important since many of data flows in internet network are considered as unmodeled flows. In contrast to previous works, we suppose that both senders and links in the network have dynamics. Each sender updates its sending rate to minimize its own cost function. The network is modeled based on fluid flow approximation with nonlinear dynamics for the links. In this research, we first derive the conditions for the existence of the system equilibrium point taking into account the constraint sets of the problem. Then, we prove input-to-state stability (ISS) of the closed-loop system for the congestion control problem with input and output disturbances in the network links. We further show that the obtain results are valid even when the routing matrix of the network varies. Finally, we verify the theoretical results by simulation on two different multi-link networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  181 - A New Modified Unequal Error Protection Approach in the Video Transmission over Wireless Networks
  H. ghaneiy M. Khademi J. chitizadeh
  The performance of video transmission over wireless channels is limited by the channel noise. Thus many error resilient tools have been incorporated into the MPEG-4 video compression method. In addition to these tools, the unequal error protection (UEP) technique has be More

  The performance of video transmission over wireless channels is limited by the channel noise. Thus many error resilient tools have been incorporated into the MPEG-4 video compression method. In addition to these tools, the unequal error protection (UEP) technique has been proposed to protect the different parts in a MPEG4 video packet with different channel coding rates based on the rate compatible punctured convolutional (RCPC) codes. However, it is still not powerful enough to achieve a high visual quality over the wireless networks. To provide more robust MPEG-4 video transmission over wireless channels, this paper proposes a modified unequal error protection (MUEP) approach based on the content of the video scene. In proposed technique, channel coding rates for motion section of the video packet are determined based on the motion of the video scene. Experimental results show that the proposed technique enhances both subjective visual quality and PSNR about 1.5 dB, comparing to the traditional UEP method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  182 - An Approach to Increase Internet Traffic Transmission Rate in All-Optical OPS Networks
  اکبر غفارپور رهبر
  Consider an all-optical time-slotted OPS network in which Internet protocol has been used at upper layers. Contention is a major problem for such a network. It is shown in this paper that optical packet retransmission at the optical domain can improve Internet throughpu More

  Consider an all-optical time-slotted OPS network in which Internet protocol has been used at upper layers. Contention is a major problem for such a network. It is shown in this paper that optical packet retransmission at the optical domain can improve Internet throughput and can even reduce cost of OPS network implementation. In this technique, a copy of transmitted optical packets are saved in ingress switches and retransmitted when required, independent of TCP layer retransmission. In other words, retransmission may happen both at the optical layer and at the TCP layer. In this paper, an approach is proposed to reduce cost of OPS network implementation. In addition, Internet throughput is studied in slotted OPS network and an approach is proposed to increase Internet throughput and to improve throughput of long-hop connections. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  183 - NGN Management Model and Utility Networks’ Estimated Capital Investment in Operation Support Systems for the Next Years
  A. Jahanbeigi M. E. Kalantari
  By the introduction of the concept of Next Generation Networks (NGN), the management of these networks has become an important issue. So, the requirements, goals and the overall architecture of NGN management systems are discussed in this paper. The functional classific More

  By the introduction of the concept of Next Generation Networks (NGN), the management of these networks has become an important issue. So, the requirements, goals and the overall architecture of NGN management systems are discussed in this paper. The functional classification of Operation Support Systems (OSS) used in NGN management is also presented. Because of the need of utility networks (such as telephony, data, water, electricity and gas) in the country to OSS, the amount of required capital investment for deployment of Network Management System (NMS) and Customer Care and Billing System (CCBS), as main parts of OSS, are calculated for the next four years in this paper. To reach these estimates, the total number of subscribers in mentioned networks is forecasted for this period and information about per customer capita is also extracted. Two frameworks of, distributed and centralized, are considered. This assessment shows an approximate investment of M606$ and M432$ is needed for NMS and CCBS in distributed scenario, respectively. The same requirement in centralized scenario is M433$ and M322$ for NMS and CCBS, respectively. So, an approximate investment of M1040$ and M750$ is needed for OSS in distributed and centralized scenarios, respectively. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  184 - Estimation of the Required Capital Investment in Mobile Telephony Network's Equipments Based on Cobb-Douglas Demand Forecasting Model
  A. Jahanbeigi M. E. Kalantari
  The goal of this paper is to estimate the capital investment in mobile telephony network's equipments, by using Cobb-Douglas model to forecast the number of subscribers for the next years in the country. Then by presenting a master plan for Base Station Subsystem (BSS) More

  The goal of this paper is to estimate the capital investment in mobile telephony network's equipments, by using Cobb-Douglas model to forecast the number of subscribers for the next years in the country. Then by presenting a master plan for Base Station Subsystem (BSS) and Network Switching Subsystem (NSS) parts of the network, the required equipments and also the capital investment amounts are estimated. In the BSS plan, the number of Base Transceiver Stations (BTS) with various configurations and also Base Station Controllers (BSC) with different capacities and required accessories (such as tower, antenna, feeder, power supply and transmission equipments between BTSs and BSCs) are determined. In the NSS plan, an architecture is proposed for network's traffic (including signaling). The routing plan and required interfaces with Public Switched Telephone Network (PSTN) and Public Data Network (PDN) are also presented and the capacity of network's nodes and E1 links are determined. Based on the mentioned plan and also the typical cost of different equipments, offered by domestic and foreign vendors, the capital investment of 26.7 trillion Rials seems to be necessary to increase the penetration rate from 12.4% to 48.4% in mobile telephony network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  185 - On-line Eye Blink Suppression from EEG Signals Using Adaptive Independent Component Analysis for Brain Computer Interfacing
  F. Shayegh A. Erfanian
  For several years, many efforts have been done to use the electro-encephalogram (EEG) as a new communication channel between human brain and computer. This new communication channel is called EEG-based brain-computer interface (BCI). The aim of brain-computer interface More

  For several years, many efforts have been done to use the electro-encephalogram (EEG) as a new communication channel between human brain and computer. This new communication channel is called EEG-based brain-computer interface (BCI). The aim of brain-computer interface (BCI) research is to establish a new communication channel that directly translates brain activities into sequences of control commands for an output device such as a computer application or a neuroprosthesis. The major advantage of EEG-based BCI is that no physical movement is required. The motor imagery is the essential part of the most EEG-based communication systems. One of the major problems in developing a real-time Brain Computer Interface (BCI) is the eye blink artifact suppression. Recently, a more effective method has been introduced for removing a wide variety of artifacts from multi-channel EEG signals based on blind source separation by Independent Component Analysis (ICA). However, the method requires visual inspection of ICA components and manual classification of the interference components. This can be time-consuming and is not desirable for real-time artifact suppression. Moreover, the real-time application of this method for artifact rejection has not been considered so far. In this paper, various ICA methods with adaptive learning algorithm are presented and evaluated by computer simulation. The results from real-data demonstrate that the proposed scheme removes perfectly eye blink artifacts from the contaminated EEG signals and is suitable for use during on-line EEG monitoring and EEG-based brain computer interface. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  186 - Design and Analysis of Low Frequency Communication Multipath Channel to Safe Transmitting Speech Signal in Persian Gulf
  H. Bakhshi H. Shahbazi
  One of the important applications of underwater communication is speech transmission between two divers or between divers and ship or submarine. This paper describes a project designed to investigate and demonstrate underwater communication system in Persian Gulf for sp More

  One of the important applications of underwater communication is speech transmission between two divers or between divers and ship or submarine. This paper describes a project designed to investigate and demonstrate underwater communication system in Persian Gulf for speech transmission in a real channel. At first, transmitter is designed, then channel with real data is simulated by neural network and at last receiver is designed. Transmitted data is speech signal that for more secure transmission and low frequency bandwidth, a cryptography algorithm and speech coding algorithm is applied in transmitter. Quadrature phase shift keying (QPSK) signaling is employed to make efficient use of the available channel bandwidth. In the receiver, linear equalizer and decision feedback equalizer (DFE) are tested and the best scheme is applied. Also, ray tracing method is used for simulation of sound waves propagation in Persian Gulf underwater communication channel. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  187 - A Simple and Effective Scheme for Hand Gesture Recognition in Finger Spelling of Farsi Alphabet
  M. J. Barzegar Sakhvidi A. R. Sharafat
  In recent years, automated recognition of gestures in the finger spelling paradigm has become an active research area. Gesture is a combination of hand postures, hand movements, and face gestures; and finger spelling is a way of presenting alphabets of a word that does More

  In recent years, automated recognition of gestures in the finger spelling paradigm has become an active research area. Gesture is a combination of hand postures, hand movements, and face gestures; and finger spelling is a way of presenting alphabets of a word that does not exist in the sign language dictionary. In this paper, we present a scheme for hand gesture recognition in finger spelling of Farsi alphabets, where a different shape for hand and fingers denote a different letter in the alphabet. Our scheme has five stages, namely, visual data gathering, preprocessing of the image, detection and extraction of hand’s features, feature reduction and consolidation, and finally, hand gesture recognition. For the last stage (hand gesture recognition), we employ three techniques, namely, the nearest neighbor using the Euclidian distance, the nearest neighbor using the normalized Euclidian distance, and neural networks. For reducing the feature space, we use the discrete cosine transform (DCT), which yields better results as compared to the discrete Fourier transform and Fourier coefficients. We achieved 99.1% correct recognition using neural networks, which is superior to existing schemes. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  188 - Improving QoS and Reducing Transmit Power in Wireless Ad Hoc Networks by Distributed Power Control Using SINR and Transmit Power Pricing Functions
  R. Haratian A. R. Sharafat
  We propose a scheme for improving QoS and reducing transmit power in wireless ad hoc networks by utilizing the signal-to-interference-plus-noise-ratio (SINR) and a pricing function that is proportional to the transmit power of each user. The performance of our proposed More

  We propose a scheme for improving QoS and reducing transmit power in wireless ad hoc networks by utilizing the signal-to-interference-plus-noise-ratio (SINR) and a pricing function that is proportional to the transmit power of each user. The performance of our proposed method is analyzed by using game theory, where each user’s quality of service is a function of its SINR. The utility function for each user is defined by its desired SINR minus a pricing to provide adequate incentive for each user to choose its power level in such a way to maximize the aggregate of all users’ utilities (total network utility) instead of selfishly maximizing its own SINR. Simulation results show that the performance of the network is improved while the total power consumption is reduced. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  189 - Application of Neuro Space Mapping in Modeling Semiconductor Devices
  M. Gordi Armaki S. E. Hosseini Mohammad Kazem Anvarifard
  In this paper an efficient method for modeling semiconductor devices using the drift-diffusion (DD) model and neural network is presented. Unlike HD model which is complicated, time consuming with high processing cost, the proposed method has lower complexity and higher More

  In this paper an efficient method for modeling semiconductor devices using the drift-diffusion (DD) model and neural network is presented. Unlike HD model which is complicated, time consuming with high processing cost, the proposed method has lower complexity and higher simulate speed. In our method, a RBF neural network is used to modify DD parameters. The modified DD model can generate simulate results of accurate HD model. The proposed method is first applied to a silicon n-i-n diode in one dimension, and then to a silicon thin-film MOSFET in two dimensions, both for interpolation and extrapolation. The obtained results for basic variables, i.e., electron and potential distribution for different voltages, confirm the high efficiency of the proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  190 - Simplify Programming of TinyOS Applications for Wireless Sensor Networks
  M. Khezri M.  Sarram F. Adibnia
  Sensor node operating system provides a limited number of common services for developers to construct applications for wireless sensor networks. The sensor network community selected TinyOS as the de facto standard with most existing applications, libraries and device d More

  Sensor node operating system provides a limited number of common services for developers to construct applications for wireless sensor networks. The sensor network community selected TinyOS as the de facto standard with most existing applications, libraries and device drivers available for TinyOS. The programming model of TinyOS is event-based and is not easy to use. In this paper, we present a new task scheduler for TinyOS that includes a new computation concept, named Job. Jobs are a collaborative and non-preemptive way of multitasking. On the next step, we propose a programming model which combines the asynchronous basis of event-driven systems with a more classical programming interface for the developer. As a result, developer that uses such an interface in his application will be provided with the sequential view we wanted. This programming model is suitable for applications that have long running computations and there is a data flow dependency between different tasks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  191 - Training of MLP Neural Network for Data Classification by GSA Method
  M. Dehbashian Seyed-Hamid Zahiri
  Nowadays, several techniques have presented for data classification. One of these techniques is neural network that has attracted many interests. In this classifier, selection a suitable learning method is very important for training of the network. Error back propagati More

  Nowadays, several techniques have presented for data classification. One of these techniques is neural network that has attracted many interests. In this classifier, selection a suitable learning method is very important for training of the network. Error back propagation is the most usual training method of neural networks that late convergence and stopping in local optimum points are its weakness. New approach in neural networks training is the usage of heuristic algorithms. This paper suggests a new learning method namely gravitational search algorithm (GSA) in training of neural network for data classification. GSA method is the latest and the most novel version of swarm intelligence optimization methods. This algorithm is inspired fby the law of Newtonian gravity and mass concept in nature. In this paper, a MLP neural network is trained for classification of five benchmark data set by GSA method. Also, the proposed method efficiency in training and testing of neural network compared with those of two training methods error back propagation and particle swarm optimization. Final results showed the GSA method extraordinary performance for data correct classification in most of cases. Also, in these experiments the GSA method produced stable results in all of cases. In addition, the run time of GSA method is shorter than that of the PSO. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  192 - Improving Pose Manifold and Virtual Images Using Bidirectional Neural Networks in Face Recognition Using Single Image per Person
  F. Abdolali S. A. Seyed Salehi
  In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a si More

  In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a single stage, but via two bottom-up and top-down phases and the recognition results of first stage is used for model adaptation. We have applied this novel adapting model in combination with clustering person and pose information technique to separate person and pose information and to estimate corresponding manifolds. To increase the number of training samples in the classifier neural network, virtual views of frontal images in the test dataset are synthesized using estimated manifolds. Training classifier network via virtual images obtained from bidirectional network, gives an accuracy rate of 85.45% on the test dataset which shows 1.82% improvement in accuracy of face recognition compared to training classifier with virtual images obtained from clustering person and pose information network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  193 - Neural Control of the Induction Motor Drive: Robust Against Rotor and Stator Resistances Variations and Suitable for Very Low and High Speeds
  H. Moayedi Rad M. A. Shamsi-Nejad mohsen Farshad
  In this paper, induction motor speed control drive is designed with application two multilayer feed-forward neural networks. That those are used one for generate PWM pulse and other for estimation of required torque and flux information. For trained of the PWM wave gene More

  In this paper, induction motor speed control drive is designed with application two multilayer feed-forward neural networks. That those are used one for generate PWM pulse and other for estimation of required torque and flux information. For trained of the PWM wave generate neural network is used from compound information two voltage and current classic model. Also, against general classic models for generate of the switching pulses is used as compound from reference voltage and current two motor phases. With these ideas are eliminated problems of the voltage and current classic models (flux saturation in current model for high speeds and voltage drop in voltage model for low speeds). As voltage profile is improved in this paper. The required feedback signals estimation (including: rotor flux, torque, etc.) is estimated by multilayer feed-forward neural network. That for robustness of the above estimator against rotor and stator resistances variations in time work of motor is used from compound trained data of the voltage and current classic models, because the voltage and current of the general classic models to sequence are independent of rotor and stator resistances. The simulation results by MATLAB-Simulink verify the proposed drive in improvement of the speed profile in transient and steady-state operating modes. Also, it verify clearly robust of the proposed drive against rotor and stator resistances variations in time work. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  194 - Stegananalysis Method Based on Co-Occurrence Matrix and Neural Network
  S. Ghanbari N. Ghanbari M. Keshtgari S. H. Nabavi Karizi
  Steganography is the art of hidden writing and secret communication. The goal of steganography is to hide the presence of information in other information. steganalysis is the art and science of detecting messages hidden using steganography. Co-occurrence matrix is the More

  Steganography is the art of hidden writing and secret communication. The goal of steganography is to hide the presence of information in other information. steganalysis is the art and science of detecting messages hidden using steganography. Co-occurrence matrix is the matrix containing information about the relationship between values of adjacent pixel in an image. In this paper, we extract features from Gray Level C0-occurrense Matrix (GLCM) that are difference between cover image (image without hidden information) and stego image (image with hidden information). In the proposed algorithm, first, we use a combined method of steganography based on both location and conversion to hide the information in the image. Then, using GLCM matrix properties, we investigate some difference values in the GLCM of the cover and stego images. We can extract features that were different between cover and stego images. Features are used for training neural network. This algorithm was tested on 800 standard image databases and it can detect 83% of stego images. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  195 - A Comprehensive Method to Secure Time Synchronization in Wireless Sensor
  Z. Ahmadi  
  One of the important requirements of sensor networks is synchronization of the nodes. The importance of time in sensor networks causes the adversary tries to disturb time synchronization by altering and faking messages, delaying or replying them, compromising the nodes More

  One of the important requirements of sensor networks is synchronization of the nodes. The importance of time in sensor networks causes the adversary tries to disturb time synchronization by altering and faking messages, delaying or replying them, compromising the nodes and sending false messages via them. Up to now, there is no method that is able to provide both synchronization and security needs of sensor networks simultaneously. In this paper, we suggest a method that is capable to provide precise synchronization, along with low communication and computational overhead, low convergence time and high security against internal and external attacks. Simulation and analytic results show the preference of our method compared to other available methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  196 - Modeling and Analysis Iterated Prison Dilemma Game by Grossberg Counter-Propagation Neural Network
  Gh. A. Montazer N. Rastegar Ramshe Alireza Askarzadeh
  Most of the time effective decisions in strategic situations such as competitive issues require a non-linear mapping between stimulus and response. Artificial neural networks can be an appropriate way for modeling and solving these kinds of problems. Prison Dilemma Game More

  Most of the time effective decisions in strategic situations such as competitive issues require a non-linear mapping between stimulus and response. Artificial neural networks can be an appropriate way for modeling and solving these kinds of problems. Prison Dilemma Game is a well-known game that is proposed in game theory. This paper tries to describe how using neural network, the iterated prisoner’s dilemma game can be modeled and analyzed. To do this a Grossberg Counter-Propagation Neural Network (GCP-NN) has been designed to play this game. Results show the capability of this method in complete modeling game. The results present the efficiency of the new method in comparison with the two conventional methods: Tit For Tat (TFT) strategy and Perceptron modeled game. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  197 - Designing a Self-Tuning Frequency Controller Based on ANNs for an Isolated Microgrid
  F. Habibi H. Bevrani J. Moshtag
  Increasing electrical energy demand, as well as fossil fuel shortages and environmental concerns have caused to use uncommon sources such as distributed generations (DGs) and renewable energy sources (RESs) into modern power systems. A microgrid (MG) system consists of More

  Increasing electrical energy demand, as well as fossil fuel shortages and environmental concerns have caused to use uncommon sources such as distributed generations (DGs) and renewable energy sources (RESs) into modern power systems. A microgrid (MG) system consists of several DGs and RESs which is responsible to provide both electrical and heat powers for local loads. Due to the MGs nonlinearity/complexity which is imposed to the conventional power systems, classical and nonflexible control structures may not represent desirable performance over a wide range of operating conditions. Therefore, more flexible/intelligent control methods are needed most of the past. Hence, in this paper addresses to design an online/self-tuning PI-controller based on artificial neural networks (ANNs) for optimal regulating the MG systems frequency. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  198 - Fuzzy Voting for Anomaly Detection in Cluster-Based Mobile Ad Hoc Networks
  Mohammad Rahmanimanesh Saeed Jalili
  In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal More

  In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal behavior of AODV protocol are defined via step by step analysis of AODV protocol and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy voting method is used for combining support vector data description (SVDD), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classifiers and the combined model is utilized to partially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and final decision on attack detection is carried out in the cluster head. In the proposed method, a fuzzy voting method is used for aggregating the votes of cluster members in the cluster head by which the performance of the method improves significantly in detecting blackhole, rushing, route error fabrication, packet replication, and wormhole attacks. In this paper, an attack analysis method based on feature sensitivity ranking is also proposed that determines which features are influenced more by the mentioned attacks. This sensitivity ranking leads to the detection of the types of attacks launched on the network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  199 - Electrical Islanding Detection in Electrical Distribution Networks with Distributed Generation Using Discrete Wavelet Transform and Artificial Neural Network
  M. Heidari Orejloo S. Gh. Seifossadat M. Razaz
  In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" o More

  In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" or "non-islands". Needed features for classification are extracted by DWT of DG transient voltage signal. DIgSILENT, MATLAB and WEKA softwares are used for simulation. Proposed method is tested on a CIGRE medium voltage distribution system with two different types of DGs. The final method is chosen from among 162 relay projects with respect to different criteria, including accuracy, speed, simplicity and cost efficiency is the best. With The analysis done in the best relay selection for DGs, the voltage signal, the mother wavelet db4 and seventh level wavelet transform are used. Simulation results show that this method in compared with existing methods, can detect the electrical islands, with a shorter time and higher accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  200 - Design Improvement of Synchronous Reluctance Motor Geometry, Using Neural-Network, Genetic Algorithm and Finite Element Method
  M. Haghparast S. Taghipour Boroujeni A. Kargar
  appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In More

  appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In this paper an intelligent- method have been used to optimizing the design of SynRel motors based on magnetic saliency ratio. To achieve this aim, all of the motor parameters including stator geometry, axial length of machine, winding type, and number of flux barriers in rotor are assumed constant and just position of the rotor flux barriers are optimized. These positions have been defined by six parameters. Changing these parameters, the magnetic saliency of machine is calculated by finite element analysis (FEA). Using these values to train a neural network (NN), a modeling function is obtained for magnetic saliency of SynRel machine. Considering this NN as the target function in genetic algorithm (GA), the parameters of SynRel machine have been optimized and the best rotor structure with highest magnetic saliency has been obtained. Finally the abilities of NN in correct estimation of magnetic saliency and motor synchronization were approved by FEA and dynamic simulation. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  201 - Bayesian Network Parameter Learning from Data Contains Missing Values
  K. Etminani M. Naghibzadeh M. Emadi A. R. Razavi
  Learning Bayesian network structure from data has attracted a great deal of research in recent years. It is shown that finding the optimal network is an NP-hard problem when data is complete. This problem gets worse when data is incomplete i.e. contains missing values a More

  Learning Bayesian network structure from data has attracted a great deal of research in recent years. It is shown that finding the optimal network is an NP-hard problem when data is complete. This problem gets worse when data is incomplete i.e. contains missing values and/or hidden variables. Generally, there are two cases of learning Bayesian networks from incomplete data: in a known structure, and unknown structure. In this paper, we try to find the best parameters for a known structure by introducing the “effective parameter”, in a way that the likelihood of the network structure given the completed data being maximized. This approach can be attached to any algorithm such as SEM (structural expectation maximization) that needs the best parameters to be known to reach the optimal Bayesian network structure. We prove that the proposed method gains the optimal parameters with respect to the likelihood function. Results of applying the proposed method to some known Bayesian networks show the speed of the proposed method compared to the well-known methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  202 - Phrase Segmentation on Persian Texts Using Neural Networks
  M. M. Mirdamadi A. M. Zareh Bidoki M. Rezaeian
  Word and phrase segmentation is one of the main activities in natural languages processing (NLP). Many programs in NLP need to be preprocessed for extraction of text’s words and distinction phrases. Getting meaningful words with their prefix and suffix is the main and t More

  Word and phrase segmentation is one of the main activities in natural languages processing (NLP). Many programs in NLP need to be preprocessed for extraction of text’s words and distinction phrases. Getting meaningful words with their prefix and suffix is the main and the final goal of segmentation. This activity depends on various natural languages can be easy or hard. Persian is among the languages with complex preprocessing tasks. One of the complexity sources is handling different writing scripts. In written Persian texts, we have two kinds of spaces: short space and white space. Also there are various scripts for writing Persian texts, differing in the style of writing words, using or elimination of spaces within or between words, using various forms of characters and so on. In this paper, we want to suggest a statistical method for phrase segmentation on Persian texts using neural networks due to using in search engines. For this purpose, we use occurrence likelihood of uniwords and biwords in corpus. The suggested algorithm includes four steps and could detect about 89.6% of correct tokens. Experimental results show this method can improve the performance of the usual methods Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  203 - Insurance Contract for the Transfer of Electrical Energy, an Incentive Method to Improve the Reliability
  A. Khandani A. Akbari Forod
  In competitive electricity market, maximizing the profit is the main objective in company’s decision making. Hence, Transmission companies (Trans Cos) are not interested in improving reliability and expanding existing structures without financial benefits. On the other More

  In competitive electricity market, maximizing the profit is the main objective in company’s decision making. Hence, Transmission companies (Trans Cos) are not interested in improving reliability and expanding existing structures without financial benefits. On the other hand, consumers demand more reliable and high quality power. In this paper, transmission insurance plan is proposed as an incentive method to improve the reliability of electrical power transmission. In this method, an insurance contract concluded between insurance company and every customer. Insurance company spend a part of its revenue to increases the reliability of the transmission system and also pays for compensation of consumers not supplied energies. The proposed method is studied in a network with six buses. Results show that the proposed method increases network reliability. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  204 - A Hybrid Algorithm for Terrain Simplification
  F. Dabaghi Zarandi Mohammad Ghodsi
  Terrain simplification problem is one of fundamental problems in computational geometry and it has many applications in other fields such as geometric information systems, computer graphics, image processing. Terrain is commonly defined by a set of n points in three dim More

  Terrain simplification problem is one of fundamental problems in computational geometry and it has many applications in other fields such as geometric information systems, computer graphics, image processing. Terrain is commonly defined by a set of n points in three dimension space. Major goal of terrain simplification problem is removing some points of one terrain so that maximum error of simplified surface is a certain threshold. There are two optimization goals for this problem: (1) min-k, where for a given error threshold , the goal is to find a simplification with the minimum number of points for which the error is that most , and (2) min-, where for a given number n, the goal is to find a simplification of at most m points that has the minimum simplification error. Simplification problem is NP-hard in optimal case. In this paper we present a hybrid algorithm for terrain simplification that performs in three phases. First, terrain is divided to some clusters, then any cluster is simplified independently and finally, the simplified clusters are merged. Our algorithm solves the problem in . The proposed algorithm is implemented and verified by experiments. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  205 - Throughput Optimization in a Broadcast Network Using Adaptive Modulation, Coding and Transmit Power Provisioning Security Constraint
  M. Taki
  A new transmission scheme is presented to improve utilization of resource in a broadcast network provisioning physical layer security. In the designed scheme, data of each user is only detectable at its corresponding receiver with a proper bit error rate (BER), while de More

  A new transmission scheme is presented to improve utilization of resource in a broadcast network provisioning physical layer security. In the designed scheme, data of each user is only detectable at its corresponding receiver with a proper bit error rate (BER), while detection BER at other unintended receivers is high enough for improper detection. Adaptive modulation, coding and transmit power is utilized based on the SNRs. Exact and approximate solutions for the formulated problem are presented where approximate solution has acceptable complexity and leads to the comparable results with the exact solution. Numerical evaluations show that a performance degradation is seen at the cost of providing security. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  206 - Composite Power System Reliability Modeling, Evaluation and Reliability-Based Analysis by Bayesian Networks
  M. Eliassi H. Seifi  
  Bayesian Networks (BNs) as a strong framework for handling probabilistic events have been successfully applied in a variety of real-world problems, but they have received little attention in the area of composite power systems reliability assessment. Reliability assessm More

  Bayesian Networks (BNs) as a strong framework for handling probabilistic events have been successfully applied in a variety of real-world problems, but they have received little attention in the area of composite power systems reliability assessment. Reliability assessment by BN provides some additional capabilities in comparison to conventional methods, both at the modeling and at the analysis levels. At the modeling level, several restrictive assumptions, implicit in the conventional methods, can be removed. At the analysis level, a variety of applicable reliability-based analysis which is hardly achievable in conventional methods, can be conveniently performed. This paper proposes a methodology based on Minimal Cutsets (MCs) to apply BNs to composite power system reliability modeling, reliability assessment and reliability-based analysis. To have a more accurate BN model, a new method of MC determination for composite power system is proposed. Bayesian structure is extracted, based on the determined MCs. Bayesian parameters are defined based on the logical relationships of nodes. To make the proposed method applicable to large composite power systems, virtual nodes are proposed and combined with Bayesian model. Also, a variety of reliability-based analyses are presented which are hardly achievable in conventional methods. The proposed method is validated by applying to RBTS and comparing the results with other reliability analysis methods. The proposed methodology is applied to the Reliability Test System (RTS), to show its feasibility in large networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  207 - A New Extended Distributed Learning Automata-Based Algorithm for Parameter Learning of a Bayesian Network
  M. R. Mollakhalili Meybodi M. R. Meybodi
  In this paper a new learning automata-based algorithm is proposed for learning of parameters of a Bayesian network. For this purpose, a new team of learning automata which is called eDLA is used. In this paper the structure of Bayesian network is assumed to be fixed. Ne More

  In this paper a new learning automata-based algorithm is proposed for learning of parameters of a Bayesian network. For this purpose, a new team of learning automata which is called eDLA is used. In this paper the structure of Bayesian network is assumed to be fixed. New arriving sample plays role of the random environment and the accuracy of the current parameters generates the random environment reinforcement signal. Linear algorithm is used to update the action selection probability of the automata. Another key issue in Bayesian networks is parameter learning under circumstances that new samples are incomplete. It is shown that new proposed method can be used in this situation. The experiments show that the accuracy of the proposed automata based algorithm is the same as the traditional enumerative methods such as EM. In addition to the online learning characteristics, the proposed algorithm is in accordance with the conditions in which the data are incomplete and due to the use of learning automaton, has a little computational overhead. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  208 - Topology Control in Wireless Sensor Networks Using Two-Level Fuzzy Logic
  A. Abdi Seyedkolaei A. Zakerolhosseini
  Wireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy More

  Wireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy consumption and increasing network lifetime. Topology control is one of the methods to reduce energy consumption and increase the lifetime of the network. Since different methods of topology control, to reduce energy consumption and enhance the network lifetime is proposed that including them is the clustering and one of the most famous clustering methods is LEACH. In this paper, we try to present a new clustering method that is superior compared to leach and other improved methods after the LEACH. we use in our clustering method from two-level fuzzy logic that be causing reduce energy consumption and increase the network lifetime compared to other methods and to prove the superiority of our method compared with other methods, we present a comparison using MATLAB software. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  209 - PLAER: Penalty Base Learning Automata for Energy Aware Routing in WSN
  M. Parvizi Omran A. Moeni H. Haj Seyyed Javadi
  Sensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys L More

  Sensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys Learning automata in each node to find a suitable path for routing data packets. In order to aim this goal the algorithm uses penalty based approach in learning automata and considers energy level of nodes and latency of packet delivery as well. Performance of our new developed algorithm has been compared with LABER and BEAR protocols in OMNET++ simulator. Simulation results show that, in a network with static nodes, energy consumption and control packets reduce significantly and network lifetime increases in comparison with two other protocols. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  210 - Simulation of Electrical Fault in Stator Winding of Permanent Magnet Synchronous Motor and Discriminating It from Other Possible Electrical Faults Using Probabilistic Neural Network
  M. Taghipour-gorjikolaie S. M. Razavi M. A. Shamsi-Nejad
  One of the most common electrical faults in Permanent Magnet Synchronous Motor (PMSM) is inter-turn fault in stator winding. At the incipient steps it seems not dangerous and so light, but spreading this fault can leads to irreparable Consequences. In this paper, the in More

  One of the most common electrical faults in Permanent Magnet Synchronous Motor (PMSM) is inter-turn fault in stator winding. At the incipient steps it seems not dangerous and so light, but spreading this fault can leads to irreparable Consequences. In this paper, the intelligent system is presented to protect PMSMs from this kind fault. At the first, intelligent protection system determine the condition of the motor (which can be: Normal, Phase-phase short circuit, Open circuit and Inter-turn fault conditions). If the system determines the faults then send an alarm to operator and also if the fault is inter-turn, it can determine the damaged phase. Obtaining results show that Probabilistic Neural Network can be the most reliable and robust protection system for PMSMs against internal faults, especially inter-turn faults. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  211 - Face Detection Using Gabor Filters and Neural Networks
  M. Mahlouji R. Mohammadian
  In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective pa More

  In this paper, a robust method for face detection from different views using a combination of Gabor filters and neural networks is presented. First, a mathematical equation of Gabor filter is expressed. Then, by examining 75 different filter banks, range of effective parameters values in Gabor filter generation is determined, and finally, the best value for them is specified. The neural network used in this paper is a feed-forward back-propagation multilayer perceptron network. The input vector of the neural network is obtained from the convolution the input image and a Gabor filter with angles π / 2 and the frequency π / 2 in the frequency domain. The proposed method has been tested on 550 image samples from Feret database with simple background and Markus Weber database with complex background, and detection accuracy of them is 98.4% and95%, respectively. Also, the face area has been detected using Viola-Jones algorithm, and then comparison between the results obtained from Viola-Jones algorithm and the proposed method is described. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  212 - Design of a CDS Backbone Based Wireless Mesh Network Energy Aware Routing Method for Maximizing Lifetime
  A. Shafaroudi S. V. Azhari
  In many applications, wireless mesh networks work by battery as a power source. In this scenario, routing method has a great impact on the network lifetime. In this research a new backbone based wireless mesh network routing method for maximizing lifetime has been prop More

  In many applications, wireless mesh networks work by battery as a power source. In this scenario, routing method has a great impact on the network lifetime. In this research a new backbone based wireless mesh network routing method for maximizing lifetime has been proposed. This approach is compatible with the features provided by IEEE standard for wireless mesh networks. In this method, backbone routers are selected based on the maximum remaining energy. The proposed algorithm is compared with optimum and shortest path routing methods. Simulation results show acceptable increase in network lifetime in the proposed approach. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  213 - Evaluation of Fuzzy-Vault-based Key Agreement Schemes in Wireless Body Area Networks Using the Fuzzy Analytical Hierarchy Process
  M. Ebrahimi H. R. Ahmadi M. Abbasnejad Ara
  Wireless body area networks (WBAN) may be deployed on each person’s body for pervasive and real time health monitoring. As WBANs deal with personal health data, securing the data during communication is essential. Therefore, enabling secure communication in this area ha More

  Wireless body area networks (WBAN) may be deployed on each person’s body for pervasive and real time health monitoring. As WBANs deal with personal health data, securing the data during communication is essential. Therefore, enabling secure communication in this area has been considered as an important challenge. Due to the WBAN characteristics and constraints caused by the small size of the nodes, selection of the best key agreement scheme is very important. This paper intends to evaluate different key agreement schemes in WBANs and find the best one. To achieve this goal, three schemes from existing research named OPFKA, PSKA and ECG-IJS are considered and a fuzzy analytical hierarchy process (FAHP) method is employed to find the best scheme. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  214 - A New Unified Power Quality Conditioner based on Trans-Z-Source Inverter
  M.  Siahi Mohammad Davoodi
  Unified Power quality conditioner (UPQC) consists of back to back voltage source inverters. In UPQC combination of parallel and series active power filters are used to compensate for the nonlinear load current harmonics and voltage distortions, simultaneously. For appro More

  Unified Power quality conditioner (UPQC) consists of back to back voltage source inverters. In UPQC combination of parallel and series active power filters are used to compensate for the nonlinear load current harmonics and voltage distortions, simultaneously. For appropriate performance of both converters and bidirectional power flow, the DC link voltage should be at least 1.41 times larger than the line to line voltage in the high voltage part of the system; i.e. the parallel active filter. One of the determining factors for the cost of semiconductors is the maximum tolerable voltage stress. The voltage stress of the series converter increases when the DC link voltage is high. In order to overcome this deficiency, a Z-source network is added to the common structure of back to back invertors in the UPQC. It will reduce the applied DC voltage to the series active power filters significantly and decrease the cost of manufacturing. In this structure, an impedance source network is used in an AC/DC inverter to produce a buck-boost effect. Additionally, dead time has been eliminated through the use of a Z source network in the parallel active filter and thus its performance and reliability has increased impressively. In this paper, a comparison study has been conducted through necessary simulations for the performance evaluation of the common and proposed structures. The total switching device power has been used as a criteria to confirm the manufacturing cost reduction in the proposed structure. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  215 - New Beamforming Algorithm for Cooperative Networks with Multiple Antenna Decode and Forward Relay
  Mohammad Mohammadi Amiri A. Olfat
  In this paper, a cooperative network consisting of one source, one relay, and one destination is considered. The source and the destination are both single-antenna systems, while the relay is equipped with N antennas and operates in decode-and-forward (DF) mode. We assu More

  In this paper, a cooperative network consisting of one source, one relay, and one destination is considered. The source and the destination are both single-antenna systems, while the relay is equipped with N antennas and operates in decode-and-forward (DF) mode. We assume that there is no direct link between the source and the destination. We propose two beamforming methods at the relay to transmit the data to the destination. Beamforming is performed at the relay by the assumption of having two bit quantized information about the phase of all links between the relay and the destination. We derive an upper bound on bit error probability of the system and show that the proposed scheme achieves full diversity order. Simulation results illustrate that performance of the system in terms of bit error probability is better than some well-known scenarios and is close to some scenarios with ideal assumptions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  216 - Leaning the Structure of Bayesian Networks Using Learning Automata
  M. R. Mollakhalili Meybodi M. R. Meybodi
  The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically lea More

  The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal in sights into its underlying causal structure. Automatically learning the graph structure of a Bayesian network is a challenge pursued within artificial intelligence studies. In this paper, a new algorithm based on learning automata is proposed for learning the structure of the Bayesian networks. In this algorithm, automata is used as a tool for searching in structure’s space (DAG’s space) of the Bayesian networks. The mathematical behavior of the proposed algorithm is studied. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  217 - Application of Wide-Area Synchrophasor Measurement System to Alleviate Blackouts by Rotor Angle Instability
  S. Kiarostami S. Kiarostami
  In this paper, a Wide-Area protection system to deal with rotor angle instabilities is proposed. Firstly, a system blackout model is developed and secondly the extreme contingencies that lead to large blackouts are extracted. Initiating events that ultimately lead to ro More

  In this paper, a Wide-Area protection system to deal with rotor angle instabilities is proposed. Firstly, a system blackout model is developed and secondly the extreme contingencies that lead to large blackouts are extracted. Initiating events that ultimately lead to rotor angle instabilities are determined by artificial neural network (ANN). Coherent generators are detected by an algorithm using the data presented by phasor measurement units (PMUs). Based on identification of coherent generators, the power system is split into stable islands by disconnecting the weak interconnecting lines and load shedding. The performance of the proposed strategy is verified by simulations on the IEEE 39-bus sample power system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  218 - A Goal-Based Approach for the Holonification of Holonic Multi-Agent Systems
  Ahmad Esmaeili N. Mozayani M. R. Jahed Motlagh
  Holonic structures are a hierarchical formation of holons that are developed and used for the purpose of restricting interaction domains, reducing uncertainty, or forming the high level goals of multi-agent systems, in such a way that the system benefits a high degree o More

  Holonic structures are a hierarchical formation of holons that are developed and used for the purpose of restricting interaction domains, reducing uncertainty, or forming the high level goals of multi-agent systems, in such a way that the system benefits a high degree of flexibility and dynamism in response to environmental changes. Although the holonic multi-agent systems are extensively used in modeling and solving complex problems, most of its prerequisites, like forming the body holons and dynamically controlling its structure, use very simple application-specific models. This is due to the immaturity of the research literatures in this field. In this article, an endeavor is made to propose a goal-based approach for the formation of holonic structures, using the concepts in social science and organizational theory. The use of concepts like role, skill, and goal structures, makes the proposed method possible to be used in wide range of applications. In order to demonstrate the capabilities of the method and also the way it can be applied in real world problems, a test bed based on the application of wireless sensor networks in object tracking is designed and presented. In this application, the sensors, which are distributed in the environment as simple agents, using holonic structures, are responsible for the track of any alien objects that enter and move in the environment. According to the empirical results of the simulations, the proposed holonic approach has provided successful performance in terms of tracking quality and energy consumption of the sensors. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  219 - A Novel Energy-Efficient Algorithm to Enhance Load Balancing and Lifetime of Wireless Sensor Networks
  S. Abbasi-Daresari J. Abouei
  Wireless senor networks (WSNs) are widely used for the monitoring purposes. One of the most challenges in designing these networks is minimizing the data transmission cost with accurate data recovery. Data aggregation using the theory of compressive sampling is an effec More

  Wireless senor networks (WSNs) are widely used for the monitoring purposes. One of the most challenges in designing these networks is minimizing the data transmission cost with accurate data recovery. Data aggregation using the theory of compressive sampling is an effective way to reduce the cost of communication in the sink node. The existing data aggregation methods based on compressive sampling require to a large number of nodes for each measurement sample leading to inefficient energy consumption in wireless sensor network. To solve this problem, we propose a new scheme by using sparse random measurement matrix. In this scheme, the formation of routing trees with low cost and fair distribution of load on the network significantly reduces energy consumption. Toward this goal, a new algorithm called “weighted compressive data gathering (WCDG)” is suggested in which by creating weighted routing trees and using the compressive sampling, the data belong to all of nodes of each path is aggregated and then, sent to the sink node. Considering the power control ability in sensor nodes, efficient paths are selected in this algorithm. Numerical results demonstrate the efficiency of the proposed algorithm with compared to the conventional data aggregation schemes in terms of energy consumption, load balancing, and network lifetime. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  220 - A New Method for Supply Reliability Assessment in Industrial Microgrids Considering Load Growth and Renewable Resources Uncertainty
  S. Rahimi Takami R. Hooshmand A. Khodabakhshian A. Khodabakhshian
  Distributed Generation (DG) resources can effect a lot on the reliability parameters in industrial microgrids. So, reliability evaluation of industrial microgrids is presented in this paper using a proposed composite index in the presence of DG resources and demand resp More

  Distributed Generation (DG) resources can effect a lot on the reliability parameters in industrial microgrids. So, reliability evaluation of industrial microgrids is presented in this paper using a proposed composite index in the presence of DG resources and demand response (DR). This procedure of the reliability assessment is based on sequential Monte Carlo method with respect to the time varying load model. In this paper, wind and photovoltaic generations those are useful renewable generations are used. Since, the output power of these DGs depends on wind speed and solar radiation that are stochastic variables, therefore a number of scenarios have been considered in order to determine the output power per hour for each of them. According to the large number of generated scenarios, scenario reduction method is used based on two conditions that consist of power generation of DGs and load. Here the new composite index represents changes in the SAIFI, SAIDI and EENS indices per each KW of installed DGs. With considering to industrial load growth in the microgrid, a ten-year period is studied and the scheduling is performed in both islanding and grid connected operational modes. The concept of DR is also used in the islanding operational mode. To demonstrate the effectiveness of the proposed method, the approach is applied on a standard IEEE RBTS BUS2 system in the presence of DG resources and the results in different conditions are achieved. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  221 - Temperature Management in 3D Network-on-Chips Using Simulated Annealing-Based Task Migration
  M. Mohebbi Moghaddam S. H. Mir Mohammadi S. H. Mir Mohammadi
  Combination of 3D stacking and network-on-chip (NoC), known as 3D NoC, has several advantages such as reduced propagation delay, chip area and interconnect, and power consumption, and bandwidth increase. Despite these advantages, 3D stacking causes the increased power d More

  Combination of 3D stacking and network-on-chip (NoC), known as 3D NoC, has several advantages such as reduced propagation delay, chip area and interconnect, and power consumption, and bandwidth increase. Despite these advantages, 3D stacking causes the increased power density per chip area and subsequently increases the chip temperature. Temperature increase causes performance degradation and reliability reduction. Therefore, design of temperature management algorithms is essential for these systems. In this paper, we propose a task migration scheme for thermal management of 3D NoCs. The process of migration destinations for hot spots is an NP-complete problem which can be solved by using heuristic algorithms. To this end, we utilize a simulated annealing method in our algorithm. We consider migration overhead in addition to the temperature of the processing elements in migration destination selection process. Simulation results indicate up to 28 percentage peak temperature reduction, on average, for the benchmark that has the largest number of tasks. The proposed scheme has low migration overhead. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  222 - Weighted Multi-Level Fuzzy Min-Max Neural Network
  R. Davtalab M. A. Balafar M. R. Feizi-Derakhshi
  In this paper a weighted Fuzzy min-max classifier (WL-FMM) which is a type of fuzzy min-max neural network is described. This method is a quick supervised learning tool which capable to learn online and single pass through data. WL-FMM uses smaller size with higher weig More

  In this paper a weighted Fuzzy min-max classifier (WL-FMM) which is a type of fuzzy min-max neural network is described. This method is a quick supervised learning tool which capable to learn online and single pass through data. WL-FMM uses smaller size with higher weight to manipulate overlapped area. According to experimental results, proposed method has less time and space complexity rather than other FMM classifiers, and also user manual parameters has less effect on the results of proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  223 - Improving Target Coverage in Visual Sensor Networks by Adjusting the Cameras’ Field-of-View and Scheduling the Cover sets Using Simulated Annealing
  B. Shahrokhzadeh M. Dehghan M. R. Shahrokhzadeh
  In recent years, target coverage is one of the important problems in visual sensor networks. An efficient use of energy is required in order to increase the network lifetime, while covering all the targets. In this paper, we address the Maximum Lifetime with Coverage Sc More

  In recent years, target coverage is one of the important problems in visual sensor networks. An efficient use of energy is required in order to increase the network lifetime, while covering all the targets. In this paper, we address the Maximum Lifetime with Coverage Scheduling (MLCS) problem that maximizes the network lifetime. We develop a simulated annealing (SA) algorithm that divides the sensors’ Field-of-View (FoV) to a number of cover sets that can cover all the targets and then applies a sleep-wake scheduling algorithm. On the other hand, we have to identify the best possible FoV of sensors according to the targets’ location using rotating cameras, to reduce the solution space and find a near-optimal solution. It also provides the balanced distribution of energy consumption by introducing a new energy and neighbor generating function as well as escaping from local optima. Finally, we conduct some simulation experiments to evaluate the performance of our proposed method by comparing with well-known solutions in the literature such as greedy algorithms. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  224 - EBONC: A New Energy-Aware Clustering Approach Based on Optimum Number of Clusters for Mobile Wireless Sensor Networks
  N. Norouzy N. Norouzy M. Fazlali
  The energy constraint is one of the key challenges in wireless sensor networks that directly affects the network lifetime. Clustering the sensor nodes is one of the possible approaches to improving the energy efficiency by uniformly distributing the energy consumption a More

  The energy constraint is one of the key challenges in wireless sensor networks that directly affects the network lifetime. Clustering the sensor nodes is one of the possible approaches to improving the energy efficiency by uniformly distributing the energy consumption among the nodes. The number of appropriate clusters plays an important role in the network throughput. A Large number of clusters imply that packets pass more hops to reach the destination, which results in higher energy consumption. In this paper, we devise an energy and location aware clustering scheme that tries to optimize the number of required clusters. Moreover, the cluster heads are chosen according to their energy levels. The devised scheme partitions the network into concentric circles and calculates the appropriate number of clusters to provide an energy efficient network. A gossiping approach is used to provide information exchange mechanism. The performance of the devised approach is compared with ASH scheme. The simulation results show the network lifetime is improved from 25% to 40% in difference network scenarios. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  225 - Fault Location in Distribution Networks Using a Combination of Impedance Base Method and Voltage Sag
  Mohammad Daisy R. Dashti
  Load taps, laterals, and sub laterals are different branches of power distribution (PD) networks. In PD systems, reliability indices and their efficiency are improved using an accurate method in fault locating. In this paper, a new combined method for locating single, d More

  Load taps, laterals, and sub laterals are different branches of power distribution (PD) networks. In PD systems, reliability indices and their efficiency are improved using an accurate method in fault locating. In this paper, a new combined method for locating single, double and three phase faults to ground is proposed in PD networks. In this article, for finding the possible fault locations, an impedance based fault-location algorithm is used. Then, for determining the faulty section, the new method is proposed using voltage sag matching algorithm. In this method, the possible fault locations are determined, after occurrence of single and double phase faults to ground, using an algorithm which is impedance based fault-location algorithm. Separately, the same fault is simulated in possible locations. Then, at the beginning of a feeder, the voltage is saved and the amplitude and angle of the voltage differences are determined and accordingly, an online data bank is generated. Then, the obtained and recorded amplitude and angle of the voltage differences (at the beginning of the feeder) is compared with that data bank, for the actual fault. By the matching value of each possible fault location, the real location of fault is determined. Compared to the other counterparts, the proposed method is more accurate in locating faults and less sensitive to the fault resistance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  226 - Optimization of Adaptive Design of Wireless Sensor Networks Using Binary Quantum-Inspired Gravitational Search Algorithm
  M. Mirhosseini F. Barani H. Nezamabadi-pour
  In this paper, the binary quantum-inspired gravitational search algorithm is adapted to dynamically optimize the design of a wireless sensor network towards improving energy consumption and extending the lifetime of the network, so that the application-specific requirem More

  In this paper, the binary quantum-inspired gravitational search algorithm is adapted to dynamically optimize the design of a wireless sensor network towards improving energy consumption and extending the lifetime of the network, so that the application-specific requirements and communication constraints are fulfilled. The proposed approach is applied on a wireless sensor network used in the application of precise agriculture to monitor environmental conditions. This algorithm would present an optimal design detecting operational mode of each sensor including cluster head, high signal range, low signal range and inactive modes taking into consideration the constraints of the network. The simulation results indicate the most performance of the proposed method in comparison with binary genetic algorithm and particle swarm optimization. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  227 - An Efficient Method for Modulation Recognition of MPSK Signals in Fading Channels
  S. Hakimi
  Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. Howev More

  Automatic modulation recognition of digital signals is an essential for intelligent communication systems. Most automatic classifications of digital signal types deal with recognizing signals formats in presence of additive white Gaussian noise (AWGN) in channels. However, real world communication environments, such as wireless communication channels, suffer from fading effects. There are few methods proposed to perform in fading channels. This paper presents a high efficient method for identification of M-array phase shift keying (MPSK) digital signal type. The proposed method is heuristic hybrid, formed by a multilayer perceptron (MLP) neural network as the classifier and the bees algorithm (BA) as the optimizer. An equalizer is also used to reduce channel effects. A suitable combination of higher order statistics, up to eighth, is considered as prominent characteristics of signals. Simulation results validate the high efficiency of the proposed technique in recognizing the types of digital signals even at low SNRs. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  228 - Green Routing Protocol Based on Sleep Scheduling in Mobile Ad-Hoc Network
  Z. Movahedi A. Karimi
  Over recent years, green communication technology has been emerged as an important area of concern for communication research and industrial community. The reason of paying attention of this area is its effect on reducing environmental pollutions. According to recent re More

  Over recent years, green communication technology has been emerged as an important area of concern for communication research and industrial community. The reason of paying attention of this area is its effect on reducing environmental pollutions. According to recent research, a significant share of these pollutions is produced by the local area computer networks. A mobile ad-hoc network (MANET) is one of the widely used local area networks. The energy efficiency is important in MANETs not only from the green communication point of view, but also due to the network limitations in terms of battery lifetime. Of course, MANETs characterization such as distributed nature and lack of administration, nodes mobility, frequent topology changes and scare resources makes the greening trend a challenging task in such a context. In this paper, we propose and implement a green routing protocol for MANET which solves the idle energy consumption by allowing the necessary nodes and switching off the other un-utilized nodes. Simulation results show this can help to the 20 percentage of saving energy in the environment on average and also aware of the quality of service. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  229 - A Multi-Criteria Decision Making Mechanism for Data Offloading from Cellular Networks to Complementary Networks
  M. Fallah Khoshbakht saleh Yousefi B. Ghalebsaz Jeddi
  Due to proliferation of smart phones, data traffic in cellular networks has been significantly increasing, which has resulted in congestions in cellular networks. Data offloading to a complementary network such as Wi-Fi has been identified as a rational and cost-effecti More

  Due to proliferation of smart phones, data traffic in cellular networks has been significantly increasing, which has resulted in congestions in cellular networks. Data offloading to a complementary network such as Wi-Fi has been identified as a rational and cost-effective solution to these congestions. In this paper, a multi-criteria offloading (MCO) mechanism is proposed to select the best transfer mode among: cellular delivery, delay-tolerant offloading (DTO) to a complementary network, and peer-assisted offloading (PAO). The proposed MCO mechanism utilizes TOPSIS multi-criteria decision analysis method and a prediction model for the Wi-Fi connection pattern. The decision criteria include: the fraction of total users’ request satisfied by offloading, data transfer costs of cellular operator to users, data transfer bandwidth of users in both cellular and complementary networks, and total users’ power consumption. To evaluate the proposed mechanism various scenarios have been simulated, and the results show that the MCO mechanism can successfully take into account the preferences of the cellular operator and its users. Through simulations, the MCO mechanism demonstrated superior performance in comparison with other proposed solutions in the literature in terms of balancing the load on the network, reducing the cost of the cellular operator, and reducing energy consumption of the users. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  230 - Placement of AVRs and Reconfiguration of Distribution Networks Simultaneously and Robust Considering Load Uncertainty
  M. R.  Shakarami Y. Mohammadi Pour
  : In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive More

  : In this paper, optimal locating for AVRs and reconfiguration of distribution networks were assessed simultaneously as an optimization problem. A new objective function was introducing which incorporated several electrical indices including real power losses, reactive power losses, reliability, voltage profile, voltage stability, and load capacity of lines (MVA). Various load levels were incorporated into the objective function to make sure that switch status in reconfiguration and AVR taps and locations would be robust against load variations. This paper also introduced a new method for calculating the load levels with respect to load uncertainty. It also considered all loads based on a voltage-dependent model. Several scenarios are defined to thoroughly assess the proposed approach. Integer particle swarm optimization algorithm (IPSO) was used to solve the mentioned optimization problem. The results obtained by the simulation of 33-bus and 69-bus standard IEEE .radial power distribution networks demonstrated the effectiveness of the proposed approach Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  231 - Efficient Multicast Routing in Reconfigurable Networks-on-Chip
  F. Nasiri   Ahmad  Khademzadeh
  Several routing algorithms have been presented for multicast and unicast traffic in MPSoCs. Multicast protocols in NoCs are used for clock synchronization, cache coherency in distributed shared memory on-chip multiprocessors, replication and barrier synchronization. Uni More

  Several routing algorithms have been presented for multicast and unicast traffic in MPSoCs. Multicast protocols in NoCs are used for clock synchronization, cache coherency in distributed shared memory on-chip multiprocessors, replication and barrier synchronization. Unicast routing algorithms are not useful for multicast. Indeed, when unicast routing algorithms are employed to realize multicast operation, high traffic, congestion and deadlock are imposed to the network. To prevent from these problems, Tree-based and path based techniques have been proposed for multicast in multicomputers (and recently NoCs). In this paper, we present a new multicast routing method to decrease power consumption and multicast message latency based on a reconfigurable NoC architecture. In this line, we benefit from simple switches in our reconfigurable architecture instead of routers; we then divide the network to smaller partitions to make better trees for conducting multicast packets. Our evaluation results reveal that, for both real and synthetic traffic loads, the proposed method outperforms the baseline tree-based routing method in a reconfigurable mesh, and reduces message latency by up to 51% and power consumption by up to 33%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  232 - An Efficient Hybrid Routing Protocol in Underwater Wireless Sensor Networks
  J. Tavakoli N. Moghim
  Underwater Wireless Sensor Network (UWSN) is a kind of sensor networks that their operational fields have been developed under water in recent decades, although these networks deal with lots of challenges due to lack of the GPS1. These networks encounter researchers wit More

  Underwater Wireless Sensor Network (UWSN) is a kind of sensor networks that their operational fields have been developed under water in recent decades, although these networks deal with lots of challenges due to lack of the GPS1. These networks encounter researchers with many challenges by some limitations like high propagation delay, low bandwidth, high bit error rate, movement, limited battery and memory. In comparison with terrestrial sensor networks, sensors in the UWSN consume energy more because they use acoustic technology to communicate. Motivation of this research is proposing a routing protocol for underwater systematic settings with a limited energy. The settled sensor nodes in underwater cannot communicate directly with nodes near surface, so they need prepared multi hop communications with a proper routing plan. In wireless sensor networks, node clustering is a common way to organize data traffic and to decrease intra-network communications along with scalability and load balance improvement plus reducing of overall energy consumption of system. Therefore, in this article a fuzzy clustering routing protocol with data aggregation and balanced energy consumption for UWSNs is proposed. Simulation results show that in the proposed protocol, energy consumption becomes more uniformly distributed in the network and average of the nodes' energy usage and number of routing packets decreases and finally, packet delivery ratio and throughput are improved in the network in comparison with DABC3 and IDACB4 algorithms. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  233 - A New BGP-based Load Distribution Approach in Geographically Distributed Data Centers
  A. Esmaeili B. Bakhshi
  Today, hosting services in geographically distributed data centers is very common among service provider companies, because of more efficiency of energy consumption, high availability of the system, and providing quality of service. Load distribution is the main issue i More

  Today, hosting services in geographically distributed data centers is very common among service provider companies, because of more efficiency of energy consumption, high availability of the system, and providing quality of service. Load distribution is the main issue in the geographical data centers. On the one hand, there are several architectures to distribute load between different clusters, e.g., central load balancer, DNS-based systems, and IGP based schemes; one the other hand, the optimum traffic load balancing between clusters is a very challengeable issue. The proposed solutions have different facilities to distribute incoming traffic; nevertheless, they are vulnerable in terms of propagation delay, centralized load balancer failure, and maintaining connections. In this paper, a new architecture based on BGP and Anycast routing protocols in SDN based data centers is proposed to distribute traffic loads between clusters. Simulation result shows improvement in comparison to the existing techniques. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  234 - Power Control and Subchannel Allocation in OFDMA Macrocell-Femtocells Networks
  H. Davoudi M. Rasti
  Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. More

  Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. Since macrocell users have priority to femtocell ones, presence of femtocell users should not prevent macrocell users to access minimum quality-of-service. In this paper, a power control and subchannel allocation scheme in downlink transmission an orthogonal frequency division multiple access (OFDMA) based two tier of macrocell and femtocell is proposed, aiming the maximization of femtocell users total data rate, in which the minimum QOS for all macrocell users and femtocell delay-sensitive users is observed. In macrocell tier, two different problems are considered. The first problem aim to maximizing the total threshold of tolerable cross-tier interference for macrocell users and the second problem’s goal is minimizing the macrocell’s total transmission power. For the femtocell tier, maximizing the users total data rate is the objective. Hungrian method, an assignment optimization method, is used for solving the first problem in macrocell tier. Moreover, in order to solve the second problem a heuristic method for subchannel allocation is proposed and dual Lagrange method is used for power control. In addition, in order to solve the problem for femtocell tier, a heuristic method is used for subchannel allocation. Subsequently, a dual Lagrange method which is one of the convex optimization problem solver is used, so that we can control the power. Finally, an extend simulations are performed to validate the performance of the proposed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  235 - Data Offloading to Femtocell with In-Band Full Duplex Deployment
  Mohammad Mollashahi M. Mehrjoo M. Abiri
  In order to increase throughput and spectral efficiency in a heterogeneous network including a macro and a femtocell, we propose a combined offloading with In-Band Full Duplex (IBFD) scheme in this paper. Traffic offloading to the femtocell is deployed to transmit netwo More

  In order to increase throughput and spectral efficiency in a heterogeneous network including a macro and a femtocell, we propose a combined offloading with In-Band Full Duplex (IBFD) scheme in this paper. Traffic offloading to the femtocell is deployed to transmit network users traffic to a macrocell base station in the uplink. In other words, all or a part of the traffic is offloaded to the femtocell and then transmitted to the macrocell, while the rest of traffic is transmitted to the macrocell directly. In the femtocell, we deploy and investigate IBFD technology, i.e., simultaneous transmit and receive traffic in one frequency band. Furthermore, in order to improve throughput of the network, we propose several scheduling schemes to transmit traffic. Finally, optimal number and position of users who use IBFD or do not use it, are determined. We propose a heuristic solution to find optimal position of IBFD users. Simulation results verify the network throughput improvement and power consumption reduction. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  236 - Cell Association Combined with Interference Management in Heterogeneous Cellular Networks Using a Distributed Algorithm
  Maryam Chinipardaz Seyed Majid Noorhosseini
  Due to the growing demand of cellular networks, the need to increase the capacity of these networks has always been a challenge. Heterogeneous cellular networks using small base stations alongside macro base stations are low cost and effective solutions for this problem More

  Due to the growing demand of cellular networks, the need to increase the capacity of these networks has always been a challenge. Heterogeneous cellular networks using small base stations alongside macro base stations are low cost and effective solutions for this problem. However the differences between the various BSs in heterogeneous networks have created new challenges in terms of cell association and interference management compared with the traditional cellular networks. Therefore, the design of new and efficient methods for allocating cells and resources in these networks is an open research topic. This paper addresses the need for an efficient solution to simultaneously allocating cells and subbands in order to prevent interference for all users. The protocol interference model and its modeling methods in cellular networks have been studied. After modeling the system, the problem is formulated as an integer optimization problem. Then, by reformulating the problem and using a one-level dual decomposition, an algorithm with efficient complexity with near-optimal answers is attained. Thereafter, a distributed protocol is presented in which each user and each base station would only require local information for making decisions. The simulation results confirm the effectiveness of the proposed solution. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  237 - Performance Analysis of Subband Adaptive Algorithms over Distributed Networks Based on Incremental Strategy
  Mohammad S. E. Abadi A. R. Danaee M. S. Shafiee
  This paper presents the problem of distributed estimation in an incremental network based on the family of normalized subband adaptive algorithms (NSAAs). The distributed NSAA (dNSAA), the distributed selective partial update NSAA (dSPU-NSAA), the distributed dynamic se More

  This paper presents the problem of distributed estimation in an incremental network based on the family of normalized subband adaptive algorithms (NSAAs). The distributed NSAA (dNSAA), the distributed selective partial update NSAA (dSPU-NSAA), the distributed dynamic selection NSAA (dDS-NSAA), and the dSPU-DS-NSAA are introduced in a unified way. The dNSAAs have better convergence speed than distributed normalized least mean square (dNLMS) algorithm especially for colored Gaussian input of the nodes. In comparison with dNSAA, the dSPU-NSAA, and dDS-NSAA have lower computational complexity and close performance to dNSAA. Also by combination of these algorithms, the dSPU-DS-NSAA is established which is computationally efficient. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed adaptive algorithms as well. The theoretical expressions for transient, and steady-state performance analysis of the various dNSAAs are introduced. The validity of the theoretical results, and the good performance of these algorithms are demonstrated by several computer simulations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  238 - A Load Balancing Scheme by D2D-Based Relay Communications in Heterogeneous Networks Signals
  shahriar gholami mehrabadi yasser attar izi soroush akhlaghi
  Heterogeneous networks have been regarded as an integral part of fifth generation communication networks in order to respond to the unprecedented growth of required data rates. In such networks, the existence of a variety of cells with base stations of varying capacitie More

  Heterogeneous networks have been regarded as an integral part of fifth generation communication networks in order to respond to the unprecedented growth of required data rates. In such networks, the existence of a variety of cells with base stations of varying capacities and transmit powers has enabled the repeated use of available bandwidth. Moreover, the excess load on the central base station can be directed to the sub-cell base stations. In the current work, a novel approach is proposed for such a load balancing problem in which some nodes previously connected to the main base station can be served by sub-cells through the use of some D2D relays. This will increase the overall network capacity, improve the quality of service (QoS) of cell edge users, and increase covered users. In this design, the maximization of the capacity of D2D links is formulated as an optimization problem which is not convex in general. To tackle this, the main problem is divided into two sub-problems of optimal resource allocation and user-relay pairing problems with much lower complexity. Simulation results demonstrate the superiority of the proposed method over existing works addressed in the literature. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  239 - Identifying Primary User Emulation Attacks in Cognitive Radio Network Based on Bayesian Nonparametric Bayesian
  K. Akbari J. Abouei
  Cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack More

  Cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack, thus malicious user attempts to send a signal same as primary user's signal to deceive secondary users and prevent them from sending signals in the spectrum holes. Meanwhile, causing traffic in CR network, malicious user obtains a frequency band to send their information. In this thesis, a method to identify primary user emulation attack is proposed. According to this method, primary users and malicious users are distinguished by clustering. In this method, the number of active users is recognized in the CR network by clustering. Indeed, by using Dirichlet process mixture model classification based on the Bayesian Nonparametric method, primary users are clustered. In addition, to achieve higher convergence rate, Chinese restaurant process method to initialize and non-uniform sampling is applied to select clusters parameter. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  240 - Introducing a Fog-Based Algorithm for Routing in Wireless Sensor Networks
  E. Mirzavand Borujeni D. Rahbari M. Nickray
  Wireless sensor networks (WSNs) consist of thousands of small nodes. The small and inexpensive parts of these nodes have led to their widespread use in various fields. However, these networks have constraints on energy consumption, processing resources, and storage whic More

  Wireless sensor networks (WSNs) consist of thousands of small nodes. The small and inexpensive parts of these nodes have led to their widespread use in various fields. However, these networks have constraints on energy consumption, processing resources, and storage which have caused many studies to find solutions to reduce these constraints. In recent years, with the advent of the concept of Fog computing, many new and effective solutions are represented for routing in wireless sensor networks. Since in WSNs it is important to save alive nodes and reduce the energy consumption of nodes, fog computing is useful for this purpose. In most WSN routing protocols, the best way to send data to cluster heads and the base station is the major part of their studies. In the new protocols, the Fog computing have been used to find the best way. In these methods, we have seen decreasing energy consumption and increasing network lifetime. In this paper, we represent a fog-based algorithm for routing in WSNs. According to the simulation results, the proposed protocol improved energy consumption by 9% meanwhile the number of alive nodes is increased by 74%, compared to the reviewed method. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  241 - A Novel Link Prediction Approach on Social Networks
  S. Rezavandi Shoaii H. Zare
  Nowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of ev More

  Nowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of evolution and structural behavior of the networks that is known as link prediction problem in the related literature. Nowadays people use multiple and different social networks simultaneously and it causes to demonstrate a new domain of research known as heterogenous social networks. There exist a few works on link prediction problem on heterogenous networks. In this paper, first a novel similarity measure for users in heterogenous networks is defined. Then a novel link prediction algorithm is described through a supervised learning approach which is consisted by the generated features from the introduced similarity measures. We employ the standard evaluation criteria for verification of the proposed approach. The comparison of the proposed algorithm to the other well-known earlier works showed that our proposed method has better performance than the other methods based on testing on several network datasets. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  242 - An Improved Grid-Based K-Coverage Technique Using Probabilistic Sensing Model for Wireless Sensor Networks
  Abdolreza Vaghefi Mahdi Mollamotalebi
  Coverage of an area, with one or multiple sensors, is one of the fundamental challenges in wireless sensor networks. Since a sensor life span is limited and reliable data is of great importance, sensitive applications like fire\leakage alarm systems, intrusion detection More

  Coverage of an area, with one or multiple sensors, is one of the fundamental challenges in wireless sensor networks. Since a sensor life span is limited and reliable data is of great importance, sensitive applications like fire\leakage alarm systems, intrusion detection, etc. need multiple sensors to cover the region of interest, which is called K-coverage. Most of the studies that have been carried out on K-coverage evaluation have used binary sensing model. In this paper, we propose a grid-based K-coverage evaluation technique using probabilistic sensing model to increase evaluation accuracy and decrease evaluation time. The proposed technique is implemented using NS-2 simulator, and its results are compared to probabilistic perimeter-based and binary grid-based techniques. The results indicate that the proposed technique improved accuracy by 14% and 24% compared to the mentioned techniques respectively. It also shows 7% decrease in evaluation time compared to probabilistic perimeter-based technique. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  243 - Sustainable Tree-Based Scheduling in Solar Powered Wireless Mesh Networks
  H. Barghi S. V. Azhari
  In many applications of wireless mesh networks, due to the lack of access to a permanent source of energy and the use of battery and energy harvesting equipment, energy sustainable design is very important. Duty-cycle adjustment, putting the node into sleep mode in some More

  In many applications of wireless mesh networks, due to the lack of access to a permanent source of energy and the use of battery and energy harvesting equipment, energy sustainable design is very important. Duty-cycle adjustment, putting the node into sleep mode in some parts of the working period, is a method for energy saving and sustainability assurance. In this case, to exchange data between neighboring nodes, protocols for sleep scheduling are needed. In some applications of these networks, such as video surveillance applications, it is necessary to collect data from different parts of the network. Tree topology is a good option for these applications. A simple method for coordinating sleep in a tree topology is the TIME-SPLIT algorithm, at which the working time of each node is evenly divided among its children. The proposed TIME-SPLIT scheduling algorithm does not consider the node energy limitations. In this paper, we have added the nodes duty-cycle constraint in the TIME-SPLIT algorithm to guarantee energy sustainability in tree-based wireless mesh networks. In situations where the energy status of the children is different, equal division of time leads to network inefficiency. To improve network efficiency and throughput, we provide two scheduling algorithms that take into account the conditions of the children's energy and traffic. In the first proposed algorithm, the time division is performed in relation to the duty-cycle of the children of each node. In the second algorithm, the time division is dynamically and in proportion to the traffic of the children, and the connection acceptance is more precisely performed based on its energy consumption during its lifespan. The simulation results performed by the NS3 network simulator show that in energy and tree structure imbalance conditions, where children of a node have different energy or sub tree, the proposed methods significantly (more than about 60%) increase the network’s total delivered traffic. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  244 - Human Action Recognition in Still Image of Human Pose using Multi-Stream neural Network
  Roghayeh Yousefi K. Faez
  Today, human action recognition in still images has become one of the active topics in computer vision and pattern recognition. The focus is on identifying human action or behavior in a single static image. Unlike the traditional methods that use videos or a sequence of More

  Today, human action recognition in still images has become one of the active topics in computer vision and pattern recognition. The focus is on identifying human action or behavior in a single static image. Unlike the traditional methods that use videos or a sequence of images for human action recognition, still images do not involve temporal information. Therefore, still image-based action recognition is more challenging compared to video-based recognition. Given the importance of motion information in action recognition, the Im2flow method has been used to estimate motion information from a static image. To do this, three deep neural networks are combined together, called a three-stream neural network. The proposed structure of this paper, namely the three-stream network, stemmed from the combination of three deep neural networks. The first, second and third networks are trained based on the raw color image, the optical flow predicted by the image, and the human pose obtained in the image, respectively. In other words, in this study, in addition to the predicted spatial and temporal information, the information on human pose is also used for human action recognition due to its importance in recognition performance. Results revealed that the introduced three-stream neural network can improve the accuracy of human action recognition. The accuracy of the proposed method on Willow7 action, Pascal voc2012, and Stanford10 data sets were 91.8%, 91.02%, and 96.97%, respectively, which indicates the promising performance of the introduced method compared to state-of-the-art performance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  245 - An Intelligent Novel Hybrid Live Video Streaming Method in Mesh-Based Peer-to-Peer Networks
  Naghmeh Farhadian behrang barekatain Majid Haroni Behzad Soleimani Neysiani
  Lack of an efficient video frame delivery method due to high delay in Pull method and large number of duplicated frames in Push method, as the two main content delivery methods among peers, has been a strong motivator for introducing hybrid methods based on these two ba More

  Lack of an efficient video frame delivery method due to high delay in Pull method and large number of duplicated frames in Push method, as the two main content delivery methods among peers, has been a strong motivator for introducing hybrid methods based on these two basic approaches for live video streaming in mesh-based peer-to-peer networks. Recent studies show that these hybrid methods suffer from inherent challenges of the two basic approaches because they are just a sequential or parallel execution of them. In this regard, this research introduces AMIN, a novel hybrid method for intelligently exchanging video frames among peers. Using AMIN, contrary to Pull, each peer sends its buffer map status (BMS) to its two-hop neighbors and the peer who receives the BMS will immediately check which video frames it can send to that peer instead of requesting missed video frames in its buffer from it. In addition, contrary to Push and because of BMS, peers do not blindly send video frames to their neighbors. Simulation results show that video quality considerably increases in peers, while End-to-End delay, received delay and the number of duplicated frames decrease in comparison with two basic methods as well as another recent similar approach. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  246 - Economic Evaluation of Integrated Operation of Electricity and Gas Networks in Khorasan Province
  Vahid khaligh Azam Ghezelbash Hassan Abniki
  Today, optimal operation and expansion planning have gained great attention in electricity industry optimizations and coordination of electricity and gas networks is one of the main goals in this procedure. In this study, decentralized operation of electricity and gas n More

  Today, optimal operation and expansion planning have gained great attention in electricity industry optimizations and coordination of electricity and gas networks is one of the main goals in this procedure. In this study, decentralized operation of electricity and gas networks is modeled on a real-world case study in Khorasan province of Iran. This modeling is from the perspective of two independent operators who seek to minimize the operation cost of their subordinating network, considering technical constraints. In this way, the amount of gas consumption in gas and electricity networks is considered as a common variable and it is compared using different methods as ADMM, ATC and centralized. Moreover, operating cost is compared in all three different methods. Finally, scenarios of increased load and second fuel have also been investigated using the obtained model. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  247 - Optimal and Sub-optimal Transmitter-Receiver Design in Dense Wireless Sensor Networks and the Internet of Things
  Farzad H. Panahi Fereidoun H. Panahi Zahra Askarizadeh Ardestani
  With the rapid development of new technologies in the field of internet of things (IoT) and intelligent networks, researchers are more interested than ever in the concept of wireless sensor networks (WSNs). The emergence of these densely structured networks in recent ye More

  With the rapid development of new technologies in the field of internet of things (IoT) and intelligent networks, researchers are more interested than ever in the concept of wireless sensor networks (WSNs). The emergence of these densely structured networks in recent years has raised the importance of the use of telecommunications technologies, such as ultra-wideband (UWB) technology with high reliability, industrial applications, and appropriate communication security. However, there are still numerous concerns about the extent of inter-network interference, particularly owing to undesired spectral discrete lines in this technology. As a result, it is necessary to provide an optimal solution to eliminate interference and control the power spectrum, and then design the optimal transmitter-receiver structures while considering high sensitivities to the synchronization problem in WSNs based on UWB technology. These goals are pursued in the present study by employing the optimal spectral strategy in the signal model, the structure of the transmitter sensor, and then constructing the optimal or sub-optimal receiver sensor structures, the results of which indicate improved communication performance in WSNs. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  248 - A Lightweight Intrusion Detection System Based on Two-Level Trust for Wireless Sensor Networks
  M. sadeghizade O. R. Marouzi
  Wireless sensor networks (WSNs) are one of the useful and attractive technologies that have received much attention in recent years. These networks have been used in a variety of applications, due to their ease of use and inexpensive deployment. Due to the criticality o More

  Wireless sensor networks (WSNs) are one of the useful and attractive technologies that have received much attention in recent years. These networks have been used in a variety of applications, due to their ease of use and inexpensive deployment. Due to the criticality of most applications of these networks, security is considered as one of the essential parameters of the quality of service (QoS), and thus Intrusion Detection System (IDS) is considered as a fundamental requirement for security in these networks. This paper provides a trust-based IDS to protect the WSN against all network layer and routing attacks based on the features extracted from them. Through simulations, the proposed IDS has been evaluated with all performance criteria. The results show that the proposed IDS, in comparison with existing works, which often focuses on a specific attack, covers all network layer and routing attacks in WSNs, and also, due to high detection accuracy, low false alarms rate, and low energy consumption is considered as a desirable and lightweight IDS for WSNs. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  249 - Performance Improvement of Heterogeneous Networks with Backhaul Constraint through Decoupled Uplink and Downlink Access Policy
  a. j. Zolfa Zeinalpour-Yazdi Aliakbar tadaion taft
  One of the major challenges in the heterogeneous networks, where different base stations with different capabilities serve users, is the access policy for establishing a communication link between the user and its serving node. To overcome this challenge in a heterogene More

  One of the major challenges in the heterogeneous networks, where different base stations with different capabilities serve users, is the access policy for establishing a communication link between the user and its serving node. To overcome this challenge in a heterogeneous network which also suffers from the backhaul constraint, a special kind of “decoupled uplink and downlink access” policy is proposed in this paper, which let users to be served by different base stations in uplink and downlink communications. More precisely, in order to efficiently utilize the system resources, increase the users’ throughput, and guarantee the users’ fairness, a special load balancing association policy is considered in the uplink transmission and a problem which maximizes the weighted sum of users’ effective rates is solved. Simulation results show that using this association policy for the considered scenario, significantly improves the load balancing index, energy efficiency, and also users’ effective rate compared to the scenario which considers the criterion of the maximum received power of the downlink connection for both uplink and downlink transmissions. In addition we propose an algorithm which further improves the load balancing considering the backhaul constraint of the base stations and evaluate its efficiency. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  250 - Coordinated Scheduling of Electricity and Natural Gas Networks Considering the Effect of PtG Units on Handling Electric Vehicles’ Uncertainties
  Iman Goroohi Sardou Ali Mobasseri
  Gas-fueled power plants are considerably effective in power system operation during peak hours due to their high up and down ramping rates. By increasing the penetration of gas-fueled power plants in power systems and development of new technologies, such as power-to-ga More

  Gas-fueled power plants are considerably effective in power system operation during peak hours due to their high up and down ramping rates. By increasing the penetration of gas-fueled power plants in power systems and development of new technologies, such as power-to-gas (ptg) units, coordinated scheduling of both electricity and natural gas (NG) networks has attracted systems researchers’ attention. The NG volume generated by ptg units are stored in storages to directly supply the NG demands, or to sell in NG markets. If necessary, the stored NG volumes are reconverted into electricity which may be a suitable replacement for batteries and storages in electricity network in long term. In this paper, a mixed integer linear programming (MILP) model is proposed for stochastic coordinated scheduling of electricity and NG networks with ptg units, considering uncertainties of charging and discharging available capacities of vehicle to grid (v2g) stations. A test network integrating modified 24-bus IEEE electricity network and Belgium gas network including nine power stations (three of them are gas-fueled), three v2g stations, and three ptg stations is studied to evaluate the effectiveness of the proposed model. Simulation results demonstrate the effectiveness of ptg units in handling the uncertainties of v2g stations charging and discharging. Besides, the effectiveness of coordinated scheduling of both electricity and NG networks in comparison with independent scheduling of both networks is demonstrated. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  251 - Coordinated Expansion Planning of Gas and Electricity Networks Considering N-1 Security Criterion
  V. Khaligh M. Oloomi Buygi
  Planning for the coordinated expansion of generation units, taking into account the fuel needed, has always attracted the attention of power industry planners. Failure to coordinated expansion of electricity and gas networks will result in excessive investment and, in s More

  Planning for the coordinated expansion of generation units, taking into account the fuel needed, has always attracted the attention of power industry planners. Failure to coordinated expansion of electricity and gas networks will result in excessive investment and, in some cases, defects in power supply. Therefore, there is a need to a model coordinating the expansion of electricity and gas networks, while taking into account the technical constraints. In this study, centralized expansion of gas and electricity networks is modeled by considering N-1 security criterion in gas network. This modeling is from the perspective of a central investor who, considering the technical constraints, seeks to minimize the total cost of investment and operation of the electricity and gas networks. The results of the gas network investment problem indicate that there is a need to increase pipeline capacity in some areas. In the proposed case study, the investment cost of the gas network is $19 million, while the total cost of investment and operation of the gas network is $37.19 billion. On the other hand, in the electricity grid, new power plants need to be installed in the designated areas. The results also indicate that the capacity of the F-H transmission line should be increased. Moreover, considering the N-1 criterion for gas pipeline outages, the power grid would prefer to install about 3200 MW of new generation units all around the grid thereby boosting the power network against pipeline outages. However, 2400 MW of new generating units would be adequate when N-1 criterion is omitted. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  252 - SAHAR: An Architecture to Strengthen the Control Plane of the Software-Defined Network Against Denial of Service Attacks
  mehran shetabi Ahmad Akbari
  Software-defined network (SDN) is the next generation of network architecture thatby separating the data plane and the control plane enables centralized control with the aim of improving network management and compatibility. However, due to the centralized control polic More

  Software-defined network (SDN) is the next generation of network architecture thatby separating the data plane and the control plane enables centralized control with the aim of improving network management and compatibility. However, due to the centralized control policy, this type of network is prone to Inaccessibility of control plane against a denial of service (DoS) attack. In the reactive mode, a significant increase in events due to the entry of new flows into the network puts a lot of pressure on the control plane. Also, the presence of recurring events such as the collection of statistical information from the network, which severely interferes with the basic functionality of the control plane, can greatly affect the efficiency of the control plane. To resist attack and prevent network paralysis, this paper introduces a new architecture called SAHAR, which consists of a control box consisting of a coordinator controller, a primary flow setup controller, and one or more (as needed) secondary flow setup controller(s). Assigning monitoring and managing tasks to the coordinator controller reduces the load of flow setup controllers. In addition, dividing the incoming traffic between the flow setup controllers by the coordinator controller distributes the load at the control plane. Thus, by assigning the traffic load resulting from a denial-of-service attack to one or more secondary flow setup controller(s), the SAHAR architecture can prevent the primary flow setup controller from impairment and resist DoS attacks. Tests show that SAHAR performs better in the face of a DoS attack than existing solutions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  253 - Wireless Powered Communication System Design with Nonlinear Energy Harvester
  مهرنوش  میرحاج مریم مسجدی محمدفرزان صباحی
  In this paper, a wireless powered communication network (WPCN) is considered, in which the hybrid access point (HAP) and the users are equipped with multiple antennas.In the downlink phase, an energy HAP transfers the energy signal to the users and in the uplink phase, More

  In this paper, a wireless powered communication network (WPCN) is considered, in which the hybrid access point (HAP) and the users are equipped with multiple antennas.In the downlink phase, an energy HAP transfers the energy signal to the users and in the uplink phase, users apply the harvested energy to transfer their information to the HAP using spatial division multiple access (SDMA) technology. By considering the nonlinear behavior of energy harvester in system design and aiming to maximize the sum of the rates, we propose an optimal method for designing energy pre-coding matrices, user information pre-coding matrices, and time devoted to the downlink and uplink phases. For this purpose, we rewrite the problem as a convex optimization problem by appropriate change of variables and propose a method to solve it. The simulation results show that in practical scenarios, employing the nonlinear energy harvesting model in the system design could reduce the transmitted energy, increase and the sum rate of the users. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  254 - Propose a New Clustering Algorithm for Data Transmission in Wireless Sensor Networks by Using Apollonius Circle
  Sh. Pourbahrami E. Khaledi Alamdari L. Mohammad Khanli
  Wireless sensor networks, as an up-to-date technology, are one of the fastest growing technologies in the world today. Since these networks are used in military and agricultural environments as well as for observation of inaccessible environments, these networks need to More

  Wireless sensor networks, as an up-to-date technology, are one of the fastest growing technologies in the world today. Since these networks are used in military and agricultural environments as well as for observation of inaccessible environments, these networks need to be organized to achieve goals such as successful and timely sending of data to the main station. Clustering of wireless sensor networks is one of the most widely used methods for organizing these networks. Various ways to cluster these networks are provided, most of which are aimed at preventing energy loss and increasing the lifetime of sensor nodes. The thesis attempts to present a new geometric method for clustering the nodes of wireless sensor networks. In this geometric method, Apollonius circle is used to draw the abstract shape of the clusters and to assemble the nodes around the cluster head. Due to the high accuracy that it has in determining the fit of node distances, this circle can accurately assign nodes to cluster heads and prevent large single-node clusters or faraway nodes. In this algorithm, a main station, a number of nodes are used as a cluster header and a number of nodes as routers. The goal is to find the most accurate cluster heads and create clusters of high coverage in the network. The proposed method is implemented in MATLAB software and comparison of the results obtained from the view of successful data transmission, clustering accuracy, network lifetime and number of coverage areas, is showing accuracy of this method compared to optimal Leach algorithms and K-means presented in this field. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  255 - Joint Power-Location Optimization in Cooperative Airborne Relay Networks for 5G+
  H. Amiri Mohamadreza Zahabi وحید مقدادی
  Future cellular networks 5G+ promise high data rates, ubiquitous services everywhere and flexibility. Cooperative airborne relay networks (CARNs) is a promising system architecture that enables network coverage extension and reliability enhancement. This article determi More

  Future cellular networks 5G+ promise high data rates, ubiquitous services everywhere and flexibility. Cooperative airborne relay networks (CARNs) is a promising system architecture that enables network coverage extension and reliability enhancement. This article determined the optimum relay location and allocate optimal power to minimize the average symbol error rate (ASER) of an aerial platform CRS with amplify-and-forward relaying protocol (AF-CRS) in the Nakagami-m fading channel. To achieve this goal, the ASER for the AF-CRS in the Nakagami-m channel for different modulations is calculated firstly. Then, we consider three scenarios. First, the optimal location of the AF relay with a given power allocation for the source and relay is determined. Second, the problem of optimizing power allocation for different relay locations is solved. Eventually, an algorithm for joint optimizing the power-location that leads to more efficient system operation is proposed. Also, we investigate the effect of the path-loss exponent, channel fading parameter, and relay altitude on the optimal relay location in the CARS. Finally, Simulations and numerical results are presented, that confirm the theoretical achievements and simulations show a more than 1 dB gain for the optimized system versus the non-optimized system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  256 - Multi-Stage Restoration of Electrical Distribution Networks
  s. ghasemi A. Khodabakhshian R. Hooshmand
  The purpose of distribution networks restoration is to re-energize the out-of-service loads after fault occurrence which is accomplished by changing the status of network switches and considering the network constraints. In this paper a multi-stage restoration method b More

  The purpose of distribution networks restoration is to re-energize the out-of-service loads after fault occurrence which is accomplished by changing the status of network switches and considering the network constraints. In this paper a multi-stage restoration method by the help of the modified decision-making tree algorithm is proposed to maximize the restored loads and also to minimize switching operations. The main stages of this method include initial restoration, reconfiguration and optimal load shedding. To reduce the search space, the network switches are categorized into different sets which avoid having any inappropriate result space. The proposed method is tested on two IEEE 69-bus and 119-bus distribution networks. The simulation results confirm the accuracy and efficiency of the proposed method in distribution network restoration. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  257 - An Adaptive Multi-Objective Clustering Algorithm based on Auction_Prediction for Mobile Target Tracking in Wireless Sensor Network
  Roghieh Alinezhad Sepideh Adabi arash Sharifi
  One of the applications of sensor networks is to track moving target. In designing the algorithm for target tracking two issues are of importance: reduction of energy consumption and improvement of the tracking quality. One of the solutions for reduction of energy consu More

  One of the applications of sensor networks is to track moving target. In designing the algorithm for target tracking two issues are of importance: reduction of energy consumption and improvement of the tracking quality. One of the solutions for reduction of energy consumption is to form a tracking cluster. Two major challenges in formation of the tracking cluster are when and how it should be formed. To decrease the number of messages which are exchanged to form the tracking cluster an auction mechanism is adopted. The sensor’s bid in an auction is dynamically and independently determined with the aim of establishing an appropriate tradeoff between network lifetime and the accuracy of tracking. Furthermore, since the tracking cluster should be formed and activated before the target arrives to the concerned region (especially in high speed of target), avoidance from delay in formation of the tracking cluster is another challenge. Not addressing the mentioned challenge results in increased target missing rate and consequently energy loss. To overcome this challenge, it is proposed to predict the target’s position in the next two steps by using neural network and then, simultaneously form the tracking clusters in the next one and two steps. The results obtained from simulation indicate that the proposed algorithm outperforms AASA (Auction-based Adaptive Sensor Activation). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  258 - Improving the Architecture of Convolutional Neural Network for Classification of Images Corrupted by Impulse Noise
  Mohammad Momeny M. Agha Sarram A. M.  Latif R. Sheikhpour
  Impulse noise is one the common noises which reduces the performance of convolutional neural networks (CNNs) in image classification. Preprocessing for removal of impulse noise is a costly process which may have a destructive effect on the training and validation of the More

  Impulse noise is one the common noises which reduces the performance of convolutional neural networks (CNNs) in image classification. Preprocessing for removal of impulse noise is a costly process which may have a destructive effect on the training and validation of the convolutional neural networks due to insufficient improvement of noisy images. In this paper, a convolutional neural network is proposed which is robust to impulse noise. Proposed CNN classify images corrupted by impulse noise without any preprocessing for noise removal. A noise detection layer is placed at the beginning of the proposed CNN to prevent the processing of noisy values. The ILSVRC-2012 database is used to train the proposed CNN. Experimental results show that preventing the impact of impulse noise on the training process and classification of CNN can increase the accuracy and speed of the network training. The proposed CNN with error of 0.24 is better than other methods in classification of noisy image corrupted by impulse noise with 10% density. The time complexity of O(1) in the proposed CNN for robustness to noise indicates the superiority of the proposed CNN. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  259 - Using Evolutionary Clustering for Topic Detection in Microblogging Considering Social Network Information
  E. Alavi H. Mashayekhi H. Hassanpour B. Rahimpour Kami
  Short texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must More

  Short texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must be specified beforehand and cannot be changed during time. From this perspective, these methods are not suitable for increasing and dynamic data. In addition, non-parametric topic evolution models lack appropriate performance on short texts due to the lack of sufficient data. In this paper, we present a new evolutionary clustering algorithm, which is implicitly inspired by the distance-dependent Chinese Restaurant Process (dd-CRP). In the proposed method, to solve the data sparsity problem, social networking information along with textual similarity has been used to improve the similarity evaluation between the tweets. In addition, in the proposed method, unlike most methods in this field, the number of clusters is calculated automatically. In fact, in this method, the tweets are connected with a probability proportional to their similarity, and a collection of these connections constitutes a topic. To speed up the implementation of the algorithm, we use a cluster-based summarization method. The method is evaluated on a real data set collected over two and a half months from the Twitter social network. Evaluation is performed by clustering the texts and comparing the clusters. The results of the evaluations show that the proposed method has a better coherence compared to other methods, and can be effectively used for topic detection from social media short texts. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  260 - Optimal Resource Allocation in Multi-Task Software-Defined Sensor Networks
  S. A. Mostafavi M. Agha Sarram T. Salimian
  Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program wh More

  Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program which serves as a common communication infrastructure for several different applications. Different sensor applications in the network can have different target functions and decision parameters. Due to the resource constraints of sensor network nodes, the multiplicity and variety of tasks in each application, requirements for different levels of quality of service, and the different target functions for different applications, the problem of allocating resources to the tasks on the sensors is complicated. In this paper, we formulate the problem of allocating resources to the sensors in the SDSN with different objective functions as a multi-objective optimization problem and provide an effective solution to solve it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  261 - DRSS-Based Localization Using Convex Optimization in Wireless Sensor Networks
  Hassan Nazari M. R. Danaee M. Sepahvand
  Localization with differential received signal strength measurement in recent years has been very much considered. Due to the fact that the probability density function is known for given observations, the maximum likelihood estimator is used. This estimator can be asym More

  Localization with differential received signal strength measurement in recent years has been very much considered. Due to the fact that the probability density function is known for given observations, the maximum likelihood estimator is used. This estimator can be asymptotically represented the optimal estimation of the location. After the formation of this estimator, it is observed that the corresponding cost function is highly nonlinear and non-convex and has a lot of minima, so there is no possibility of achieving the global minimum with Newton method and the localization error will be high. There is no analytical solution for this cost function. To overcome this problem, two methods are existed. First, the cost function is approximated by a linear estimator. But this estimator has poor accuracy. The second method is to replace the non-convex cost function with a convex one with the aid of convex optimization methods, in which case the global minimum is obtained. In this paper, we proposed new convex estimator to solve cost function of maximum likelihood estimator. The results of the simulations show that the proposed estimator has up to 20 percent performance improvement compared with existing estimators, moreover, the execution time of proposed estimator is 30 percent faster than other convex estimators. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  262 - Comprehensive Optimal Management System of Distributed Resources Using Dynamic Neural Network in Modeling of Electricity Consumption Uncertainty for Grid-Connected Microgrids
  Mohammad Veysi محمدرضا سلطانپور jafar Khalilpour hadi niaei
  In this paper, to enhance the optimal planning for power management of micrigrids, a strategy is proposed using power sharing through coordination between microgrids and the neighborhood system, which has no additional costs for generating units. The uncertainty values More

  In this paper, to enhance the optimal planning for power management of micrigrids, a strategy is proposed using power sharing through coordination between microgrids and the neighborhood system, which has no additional costs for generating units. The uncertainty values of electrical consumers are modeled by dynamic neural network, considering the implementation process and high accuracy of forecasting. In another view, to supply the electrical energy of microgrid, diesel generator, renewable energies such as solar energy and wind energy and so, battery energy storage are used, in addition to the upstream grid connection. As well as, using of the reliability factors, along with a detailed assessment of current costs will improve the performance of microgrid. Hence, the loss of power supply probability (LPSP) and loss of load expectations (LOLE) are expressed as factors for assessing the accuracy of current costs. The proposed model is implemented in GAMS and MATLAB environment and the simulation results clearly demonstrate the desired performance of the proposed algorithm, and leads to gaining revenue for the under-study system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  263 - Proposing a Robust Method Against Adversarial Attacks Using Scalable Gaussian Process and Voting
  Mehran Safayani Pooyan Shalbafan Seyed Hashem Ahmadi Mahdieh Falah aliabadi Abdolreza Mirzaei
  In recent years, the issue of vulnerability of machine learning-based models has been raised, which shows that learning models do not have high robustness in the face of vulnerabilities. One of the most well-known defects, or in other words attacks, is the injection of More

  In recent years, the issue of vulnerability of machine learning-based models has been raised, which shows that learning models do not have high robustness in the face of vulnerabilities. One of the most well-known defects, or in other words attacks, is the injection of adversarial examples into the model, in which case, neural networks, especially deep neural networks, are the most vulnerable. Adversarial examples are generated by adding a little purposeful noise to the original examples so that from the human user's point of view there is no noticeable change in the data, but machine learning models make mistakes in categorizing the data. One of the most successful methods for modeling data uncertainty is Gaussian processes, which have not received much attention in the field of adversarial examples. One reason for this could be the high computational volume of these methods, which limits their used in the real issues. In this paper, a scalable Gaussian process model based on random features has been used. This model, in addition to having the capabilities of Gaussian processes for proper modeling of data uncertainty, is also a desirable model in terms of computational cost. A voting-based process is then presented to deal with adversarial examples. Also, a method called automatic relevant determination is proposed to weight the important points of the images and apply them to the kernel function of the Gaussian process. In the results section, it is shown that the proposed model has a very good performance against fast gradient sign attack compared to competing methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  264 - Convolutional Neural Networks for Sentiment Analysis in Persian Social Media
  M. Rohanian M. Salehi A. Darzi وحید رنجبر
  With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entit More

  With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entities in a given document. In this paper we propose a sentiment analysis method for Persian text using Convolutional Neural Network (CNN), a feedforward Artificial Neural Network, that categorize sentences into two and five classes (considering their intensity) by applying a layer of convolution over input data through different filters. We evaluated the method on three different datasets of Persian social media texts using Area under Curve metric. The final results show the advantage of using CNN over earlier attempts at developing traditional machine learning methods for Persian texts sentiment classification especially for short texts. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  265 - Resource Management in Multimedia Networks Using Software-Defined Network Technology
  Ahmadreza Montazerolghaem
  Nowadays, multimedia networks on the Internet have become a low-cost and efficient alternative to PSTN. Multimedia transfer applications on the Internet are becoming more and more popular. This connection consists of two phases: signaling and media. The signaling phase More

  Nowadays, multimedia networks on the Internet have become a low-cost and efficient alternative to PSTN. Multimedia transfer applications on the Internet are becoming more and more popular. This connection consists of two phases: signaling and media. The signaling phase is performed by SIP proxies and the media phase by network switches. One of the most important challenges in multimedia networks is the overload of SIP proxies and network switches in the signaling and media phases. The existence of this challenge causes a wide range of network users to face a sharp decline in the quality of service. In this article, we model the routing problem in multimedia networks to deal with the overload. In this regard, we present a technology-based method of software-based networks and a mathematical programming model in multimedia networks. The proposed method is simulated under various scenarios and topologies. The results investigate that the throughput and resource consumption has improved. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  266 - A Hybrid Long-Term Probabilistic Net Load Forecasting Approach Considering Renewable Energies Power in Smart Grids
  Mohsen  Jahantigh majid moazzami
  With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and More

  With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and solving regression problem with the aid of mini-batch LBFGS method. Net load forecasting is suggested in this paper trough forecast combination via adaptive network-based fuzzy inference system. The structure includes a combination of several long-term forecasts, including forecasts of load, the generation of a solar station, and the generation of a wind farm with wind turbines equipped with doubly-fed induction generator. Also, the net load forecasting and the relationship between errors of load, wind and solar predictions are studied in this paper. The simulation results of the proposed method and its comparison with Tao and quantile regression models show that mean absolute percentage error of load forecasting, and the forecasts of solar and wind generations improved by 0.947%, 0.3079% and 0042%, respectively which result to a decrease in net load forecasting error. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  267 - Performance Improvement of Polynomial Neural Network Classifier using Whale Optimization Algorithm
  Mahsa Memari A. Harifi a. Khalili
  Polynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, le More

  Polynomial neural network (PNN) is a supervised learning algorithm which is one of the most popular models used in real applications. The architectural complexity of polynomial neural network in terms of both number of partial descriptions (PDs) and number of layers, leads to more computation time and more storage space requirement. In general, it can be said that the architecture of the polynomial neural networks is very complex and it requires large memory and computation time. In this research, a novel approach has been proposed to improve the classification performance of a polynomial neural network using the Whale Optimization Algorithm (PNN-WOA). In this approach, the PDs are generated at the first layer based on the combination of two features. The second layer nodes consists of PDs generated in the first layer, input variables and bias. Finally, the polynomial neural network output is obtained by sum of weighted values of the second layer outputs. Using the Whale Optimization Algorithm (WOA), the best vector of weighting coefficients will be obtained in such a way that the PNN network reach to the highest classification accuracy. Eleven different dataset from UCI database has been used as input data of proposed PNN-WOA and the results has been presented. The proposed method outperforms state-of-the-art approaches such as PNN-RCGA, PNN-MOPPSO, RCPNN-PSO and S-TWSVM in most cases. For datasets, an improvement of accuracy between 0.18% and 10.33% can be seen. Also, the results of the Friedman test indicate the statistical superiority of the proposed PNN-WOA model compared to other methods with p value of 0.039. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  268 - Autonomous Controlling System for Structural Health Monitoring Wireless Sensor Networks
  Sahand Hashemi Seyyed Amir Asghari Mohammad Reza Binesh Marvasti
  Nowadays, office, residential, and historic buildings often require special monitoring. Obviously, such monitoring involves costs, errors and challenges. As a result of factors such as lower cost, broader application, and ease of installation, wireless sensor networks a More

  Nowadays, office, residential, and historic buildings often require special monitoring. Obviously, such monitoring involves costs, errors and challenges. As a result of factors such as lower cost, broader application, and ease of installation, wireless sensor networks are frequently replacing wired sensor networks for structural health monitoring. Depending on the type and condition of a structure, factors such as energy consumption and accuracy, as well as fault tolerance are important. Particularly when wireless sensor networks are involved, these are ongoing challenges which, despite research, have the possibility of being improved. Using the Markov decision process and wake-up sensors, this paper proposes an innovative approach to monitoring stable and semi-stable structures, reducing the associated cost and error over existing methods, and according to the problem, we have advantages both in implementation and execution. Thus, the proposed method uses the Markov decision process and wake-up sensors to provide a new and more efficient technique than existing methods in order to monitor the health of stable and semi-stable structures. This approach is described in six steps and compared to widely used methods, which were tested and simulated in CupCarbon simulation environment with different metrics, and shows that the proposed solution is better than similar solutions in terms of a reduction of energy consumption from 11 to 70%, fault tolerance in the transferring of messages from 10 to 80%, and a reduction of cost from 93 to 97%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  269 - A Semi-Central Method to Improve Energy Saving in Real Wireless Sensor Networks Using Clustering and Mobile Sinks
  Fatemeh Sadeghi Sepideh Adabi Sahar Adabi
  Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to criti More

  Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to critical regions (i.e., regions those have low remaining energy and thus, high risk of energy hole problem). The limited number of mobile sinks should be utilized due to a high cost. Therefore, allocating the limited number of mobile sinks to the high amount of requests received from the critical regions is categorized as a NP-hard problem. Most of the previous studies address this problem by using heuristic methods which are carried out by sensor nodes. However, this type of solutions cannot be implemented in real WSN due to the sensors’ current technology and their limited processing capability. In other words, these are just theoretical solutions. Consequently, a semi-central genetic algorithm based method using mobile sink and clustering technique is proposed in order to find a trade-off between reduction of computation load on the sensors and increasing accuracy. In our method, lightweight computations are separated from heavyweight computations. While, the former computations are carried out by sensors, the latter are carried out by base station. Following activities are done by the authors: 1) cluster head selection by using effective environmental parameters and defining cost function of cluster membership, 2) mathematical modeling of a region’s chance to achieve mobile sink, and 3) designing a fitness function to evaluate the fitness of each allocation of mobile sinks to the critical regions in genetic algorithm. Furthermore, in our activities minimizing the number and length of messages are focused. In summary, the main distinguishing feature of the proposed method is that it can be implemented in real WSN (due to separation of lightweight computations from heavyweight computations) with respect to early mentioned objectives. The simulation results show the better performance of the proposed method compared to comparison bases. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  270 - testDesign Decentralized Controller for a Group of Cooperative Robot to Pushing a Box in Presence of Network Constraints
  میلاد مرادی سید محمد مهدی Seyyed M. Mehdi Dehghan
  The problem of pushing objects by a group of cooperative robots has many applications on land and sea level and due to its importance, it has become a standard problem for evaluating various theories of robot cooperation. In this case, each robot produces distributed co More

  The problem of pushing objects by a group of cooperative robots has many applications on land and sea level and due to its importance, it has become a standard problem for evaluating various theories of robot cooperation. In this case, each robot produces distributed control force to push the object in the desired direction. The proposed methods for distributed control of an object on a time-varying path require information about the position of the robots relative to the object. The problem of the lack of sufficient knowledge of each robot of how the robots are positioned relative to the body can be solved by proposing a consensus issue on positional moments. In this case, the robots must reach a consensus on these moments by exchanging information through the communication network between them. The effect of communication network between robots on the process of reaching consensus and the effect of delay in consensus on the results of control of object on the desired path is the subject of this article. In this paper, the appropriate control law for achieving consensus in the absence of full connection between all bots, delay and the probability of data loss in the communication network is presented. The maximum allowable network delay is also specified to prevent the instability of object motion control. The simulation results show the capability of the proposed method for controlling the velocity of the object on the desired variable path and show the effect of network constraints on the performance of the controller. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  271 - Numeric Polarity Detection based on Employing Recursive Deep Neural Networks and Supervised Learning on Persian Reviews of E-Commerce Users in Opinion Mining Domain
  Sepideh Jamshidinejad Fatemeh Ahmadi-Abkenari Peiman Bayat
  Opinion mining as a sub domain of data mining is highly dependent on natural language processing filed. Due to the emerging role of e-commerce, opinion mining becomes one of the interesting fields of study in information retrieval scope. This domain focuses on various s More

  Opinion mining as a sub domain of data mining is highly dependent on natural language processing filed. Due to the emerging role of e-commerce, opinion mining becomes one of the interesting fields of study in information retrieval scope. This domain focuses on various sub areas such as polarity detection, aspect elicitation and spam opinion detection. Although there is an internal dependency among these sub sets, but designing a thorough framework including all of the mentioned areas is a highly demanding and challenging task. Most of the literatures in this area have been conducted on English language and focused on one orbit with a binary outcome for polarity detection. Although the employment of supervised learning approaches is among the common utilizations in this area, but the application of deep neural networks has been concentrated with various objectives in recent years so far. Since the absence of a trustworthy and a complete framework with special focuses on each impacting sub domains is highly observed in opinion mining, hence this paper concentrates on this matter. So, through the usage of opinion mining and natural language processing approaches on Persian language, the deep neural network-based framework called RSAD that was previously suggested and developed by the authors of this paper is optimized here to include the binary and numeric polarity detection output of sentences on aspect level. Our evaluation on RSAD performance in comparison with other approaches proves its robustness. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  272 - An Intelligent Vision System for Automatic Forest Fire Surveillance
  Mohammad Sadegh  Kayhanpanah Behrooz Koohestani
  Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, o More

  Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, object detection, and image segmentation. Because forests are highly complex and nonstructured environments, the use of the vision system is still having problems such as the analogues of flame characteristics to sunlight, plants, and animals, or the smoke blocking the images of the fire, which causes false alarms. The proposed method in this research is the use of convolutional neural networks (CNNs) as a deep learning method that can automatically extract or generate features in different layers. First, we collect data and increase them according to data augmentation methods, and then, the use of a 12-layer network for classification as well as transfer learning method for segmentation of images is proposed. The results show that the data augmentation method used due to resizing and processing the input images to the network to prevent the drastic reduction of the features in the original images and also the CNNs used can extract the fire and smoke features in the images well and finally detect and localize them. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  273 - Energy-Aware Data Gathering in Rechargeable Wireless Sensor Networks Using Particle Swarm Optimization Algorithm
  Vahideh Farahani Leili Farzinvash Mina Zolfy Lighvan Rahim Abri Lighvan
  This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine t More

  This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware Data Gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5.6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  274 - A POI Recommendation Model According to the Behavior Pattern of Users Based on Friends List Using Deep Learning
  sadaf safavi mehrdad jalali
  The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is More

  The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is to investigate numerous significant traits of users and POIs. In this work, a novel method is presented for POI recommendation to develop the accurate sequence of top-k POIs to users, which is a combination of convolutional neural network, clustering and friendship. To discover the likeness, we use the mean-shift clustering method and only consider the influence of the most similarities in pattern’s friendship, which has the greatest psychological and behavioral impact rather than all user’s friendship. The new framework of a convolutional neural network with 10 layers can predict the next suitable venues and then select the accurate places based on the shortest distance from the similar friend behavior pattern. This approach is appraised on two LBSN datasets, and the experimental results represent that our strategy has significant improvements over the state-of-the-art techniques for POI recommendation. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  275 - A Prediction-Based Load Distribution Approach for Software-Defined Networks
  Hossein Mohammadi سیداکبر مصطفوی
  Software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network More

  Software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network. It results in a flexible network with centralized management in which network parameters can be well controlled. Due to the increasing number of users, the emergence of new technologies, the explosive growth of network traffic, meeting the requirements of quality of service and preventing underload or overload of resources, load balancing in software-based networks is of substantial importance. Load imbalance increases costs, reduces scalability, flexibility, efficiency, and delay in network service. So far, a number of solutions have been proposed to improve the performance and load balancing in the network, which take into account different criteria such as power consumption and server response time, but most of them do not prevent the system from entering the load imbalance mode and the risks of load imbalance. In this paper, a predictive load balancing method is proposed to prevent the system from entering the load imbalance mode using the Extreme Learning Machine (ELM) algorithm. The evaluation results of the proposed method show that in terms of controller processing delay, load balance and response time, it performs better than CDAA and PSOAP methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  276 - Stochastic Planning of Resilience Enhancement for Electric Power Distribution Systems against Extreme Dust Storms
  M. Haghshenas R. Hooshmand M. Gholipour
  Resilience in power systems refers to the system's ability to withstand severe disturbances with a low probability of occurrence. Because in recent years extreme dust storms have caused severe damage to Iran's electricity industry, especially in the south and southwest, More

  Resilience in power systems refers to the system's ability to withstand severe disturbances with a low probability of occurrence. Because in recent years extreme dust storms have caused severe damage to Iran's electricity industry, especially in the south and southwest, in this paper proposed a new scenario-based stochastic planning model for enhancement of power distribution systems resilience against extreme dust storms. In proposed model, in the first stage, the investment costs to improve the distribution system resilience against extreme dust storms are minimized due to the financial constraints, and in the second stage, the expected operating costs in dust storm conditions are minimized due to the operating constraints. Because network's insulation equipment are major cause of distribution system vulnerabilities in the dust storms, measures in the planning stage include replacement of porcelain insulators with silicon-rubber type, installation of automatic tie switches and installation of emergency generators. In the second stage, the measures are divided into preventive actions and corrective actions, and coordination between stages 1 and 2 is implemented in such a way that the results of each stage depend on the decision variables of the other stage. The simulation results for IEEE 33-bus test system and a 209 bus radial distribution network located in Khuzestan province, Iran, confirm the efficiency of the proposed model in different financial conditions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  277 - Sum Rate and Energy Efficiency Maximization in a Cognitive Radio Network with a Successive Relay Primary User
  elahe maddah Mohammad lari
  In this paper, we propose a cognitive radio network which consists of a number of secondary users and one primary user. The primary user utilized a successive relay performance. The successive relaying technique creates a full duplex relay performance by two half duplex More

  In this paper, we propose a cognitive radio network which consists of a number of secondary users and one primary user. The primary user utilized a successive relay performance. The successive relaying technique creates a full duplex relay performance by two half duplex relays, which improves spectral efficiency. In the presence of secondary users, we use successive relay technique in the primary user to ensure its acceptable performance. Also, the sum rate of secondary users is increased. The challenges of this network are inter-relay interference and inter user interference. The interference alignment method is utilized to eliminate the interferences in the successive relay technique and in the cognitive radio network. Besides, the minimum transmitted power of the primary user is derived to guarantee its quality of service requirement. Two power allocations algorithms are proposed to maximize the sum rate of secondary users and the energy efficiency of the network. In both power allocations algorithms, satisfying the quality of service of the primary user is considered. The closed-form solutions of these algorithms are studied. The fractional programing approach was employed to solve the energy efficiency optimization in two steps. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  278 - A Step towards All-Optical Deep Neural Networks: Utilizing Nonlinear Optical Element
  Aida Ebrahimi Dehghan Pour S. K.
  In recent years, optical neural networks have received a lot of attention due to their high speed and low power consumption. However, these networks still have many limitations. One of these limitations is implementing their nonlinear layer. In this paper, the implement More

  In recent years, optical neural networks have received a lot of attention due to their high speed and low power consumption. However, these networks still have many limitations. One of these limitations is implementing their nonlinear layer. In this paper, the implementation of nonlinear unit for an optical convolutional neural network is investigated, so that using this nonlinear unit, we can realize an all-optical convolutional neural network with the same accuracy as the electrical networks, while providing higher speed and lower power consumption. In this regard, first of all, different methods of implementing optical nonlinear unit are reviewed. Then, the impact of utilizing saturable absorber, as the nonlinear unit in different layers of CNN, on the network’s accuracy is investigated, and finally, a new and simple method is proposed to preserve the accuracy of the optical neural networks utilizing saturable absorber as the nonlinear activating function. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  279 - Generation of Persian sentences By Generative Adversarial Network
  Nooshin riahi Sahar Jandaghy
  Text generation is a field of natural language processing. Text generation enables the system to produce comprehensive, .grammatically correct texts like humans. Applications of text generation include image Captioning, poetry production, production of meteorological re More

  Text generation is a field of natural language processing. Text generation enables the system to produce comprehensive, .grammatically correct texts like humans. Applications of text generation include image Captioning, poetry production, production of meteorological reports and environmental reports, production of business reports, automatic text summarization, .With the appearance of deep neural networks, research in the field of text generation has change to use of these networks, but the most important challenge in the field of text generation using deep neural networks is the data is discrete, which has made gradient inability to transmit. Recently, the use of a new approach in the field of deep learning, called generative adversarial networks (GANs) for the generation of image, sound and text has been considered. The purpose of this research is to use this approach to generate Persian sentences. In this paper, three different algorithms of generative adversarial networks were used to generate Persian sentences. to evaluate our proposed methods we use BLEU and self-BLEU because They compare the sentences in terms of quality and variety. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  280 - Provide a Personalized Session-Based Recommender System with Self-Attention Networks
  Azam Ramazani A. Zareh
  Session-based recommender systems predict the next behavior or interest of the user based on user behavior and interactions in a session, and suggest appropriate items to the user accordingly. Recent studies to make recommendations have focused mainly on the information More

  Session-based recommender systems predict the next behavior or interest of the user based on user behavior and interactions in a session, and suggest appropriate items to the user accordingly. Recent studies to make recommendations have focused mainly on the information of the current session and ignore the information of the user's previous sessions. In this paper, a personalized session-based recommender model with self-attention networks is proposed, which uses the user's previous recent sessions in addition to the current session. The proposed model uses self-attention networks (SANs) to learn the global dependencies among all session items. First, SAN is trained based on anonymous sessions. Then for each user, the sequences of the current session and previous sessions are given to the network separately, and by weighted combining the ranking results from each session, the final recommended items are obtained. The proposed model is tested and evaluated on real-world Reddit dataset in two criteria of accuracy and mean reciprocal rank. Comparing the results of the proposed model with previous approaches indicates the ability and effectiveness of the proposed model in providing more accurate recommendations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  281 - Identifying Key Indicators Affecting Educational Needs Assessment through Realizing Organizational Strategies: A Network Analysis Process Method
  Ghasem Khajehvandi Seyed Reza Seyed Javadin Mojtaba  Amiri Kavehmohammad  Cyrus Naser  Sharifi
  10.52547/istd.31533.8.31.81
  The present study attempts to answer the question in terms of the indicators and the criteria affecting the educational needs assessment with the approach of realizing organizational strategies in Abadan Oil Refining Company. Therefore, this study aimed to identify and More

  The present study attempts to answer the question in terms of the indicators and the criteria affecting the educational needs assessment with the approach of realizing organizational strategies in Abadan Oil Refining Company. Therefore, this study aimed to identify and rank the indicators and the criteria affecting the educational needs assessment accordingly. Descriptive research method of Delphi was used in this study. To this end, a field survey was used to identify the effective criteria on educational needs assessment with the approach of realizing organizational strategies. These criteria were provided to 15 experts to be scored based on the research setting. Next, the extracted results yielded six indicators and 33 components. DEMATEL method and network analysis process were used to analyze the data. The results show that managerial factors were the most effective (7.46), and environmental factors were the most affected ones (6.59). The results also showed that environmental factors were the most important (0.240), and structural factors were the least important ones (0.106). Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  282 - The Role of Religious Training in Preventing the Side-Effects of Cyberspace and Social Networks
  Moslem  Moslem Shojaei
  The present article is an attempt to depict the role of religious training in preventing the social damages and offer some appropriate solutions for them. Social networks constitute the main factors of values, beliefs, and outlooks, for they can change the views and con More

  The present article is an attempt to depict the role of religious training in preventing the social damages and offer some appropriate solutions for them. Social networks constitute the main factors of values, beliefs, and outlooks, for they can change the views and conducts of people as they wish. The aim of this article is to study the impact of religious training in preventing the social damages and also explain the nature and characteristics of technology. The method of this work is based on analytic-documental studies and is an applied study. It is concluded that the function of social networks and cyberspaces are so strong that they can bring human societies under their influences and draw them away from their vales. Thus in order to protect them from the negative consequences of social networks we should replace the intellectual foundations of western technology by some religious foundations for a life style. A religious training with a monotheistic view in individual and social life considered to be the most important element in preventing the side-effects of social networks. Religious training can put forward certain lifestyles that could protect man from lapsing into such problems. The present article is an attempt to offer some suggestions for confronting with the side-effects of educational centers. Obviously, those societies that approach educational system for controlling and warding off social problem turn out to be successful. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  283 - Open Innovation Development Policy: Requirements for Iran
  atefeh zolfaghari Morteza Akbari Shokooh Sadat  Alizadeh
  10.52547/jstpi.31842.18.71.33
  In the open innovation system, in addition to ideas within the organization, organizations can also benefit from ideas outside the organization. In this way, the boundaries between organizations and their external environments become more permeable, and if appropriate p More

  In the open innovation system, in addition to ideas within the organization, organizations can also benefit from ideas outside the organization. In this way, the boundaries between organizations and their external environments become more permeable, and if appropriate policies are developed for the development of open innovation, it will be possible to take advantage of the facilities of other organizations in an appropriate way. This research have been reviewed texts and articles and researches related to open innovation and policy issues in open innovation. Requirements for open innovation policy in Iran regarding open innovation policy in the field of research and technology development, open innovation policy In the field of networking and interactions of open innovation ecosystem, open innovation policy in the field of entrepreneurship, open innovation policy in the field of science, open innovation policy in co-creation in universities, open innovation policy in the field of labor market, Competitive Open Innovation Policy-Making Provides International Open Innovation Policy-Making. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  284 - Neural-Fuzzy Network and Z-Source Converter Adaptive Controller for Control the Power of the Hybrid Network Consisting of Doubly-Fed Induction Generator and Solar Cel
  ali akbar habibi borzou yousefi abdolreza noori shirazi Mohammad rezvani
  Renewable energies outfitted with low latency assets as wind turbines and photovoltaic exhibits give significant adverse consequences through power framework dynamic protections. For this issue, in view of fostering a high voltage direct current (HVDC) interface, a vers More

  Renewable energies outfitted with low latency assets as wind turbines and photovoltaic exhibits give significant adverse consequences through power framework dynamic protections. For this issue, in view of fostering a high voltage direct current (HVDC) interface, a versatile Neuro-Fuzzy-based damping regulator is introduced in this paper for working on unique execution of low inertia resources associated with power frameworks. The created power framework comprises of various age sources including seaward and inland wind turbines (WTs), photovoltaic exhibits (PVs) and limited doubly fed induction generators (DFIGs) which are incorporated together through an interconnected framework. For this situation, thinking about various functional and innovative conditions, damping execution of proposed ANFIS plot is assessed. The proposed plot is a non-model-based regulator which utilizes the benefits of both neural and fluffy rationales together for giving a quick and secure design of damping regulator through continuous recreations. To research ANFIS plot through genuine cases, considering a commonplace microgrid comprises of various low-latency assets (for example WT, PV, DFIG), the framework damping exhibitions through hamper occasions are assessed. Recreation results demonstrate viability and effectiveness of the proposed plot for damping dynamic motions of low inertia resources with high damping proportions with respect to extreme issue occasions. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  285 - Presenting a novel solution to choose a proper database for storing big data in national network services
  Mohammad Reza Ahmadi davood maleki ehsan arianyan
  The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector an More

  The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector and social networks, has made the migration to new databases solutions with appropriate features inevitable. With the expansion and change in the size and composition of data and the formation of big data, traditional practices and patterns do not meet new requirements. Therefore, the necessity of using information storage systems in new and scalable formats and models has become necessary. In this paper, the basic structural dimensions and different functions of both traditional databases and modern storage systems are reviewed and a new technical solution for migrating from traditional databases to modern databases is presented. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the comprehensive services of the national information network are presented and the parameters and capabilities of databases in the standard and Hadoop context are examined. In addition, as a practical example, a solution for combining traditional and modern databases has been presented, evaluated and compared using the BSC method. Moreover, it is shown that in different data sets with different data volumes, a combined use of both traditional and modern databases can be the most efficient solution. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  286 - Analysis of marketing challenges in social networks
  seyyede somsye ghorbi Meysam Latifi
  Social networks have emerged as a convenient tool for providing products and services to customers by combining online sales and marketing of goods and services. This tool has become a unique tool in modern marketing with features such as information, entertainment, soc More

  Social networks have emerged as a convenient tool for providing products and services to customers by combining online sales and marketing of goods and services. This tool has become a unique tool in modern marketing with features such as information, entertainment, social creation of people and customers and interaction and retention with customers the present study aimed to identify and analyze the challenges of marketing in social networks. This research is based on an applied purpose and in terms of qualitative method and has an inductive approach. In order to analyze the data of in-depth semi-structured interviews, the content analysis method has been used. The statistical population includes 8 activists and experts in the field of digital marketing and appropriate texts for extracting indicators (theoretical foundations). In this research, snowball method has been used for sampling and saturation of themes has been used as a method to end sampling. The result of qualitative data analysis is the identification of 9 comprehensive themes, 21 organizing themes and 10 basic concepts. Comprehensive themes include user engagement, negative word of mouth, price competition, site / social page optimization, lack of academic training, uncontrollable competition, customer persuasion, lack of skilled manpower, and organizational infrastructure presented in a model. In the end, some suggestions are presented based on the results Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  287 - Fashionism and Youth: An Emerging Social Problem
  Javad Maddahi Alireza Ghobadi
  Fashion is a pervasive concept in the life cycle of the contemporary world. A phenomenon that, more than any other group, seduces young people as its followers and seems to be an emerging social problem. The purpose of this study was to investigate the socio-cultural fa More

  Fashion is a pervasive concept in the life cycle of the contemporary world. A phenomenon that, more than any other group, seduces young people as its followers and seems to be an emerging social problem. The purpose of this study was to investigate the socio-cultural factors related to youth fashionism. The research method was survey and the statistical population of this research was the youth of Tehran. The sample size was 387 people which was obtained by using Cochran's formula and the sample was selected by multi-stage cluster sampling method. The data collection technique is combination of researcher-made and standard questionnaire that was evaluated for validity and reliability. The reliability measurement through Cronbach's alpha test shows an acceptable level. According to the findings of this study, there is a significant relationship between the variables of age, parental education, social class, personality, public media and social networks, religiosity and fashion. The results of regression analysis show that according to the adjusted coefficient of determination in the regression equation, 50.3% of the variance of the dependent variable is explained by independent variables. Individualism showed the strongest relationship with fashion. Also, in the section of structural equation path analysis, the model indices have an acceptable fit. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  288 - Virtual Social Networks and Consumption Patterns of Tehran Citizens
  Hadi Barghamadi adel abdollahi Elaheh  Shams Koushki
  The purpose of this study was to investigate the relationship between the use of virtual social networks of Tehran citizens and their consumption pattern. The theoretical framework is based on the views of theorists such as Veblen, Giddens and Bourdieu. The approach o More

  The purpose of this study was to investigate the relationship between the use of virtual social networks of Tehran citizens and their consumption pattern. The theoretical framework is based on the views of theorists such as Veblen, Giddens and Bourdieu. The approach of the present research is quantitative and the research method is descriptive in terms of descriptive path and applied in terms of purpose. Data were collected by a survey method and using a questionnaire tool. The statistical population of the study included citizens over 15 years of age in Tehran who were interviewed using the sample size estimation formula, 600 of them were interviewed using multi-stage stratified sampling. The results, regarding objective dimension, showed that cultural consumption is one of the influential variables in the consumption pattern of Tehran citizens and in addition there is a significant relationship between cultural consumption and the use of virtual social networks. On the other hand, there is a relationship between the use of virtual social networks and the clothing consumption pattern of Tehran citizens. In fact, the use of virtual social networks by Tehran citizens has affected their coverage and how they buy. The results obtained in the mental dimension of the subject under study also indicate that the use of social networks has affected the values and social attitudes of Tehran citizens based on consumerism. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  289 - A Semi-Intelligent Method for Charging Electric Vehicles in Unbalanced Four-Wire Distribution Networks
  Saeed Zolfaghari Moghaddam
  The growing penetration of electric vehicles (EVs) in distribution networks (DNs) has posed many challenges for electricity distribution companies, such as: increasing the amount of voltage drop, network losses and the number of outages due to the overload. To overcome More

  The growing penetration of electric vehicles (EVs) in distribution networks (DNs) has posed many challenges for electricity distribution companies, such as: increasing the amount of voltage drop, network losses and the number of outages due to the overload. To overcome this, it is recommended to use coordinated charging methods. However, these methods require telecommunication, measurement and processing infrastructure with high costs and can only be implemented in smart grids. In this paper, a semi-intelligent method for charging EVs is presented that does not require complex infrastructure. This method, using a simple and inexpensive local automation system, charges EVs in the off-peak periods of the DN and thus improves its parameters. Since the EVs are charged at the low tariff time intervals, the proposed method will also benefit the EV owners. To achieve real results, four-wire DN is considered to model the effect of neutral conductor. To confirm the effectiveness of the proposed method, it is compared with different uncontrolled charging methods. A standard 19 bus test system is used for simulations. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  290 - Designing a model based on blockchain technology to strengthen cyber security in the banking industry
  hossein amoozadkhalili neda haghi Reza Tavakkoli-Moghaddam
  10.61186/jstpi.33168.19.76.60
  Designing a model based on blockchain technology to strengthen cyber security in the banking industry is one of the new methods studied in the banking industry to strengthen cyber security. Accordingly, this study seeks to achieve the goal of evaluating a model based on More

  Designing a model based on blockchain technology to strengthen cyber security in the banking industry is one of the new methods studied in the banking industry to strengthen cyber security. Accordingly, this study seeks to achieve the goal of evaluating a model based on blockchain technology to strengthen cyber security in the banking industry based on artificial neural networks. This model is based on a conceptual model used in an MLP neural network simulation that simulates a blockchain-like process. Also, the neural networks created in the block chain have a strong connection and the possibility of breaking them is low. The data became closer to normal distribution after learning, indicating that blockchain technology will be able to provide cyber security. The level of correlation and efficiency presented was also reported and the findings of the study showed that the efficiency related to blockchain technologies after learning reached the level of 770.57 units, which shows that using the MLP method to learn the process of blockchain technology can be Lead to greater efficiency for cyber security. Also, the value of variance is equal to 27.77 and the mean value of computational values is equal to 0.35 and the value of correlation is equal to 0.99. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  291 - Inferring Diffusion Network from Information Cascades using Transitive Influence
  Mehdi Emadi Maseud Rahgozar Farhad Oroumchian
  10.61186/jist.33656.11.44.307
  Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a so More

  Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n3). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  292 - Sonic wave velocity estimation using intelligent system and multi resolution graph base clustering: A case study from one of Iranian south field
  مرتضی نوری مینا کریمی خالدی
  Abstract Compressional and shear velocity are two fundamental parameters, which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using Dipole Sonic Imaging tool (DSI), but unfortunately this tool More

  Abstract Compressional and shear velocity are two fundamental parameters, which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using Dipole Sonic Imaging tool (DSI), but unfortunately this tool is run just in few wells of a field. Therefore it is important to predict compressional and shear velocity indirectly from the other conventional well logs that have good correlation with these parameters in wells without these logs. Classical methods to predict the mentioned parameters are utilizing correlations and regression analysis. However, the best tool is intelligent systems including Artificial Neural Network, Fuzzy Logic, Adaptive Neuro Fuzzy Inference System, and Multi resolution graph base clustering for performing such tasks. In this paper 1321 data points from Kangan and Dalan formations which have compressional and shear velocity are used. These data are divided into two groups: 995 and 326 data points were used for construction of intelligent systems and model testing, respectively. The results showed that despite differences in concept, all of the intelligent techniques were successful for estimation of compressional and shear velocities. The Multi resolution graph base clustering. The method had the best performance among the others due to precise clustering the data points. Using this method, the compressional and shear velocity were correlated with correlation factor of 0.9505 and 0.9407, respectively. The developed model does not incorporate depth or lithological data as a part of the inputs to the network. This means that utilized methodology is applicable to any field. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  293 - Numerical calculation of permeability tensor in fractured reservoirs
  سیما جلیلی رئوف حسین معماریان محمد رضا  رسائی بهزاد تخم چی
  Abstract Proper characterization of fracture reservoir is crucial for their sound development plan. It is however very difficult to correctly obtain various fracture reservoir properties such as permeability due to high order of heterogeneity and anisotropy within th More

  Abstract Proper characterization of fracture reservoir is crucial for their sound development plan. It is however very difficult to correctly obtain various fracture reservoir properties such as permeability due to high order of heterogeneity and anisotropy within these reservoirs. Classical dual porosity and/or dual permeability models consider a regular fracture network across the reservoir. To improve the concept, we develop a numerical method for tonsorial permeability calculation of blocks with random/disordered fracture distribution. We considered a 2D Cartesian fine grid in which the fractures were defined explicitly with their endpoints coordinates. Applying proper boundary conditions, single phase flow is then solved. Full tensor permeability is then obtained analytically from the calculated flow and pressure fields. The result of our method is compared well with that of the analytical models for simple fracture systems. In addition we reported the permeability tensor values of random fracture networks where no analytical solution is available. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  294 - Porosity modeling in Azadegan oil field: a comparative study of Bayesian theory of data fusion, multi layer neural network, and multiple linear regression techniques
  عطیه  مظاهری طرئی حسین معماریان بهزاد تخم چی بهزاد مشیری
  Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for More

  Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using these techniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  295 - Application of Artificial Intelligence during History matching in One of fractured oil Reservoirs
  ناصر اخلاقی ریاض خراط صدیقه مهدوی
  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with More

  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators, the results of these simulation models may have lot errors so production data (Pressure, Production Rate, Water Oil Ratio (WOR), Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry, which is based on the soft computing. In this study, Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity, horizontal and vertical fracture permeability, height of matrix and matrix-fracture dual porosity were applied as input data of the networks, and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process, has a good ability to history matching. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  296 - Application of Artificial Intelligence during History matching in One of fractured oil Reservoirs
  ناصر اخلاقی Reyaz kharata Sedigheh Mahdavi
  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with u More

  Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators, the results of these simulation models may have lot errors so production data (Pressure, Production Rate, Water Oil Ratio (WOR), Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry, which is based on the soft computing. In this study, Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity, horizontal and vertical fracture permeability, height of matrix and matrix-fracture dual porosity were applied as input data of the networks, and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process, has a good ability to history matching. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  297 - Improve the detection of buried channel, using Artificial Neural Networks and seismic attributes
  Alireza Ghazanfari Abdolrahim Javaherian Mojtaba Seddigh Arabani
  Channels are one of the most important stratigraphic and morphological events. If channels place in a suitable position such as enclosed in impermeable place can make suitable oil and gas reservoir; So identifying channels are crucial. Different tools such as filters, s More

  Channels are one of the most important stratigraphic and morphological events. If channels place in a suitable position such as enclosed in impermeable place can make suitable oil and gas reservoir; So identifying channels are crucial. Different tools such as filters, seismic attributes, artificial neural networks, and meta-attributes have played an important role in this regard. In this paper dip-steering cube, dip-steer median filter, dip-steer diffusion filter, and fault enhancement filter, have been used. Then, various seismic attributes such as similarity, texture, spectral decomposition, energy and polar dip have been defined and studied. Therefore, work on F3 real seismic data of Dutch part of the North sea for detecting channels has been started by detecting suitable attributes. For identifying the channel in data, it has been used from compilation and combination of seismic attributes using supervised ANN (multi-layer perceptron), and development of mata-attributes, then recombine meta-attributes created along the channel, and using different interpretation point, for eliminating the impact of facies and lithology changes along the channel. Among the advantages and the reasons for using this kind of neural network (supervised), which increases the effect of the neural network and improves the result, is the ability to train the network by specifying the channel and non-channel points used in this paper. Finally, using the above methods, the identification of the channel examined in the above seismic data has been improved, and the channel has been properly detected and extracted throughout its entire length. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  298 - Compilation of artificial neural networks and the thinned Fault likelihood auto-tracking algorithm, for identification, interpretation and extraction of faults
  Alireza Ghazanfari Hoseyn Mohammadrezaei Hamidreza Ansari
  Fault identification and investigating their evolution is of special importance in the exploration and development of hydrocarbon resources. Success in exploration and development of hydrocarbon fields, need to recognition of petroleum systems and in this regard one of More

  Fault identification and investigating their evolution is of special importance in the exploration and development of hydrocarbon resources. Success in exploration and development of hydrocarbon fields, need to recognition of petroleum systems and in this regard one of the most important topics is identifying faults and their extension condition as a main fluid migration path, specially in deeper zones. Faults and fractures have crucial role in making high permeable and porous segments and cut reservoir and cap rock in the fluid migration path. In addition, for maximizing the production of hydrocarbon from reservoirs and also for reducing the risk of drilling, it is necessary to gain information about geometry and nature of faults of reservoirs. In this paper, the purpose is investigating the performance of combination of neural networks and Fault Likelihood auto-tracking algorithm for identification and interpretation of faults in seismic data. At first using the Dip-steering feature of software, the early filter for accurate identification of dip of structures in the data, have been designed and applied. Then with designing and applying the appropriate filters, the seismic data have been improved. After that proper seismic attributes for fault identification have been calculated from seismic data. With picking fault and non-fault points from data, a supervised neural network using the selected attributes was formed and after training the network, the appropriate output achieved. Then the output of neural network has been used as a input for Thinned Fault Likelihood auto-tracking algorithm. The output of this part contains a volume of tracked faults. Finally using sub-tools of TFL and optimal setting of parameters, 3D fault planes has been interpreted and extracted. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  299 - Permeability estimation using petrophysical logs and artificial intelligence methods: A case study in the Asmari reservoir of Ahvaz oil field
  Abouzar Mohsenipour Bahman Soleimani iman Zahmatkesh Iman  Veisi
  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calcula More

  Permeability is one of the most important petrophysical parameters that play a key role in the discussion of production and development of hydrocarbon fields. In this study, first, the magnetic resonance log in Asmari reservoir was evaluated and permeability was calculated using two conventional methods, free fluid model (Coates) and Schlumberger model or mean T2 (SDR). Then, by constructing a simple model of artificial neural network and also combining it with Imperialist competition optimization (ANN-ICA) and particle swarm (ANN-PSO) algorithms, the permeability was estimated. Finally, the results were compared by comparing the estimated COATES permeability and SDR permeability with the actual value, and the estimation accuracy was compared in terms of total squared error and correlation coefficient. The results of this study showed an increase in the accuracy of permeability estimation using a combination of optimization algorithms with artificial neural network. The results of this method can be used as a powerful method to obtain other petrophysical parameters. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  300 - Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks
  maryam Najimi
  10.61186/jist.33968.11.44.320
  Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor networ More

  Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor network, in which the cognitive sensor transmitter has the capability of the energy harvesting. In the first slot, the cognitive sensor transmitter participates in spectrum sensing and in the existence of the primary user, it harvests the energy from the primary signal, otherwise the sensor transmitter sends its signal to the corresponding receiver while in the second slot, using the decode-and-forward (DF) protocol, a part of the bandwidth is used to forward the signal of the primary user and the remained bandwidth is used for transmission of the cognitive sensor. Therefore, our purposed algorithm is to maximize the cognitive network transmission rate by selection of the suitable cognitive sensor transmitters subject to the rate of the primary transmission and energy consumption of the cognitive sensors according to the mobility model of the cognitive sensors in the dynamic network. Simulation results illustrate the effectiveness of the proposed algorithm in performance improvement of the network as well as reducing the energy consumption. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  301 - SQ-PUF: A Resistant PUF-Based Authentication Protocol against Machine-Learning Attack
  Abolfazl Sajadi Bijan Alizadeh
  Physically unclonable functions (PUFs) provide hardware to generate a unique challenge-response pattern for authentication and encryption purposes. An essential feature of these circuits is their unpredictability, meaning that an adversary cannot sufficiently predict fu More

  Physically unclonable functions (PUFs) provide hardware to generate a unique challenge-response pattern for authentication and encryption purposes. An essential feature of these circuits is their unpredictability, meaning that an adversary cannot sufficiently predict future responses from previous observations. However, machine learning algorithms have been demonstrated to be a severe threat to PUFs since they are capable of accurately modeling their behavior. In this work, we analyze PUF security threats and propose a PUF-based authentication mechanism called SQ-PUF, which can provide good resistance to machine learning attacks. In order to make it harder to simulate or predict, we obfuscated the correlation between challenge-response pairs. Experimental results show that, unlike existing PUFs, even with a large data set, the SQ-PUF model cannot be successfully attacked with a maximum prediction accuracy of 53%, indicating that this model is unpredictable. In addition, the uniformity in this model remains almost the same as the ideal value in A-PUF. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  302 - Distributed Target Tracking by Solving Average Consensus Problem on Sensor Network Measurements
  Iman  Maghsudlu Meysam r. Danaee Hamid  Arezumand
  In this paper, a new algorithm is presented to drastically reduce communication overhead in distributed (decentralized) single target tracking in a wireless sensor network. This algorithm is based on a new approach to solving the average consensus problem and the use of More

  In this paper, a new algorithm is presented to drastically reduce communication overhead in distributed (decentralized) single target tracking in a wireless sensor network. This algorithm is based on a new approach to solving the average consensus problem and the use of distributed particle filters. For the algorithm of this paper, unlike the common algorithms that solve an average consensus problem just to approximate the global likelihood function to calculate the particle importance weights in distributed tracking, a new model for observation is presented based on the Gaussian approximation, which only solves the problem Consensus is applied to the mean on the received observations of the nodes in the network (and not to approximate the global likelihood function). These innovations significantly reduce the exchange of information between network nodes and as a result uses much less energy resources. In different scenarios, the efficiency of the proposed algorithm has been compared with the centralized algorithm and the distributed algorithm based on the graph, and the simulation results show that the communication overhead of the network is greatly reduced in exchange for an acceptable drop in tracking accuracy by using our proposed algorithm. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  303 - virtual media influence on the spread of iranian culture IRAN
  tahereh nikpoor ALIASGHAR KEYA mohammad reza rasouli
  abstract the purpose ofthisstudy was toinvestigate the effect of cyberspace and specifically social networks on the promotion of iranian environmental culture .this study was anapplied research in terms ofpurpose and exploratory mixed research design which was carriedo More

  abstract the purpose ofthisstudy was toinvestigate the effect of cyberspace and specifically social networks on the promotion of iranian environmental culture .this study was anapplied research in terms ofpurpose and exploratory mixed research design which was carriedout in two stages .in the qualitative part , semi structured interviews were used to explore and describe ideas and attitudes ofinterviewees .so , atfirst , an expert was interviewed and then data analysis was performed in three stages : open , axial and selective coding .as aresult , the open source codes were extracted in the form of axial codes of information functionality , educational and cultural performance , correlation function , promotional function , persuasive function and supervisory function .in quantitative section , among all actors of the environment in the form of ngos and virtual space activists ofiran , 217 people were selected and tested by cochran formula and randomly .aresearcher -made questionnaire was used to collect data in this section .finally , in order to investigate therelationships between variables , structural equation modeling technique was used with smart pls software .the results indicate that thevariables ofinformation function , educational and cultural function , correlation function , advertising function , persuasive function and monitoring function in promoting environmental culture of iran and cultureof environmental pollution are effective .in addition , demographic variables have moderator role in therelationship between thecomponents of cyberspace and culture of culture Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  304 - Performance evaluation model of educational centers using artificial neural network: One of the government organizations in the country
  zaman azhdari Hosein Abdollahi samad Borzoian morteza Taheri mostafa Ebrahimpour Azbari
  The purpose of this study is “to design a model for evaluating the performance of educational centers of one of the government organizations in the country using artificial neural network”. This is an evaluation study to evaluate the performance of public organizational More

  The purpose of this study is “to design a model for evaluating the performance of educational centers of one of the government organizations in the country using artificial neural network”. This is an evaluation study to evaluate the performance of public organizational educational centers. The information required for this research was collected through parallel information channels such as using the documents of educational centers under the organization and referring to their documents while maintaining the classification level. The statistical population of this study was five educational centers, one of the government organizations that hold educational courses for about 10 thousand personnel between 2013 and 2020; Based on the opinion of experts and the results of related studies, the inputs and outputs of the research were selected and determined. In order to reduce the input and output variables, the structural equation modeling method - partial least squares were used. In order to train the MLP bilayer neural network, the training method was used. After the teaching of neural network. The performance of neural network was examined through test patterns. The value (mean square error) of the MSE corresponds to 13 equal test patterns and 74/7413, which indicated the high accuracy of the trained network. Finally, the performance of the educational centers was ranked based on the analyzed data. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  305 - Comparative Analysis of Urban Contexts around Subway Stations Based on Indicators of Transit-Oriented Development (TOD), Case Study: Mirza Shirazi and Namazi Subway Stations
  Ali Reza Sadeghi Seyedeh Tayebeh Hosseinipour masoud dadgar
  Transportation issue has always been considered as one of the major urban problems. The rapid economic and demographic growth of cities, the increase of private car ownership, the limitation of urban transportation infrastructure, the increase of intra-city travel and t More

  Transportation issue has always been considered as one of the major urban problems. The rapid economic and demographic growth of cities, the increase of private car ownership, the limitation of urban transportation infrastructure, the increase of intra-city travel and the disproportionate development of urban spaces will increase the traffic problems in cities. Today, to get rid of these problems and increase the quality of life in cities, urban spaces are developed based on the transit system. The benefits of TOD have been widely demonstrated, from reducing carbon emissions to achieving a range of other inherent socio-economic benefits in sustainable cities. The increase in car ownership in Shiraz, whose population has overgrown in the years after the revolution and the intensification of the use of private cars in this city, caused many problems such as heavy traffic, air pollution, and noise pollution. This revealed the necessity of attention to transit in this city, and as a result, the urban train was considered an efficient transit method. The primary purpose of this study is to evaluate and analyze the urban areas around the public transportation stations in Shiraz based on the TOD criteria. The case study is two selected stations from Shiraz metro, Mirzai Shirazi and Namazi stations. In this study, first, the principles of TOD have been identified. After that, the characteristics of the case study have been evaluated with the TOD criteria and the ITDP standards. Then, the stations were analyzed using SWOT. This study shows that Mirzai Shirazi station is in a bronze condition, but the Namazi station is not even in this condition. Access to local services, proper non-residential density, and transit options at the stations are the strengths of this approach. Housing density in stations is low and needs to be increased. The findings of this study can be used to assist planners in evaluating actions taken in intra-city rail development. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  306 - Legal aspects of network marketing (MLM)in iran with pyramid schemes in comparison with America Federal Trade
  hamid samadifard farshid teyebi
  In practice , we are dealing with two types of network marketing , first network marketing and other pyramid schemes , which are the vast majority of the second type , which is illegal in most countries . From the United States Federal Trade Organization perspective , More

  In practice , we are dealing with two types of network marketing , first network marketing and other pyramid schemes , which are the vast majority of the second type , which is illegal in most countries . From the United States Federal Trade Organization perspective , the marketing plan is a very good way to sell products and services by distributors . Although this type of market includes new economic activities , which can be incorporated into an independent contract without being incorporated under the title of one of the contarct, it may have a few similarities with such as rent , peace , sale of law , and can be introduced , but each may have problems . In this study , it has been tried primarily to describe this form of marketing and the distinction of two healthy and unhealthy types , then the healthy type that is legally reviewed by matching the aspect of jurisprudence and the rules of the Federal Convention Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  307 - Investigating Social Relations between Eco-Tourists and Local People of Hormoz Island
  Elham  Nasrabadi حنانه محمدی کنگرانی Mehdi  Mirzadeh Kohshahi
  Ecotourism creates special opportunities to identify a wonderful aspect of nature, provides new information for tourists, and improves the living conditions of the host society. Since the Hormuz Island has a great capability in ecotourism, it can attract a large number More

  Ecotourism creates special opportunities to identify a wonderful aspect of nature, provides new information for tourists, and improves the living conditions of the host society. Since the Hormuz Island has a great capability in ecotourism, it can attract a large number of eco-tourists. Identifying and analyzing the friendly networks between eco-tourists and local people of Hormuz Island are the main aims of this study to investigate such relationship and their social effects. Upon conducting interviews and completing questionnaires by selected people, the collected data were analyzed using descriptive analysis, network’s analysis, and Visone’s software. Investigating the social relations between eco-tourists and local people showed that the formation of an informal friendly relationship and cooperation between the inhabitants and eco-tourists of Hormuz Island positively influenced ecotourism. Created social networks helped ecotourism continue in Hormuz Island and would have desirable economic and environmental effects for local people and Hormuz Island. This can have positive consequences such as increasing ecotourism development, sustainable ecotourism, and better ecotourism recognition for Hormuz Island inside and outside of Iran. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  308 - Social Capital and Loyalty of Pilgrims from Isfahan to Sarshur Neighborhood in Mashhad
  neda eidgahian kaseb Mozhgan  Azimi Hashemi
  A tourist behavior in the destination forms as a meaningful behavior in the form of a social action, of which relationship with others is the most important element. Through the description and analysis of the type and frequency of tourist relationships, and social capi More

  A tourist behavior in the destination forms as a meaningful behavior in the form of a social action, of which relationship with others is the most important element. Through the description and analysis of the type and frequency of tourist relationships, and social capital resulting from them, one can predict a tourist behavior, attachment, and loyalty to the destination. This study examined the effect of the relationship network among pilgrims from Isfahan in the host community on their loyalty to Sarshur neighborhood in Mashhad. This research followed the survey method. In 2016, 150 pilgrims from Isfahan were selected during the day of Arbaeen and the week following it. Results showed that the average size of the relationship network of Isfahan pilgrims in Sarshur neighborhood was 2.11 People (range: 0 to 9). The average of the general index of Isfahan pilgrims’ attachment to Sarshur neighborhood was 4.31 (range: 3.06 to 5) and the loyalty index was 4.43 (range: 1 to 5). The results of the model of structural equations by AMOS software showed that two causal ways used in this model can explain 80% of the loyalty variance of Isfahan pilgrims. The social capital of the relationship network among Isfahan pilgrims in this neighborhood increased their attachment. This attachment (through increased satisfaction) caused an increase in tourists’ loyalty. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  309 - The Effect of E-tourism on the Purchase of Tourism Services and its Conformity with the Behavior of Iranian Tourists
  Reza Minaee Farshid Namamian Fakhrodin Maroofi Alireza Moradi
  Tourism will soon become the largest service industry in the world with the help of IT. Social networking platforms, which have become prevalent via mobile data, are valuable tools that contribute to the information and telecommunication technology (ICT). These platform More

  Tourism will soon become the largest service industry in the world with the help of IT. Social networking platforms, which have become prevalent via mobile data, are valuable tools that contribute to the information and telecommunication technology (ICT). These platforms, which provide interactions and exchanges of various contents among the users, have developed new lifestyles and behavioral models among the tourists. Marketers must enjoy the online social networks and e-tourism to increase their competitiveness, market share, and the generated profit by providing the tourists with some services tailored to their demands. Upon reviewing 100 relevant qualitative and quantitative studies, we used an interpretive structural modeling (ISM) approach to present a model with five primary themes, in order of priority: a major environment, managerial factors, individual factors, group factors, and marketing factors. Through these factors, social networks affected tourists’ behaviors. The model was then tested on Iranian tourists’ behaviors using qualitative and quantitative statistical methods. Results could be used by virtually all marketers and activists in the tourism industry. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  310 - Assessing Users’ Recreational Demand in Urban Parks in Tehran with the Help of the Artificial Neural Network
         
  The advantages of public green spaces are clear. However, it is hard to estimate which parks provide higher standards for users’ (both tourists and citizens) recreational demands. This study provided a model to assess recreational demands in urban parks with the help of More

  The advantages of public green spaces are clear. However, it is hard to estimate which parks provide higher standards for users’ (both tourists and citizens) recreational demands. This study provided a model to assess recreational demands in urban parks with the help of the artificial neural network. The aim was to clarify the rules of satisfaction among the users’ recreational demands in urban parks. In 22 districts of the city of Tehran, 104 local urban parks (with a high diversity of the quality of welfare services and design) were selected. Using the user-centered viewpoint, we assessed the recreational demand. A field study from 1395 to 1396 helped to investigate the role of the urban district and park service variables in increasing the demand for urban parks. Results of trained networks showed that the artificial neural network created the best function of topology optimization with a higher coefficient of determination in three categorists of training, validation, and test data. Sensitivity analysis showed that the number of urban district parks, sports areas, cultural areas, and the quality of landscape with a sensitivity coefficient of 183.5, 58.1, 52.7, and 30.4, respectively, had the highest effect on the users’ recreational demand in urban parks. The suggested model would be a decision support system in designing urban parks. Such an approach would help improve urban development based on tourism attractions and would develop urban tourism on a broader scale. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  311 - Key Success Factors of Crowdsourcing in in Developing Tourism Destination Capabilities: A Case Study of Yazd City
  Hamed Fallah Tafti Mahdieh Zahmatkesh Saredorahi بهاره گورنگی
  Today, information technology has made it possible to solicit the views of different people with diverse perspectives and ideological backgrounds. Being one of the benefits of IT, crowdsourcing could, as an easy-to-use public opinion survey tool, be used as a very usefu More

  Today, information technology has made it possible to solicit the views of different people with diverse perspectives and ideological backgrounds. Being one of the benefits of IT, crowdsourcing could, as an easy-to-use public opinion survey tool, be used as a very useful instrument wherever social participation may facilitates the identification and resolution of problems. Having carefully reviewed previous studies of the field, we found that few attempts have so far been made to induce tourists to socially participate in identifying the opportunities for the development of civilian, cultural, and social infrastructures of tourist destinations. As one of the Iranian cultural and historical tourist destinations, Yazd city has recently been enlisted in UNESCO World Heritage sites and has attracted the attention of many tourists worldwide. Having said that, it appears that its tourism capacities and infrastructures need to be developed. This study, therefore, sought to identify the criteria which could be effective in the success of tourist crowdsourcing in offering appropriate solutions for the development of tourism in Yazd. To this end, the primary criteria for the successful implementation of crowdsourcing were extracted from the relevant literature, from which 25 criteria in six general categories were screened and identified via Delphi method. To rank the criteria mentioned, the opinions of Yazd tourism experts were collected through paired comparisons, being analyzed and ranked by fuzzy network analysis. The finding of the study indicated that human resources and cultural indices with the weights of 0.28 and 0.21 respectively were the most important factors in the success of tourist crowdsourcing. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  312 - Network Analysis of Organizational Cooperation in Tourism Destination Management
  Asghar   Tahmasebi mehri zavarniya
  Organizational cooperation is regarded as a key factor in the sustainability and competitiveness of tourist destinations. This study sought to investigate the organizational cooperation network and the position of different actors and organizations within the management More

  Organizational cooperation is regarded as a key factor in the sustainability and competitiveness of tourist destinations. This study sought to investigate the organizational cooperation network and the position of different actors and organizations within the management of Mashhad tourist destinations, using Social Network Analysis. To this end, the data on the organizational interactions of 30 actors involved were collected through surveying and interviewing three key experts in each relevant organization. The data were then analyzed via Ucinet software, using the in-degree, out-degree, and betweenness centrality indicators. The findings of the study indicated that the governmental organizations such as the provincial governments, gubernatorial offices, and the Law Enforcement Forces mostly exercised control and power on Mashhad tourist destinations’ organizational cooperation network. Moreover, with the highest amount of betweenness centrality, the gubernatorial offices, and the Office of Tourism and Cultural Heritage enjoyed the greatest potentials for cooperation within the organizational cooperation network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  313 - Bibliometric Analysis, Tourism Experience, Computational Literature Review, Co-occurrence Network, Co-authorship Network
  leila ilchi mesbah seivandian amir salar vanaki
  10.61186/journalitor.36036.11.24.139
  This study set out to investigate the trend of studies conducted on tourist experience using bibliometric analysis. To this end, the study analyzed 2683 articles published in the Web of Science database from 1980 to 2020 using the VOS Viewer software to visualize and an More

  This study set out to investigate the trend of studies conducted on tourist experience using bibliometric analysis. To this end, the study analyzed 2683 articles published in the Web of Science database from 1980 to 2020 using the VOS Viewer software to visualize and analyze the results. In this regard, two analysis levels were considered. At the first level, the publication trend and the impact factor of authors, journals, and articles were examined. At the second level, the structure of the co-occurrence network of keywords and the co-authorship network of authors, institutions, and countries were analyzed. The findings of the study indicated a significantly growing trend of studies (most of which were published in 2019) conducted on tourism experience in recent years, which is expected to continue to grow in the future. On the other hand, hospitality and leisure, management, and environmental studies were found as the highly researched areas, with most articles being published on tourism experience. Moreover, social media, trust, user opinions, and competitiveness are among the emerging research areas in the relevant literature. In this regard, health tourism, culinary tourism, social experience, shared value creation, authenticity, cultural experience, destination experience, backpackers, information technology, cultural differences, cultural heritage, students’ experiences, sustainable management, and transportation are among the main topics addressed in the literature. On the other hand, the analysis of the authors' co-authorship network revealed that thirty-five research groups are currently working in this field worldwide, with the Hong Kong Polytechnic University exerting the highest influence on the international collaboration network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  314 - Stock Price Movement Prediction Using Directed Graph Attention Network
  Alireza Jafari Saman Haratizadeh
  Prediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction perfor More

  Prediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction performance. Previous studies have demonstrated that aggregating information from related stocks can improve the performance of prediction. However, the capacity of modeling the stocks relations as directed graphs and the power of sophisticated graph embedding techniques such as Graph Attention Networks have not been exploited so far for prediction in this domain. In this work, we introduce a framework called DeepNet that creates a directed graph representing how useful the data from each stock can be for improving the prediction accuracy of any other stocks. DeepNet then applies Graph Attention Network to extract a useful representation for each node by aggregating information from its neighbors, while the optimal amount of each neighbor's contribution is learned during the training phase. We have developed a novel Graph Attention Network model called DGAT that is able to define unequal contribution values for each pair of adjacent nodes in a directed graph. Our evaluation experiments on the Tehran Stock Exchange data show that the introduced prediction model outperforms the state-of-the-art baseline algorithms in terms of accuracy and MCC measures. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  315 - A New Parallel Method to Verify the Packets Forwarding in SDN Networks
  Rozbeh Beglari Hakem Beitollahi
  The rise of Software-Defined Networking (SDN) has revolutionized network management, offering greater flexibility and programmability. However, ensuring the accuracy of packet forwarding remains paramount for maintaining network reliability and security in SDN environme More

  The rise of Software-Defined Networking (SDN) has revolutionized network management, offering greater flexibility and programmability. However, ensuring the accuracy of packet forwarding remains paramount for maintaining network reliability and security in SDN environments. Unlike traditional IP networks, SDN separates the control plane from the data plane, creating new challenges for securing data transmission. Existing verification methods designed for IP networks often cannot be directly applied to SDN due to this architectural difference. To address the limitations of existing verification methods in SDN networks, new approaches are necessary. This research proposes a novel parallel method for verifying packet forwarding, building upon concepts from DYNAPFV. The proposed approach aims to overcome specific limitations of existing methods (including DYNAPFV), such as scalability issues, slow verification times. Simulations demonstrate significant improvements compared to DYNAPFV. The proposed parallel method achieves a 92% reduction in time required to identify malicious nodes within the network. The results also reveal a trade-off between security and verification time. As the probability of packet integrity confirmation increases from 0.8 to 0.99, system security strengthens, but the time to detect malicious switches also increases. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  316 - Social Networks Embedding Based on the Employment of Community Recognition and Latent Semantic Feature Extraction Approaches
  Mohadeseh Taherparvar Fateme Ahmadi abkenari Peyman bayat
  The purpose of embedding social networks, which has recently attracted a lot of attention, is to learn to display in small dimensions for each node in the network while maintaining the structure and characteristics of the network. In this paper, we propose the effect of More

  The purpose of embedding social networks, which has recently attracted a lot of attention, is to learn to display in small dimensions for each node in the network while maintaining the structure and characteristics of the network. In this paper, we propose the effect of identifying communities in different situations such as community detection during or before the process of random walking and also the effect of semantic textual information of each node on network embedding. Then two main frameworks have been proposed with community and context aware network embedding and community and semantic feature-oriented network embedding. In this paper, in community and context aware network embedding, the detection of communities before the random walk process, is performed through using the EdMot non-overlapping method and EgoNetSplitter overlapping method. However, in community and semantic feature-oriented network embedding, the recognition of communities during a random walk event is conducted using a Biterm topic model. In all the proposed methods, text analysis is examined and finally, the final display is performed using the Skip-Gram model in the network. Experiments have shown that the methods proposed in this paper work better than the superior network embedding methods such as Deepwalk, CARE, CONE, and COANE and have reached an accuracy of nearly 0.9 and better than other methods in terms of edge prediction criteria in the network. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  317 - nvestigating the Relationship between Religious Beliefs and the Use of Social Networks on Commitment to Islamic Hijab (Case Study: Students of Mohaghegh Ardabili University)
  Ali Ahmadpour ALI rezaei sharif
  The aim of this study was to investigate the relationship between religious beliefs and dependence on the use of social networks with the degree of observance and commitment to the Islamic hijab of students of Mohaghegh Ardabili University. The statistical population of More

  The aim of this study was to investigate the relationship between religious beliefs and dependence on the use of social networks with the degree of observance and commitment to the Islamic hijab of students of Mohaghegh Ardabili University. The statistical population of this study was undergraduate students in different fields of Mohaghegh Ardabili University of Ardabil in the academic year of 1398. For this purpose, 430 students (307 girls and 123 boys) in different disciplines were selected by stratified and random cluster sampling. Made replied; In this study, structural equation modeling method was used to analyze the data. Findings indicate that the direct effect of religious dimension, emotional dimension, consequential dimension, ritual dimension on students 'observance of Islamic hijab is significant, also the direct effect of dependence on the use of virtual social networks has an effect on students' observance of Islamic hijab. It is worth noting that the dimensions of student religiosity and dependence on the use of virtual social networks in data analysis overlap. In other words, the dimensions of religiosity and dependence on the use of virtual social networks, if together, can have a significant impact on students' observance of the Islamic hijab. Of course, the effect of religiosity dimensions on dependence on the use of virtual social networks is negative and inverse. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  318 - Feasibility of Economic Convergence in the Persian Gulf and Realizing Requirements
  Hemmat  Imani
  Achieving economic integration, security and sustainable development in the Middle East and the Persian Gulf has suggested as a considerable topic for many international relations' researchers. Reviewing the research literature, it can conclude that the researchers did More

  Achieving economic integration, security and sustainable development in the Middle East and the Persian Gulf has suggested as a considerable topic for many international relations' researchers. Reviewing the research literature, it can conclude that the researchers did not provide the same solutions on how to achieve regional convergence in the Persian Gulf. Instead, a variety of analysis, hypotheses and suggestions made in this regard. The study aims on the feasibility of economic integration in the Persian Gulf region (including Iran, Saudi Arabia, Iraq, Qatar, the United Arab Emirates, Oman, Kuwait and Bahrain) and the requirements for achieving it. Economic convergence considered as a process that aims to achieve peace, security and sustainable development in the region. Accordingly, the research question would be “How the Gulf States could achieve economic integration in order to achieve peace, security and sustainable development”. In order to achieve peace, security and sustainable development, the research hypothesis the Persian Gulf States should rely on a combination of various economic, political, security, and religious-cultural capacities. Then, overcoming internal, regional and visionary divergent factors must take into account the behavioral requirements of achieving economic and political-security cooperation (in the short and medium term) and economic convergence (in the long-term) in major national decisions as well as their foreign policy approaches. The research methodology, as needed, was a combination of qualitative method based on library documents and quantitative and statistical methods. The findings provide a comprehensive network model for achieving peace, security and sustainable development through economic integration in the Persian Gulf. While rejecting strategic hierarchy model in prioritizing the indicators, the simultaneous and interconnected use of different economic, political, security and religious-cultural components in achieving economic and political-security cooperation in the short and medium-term and economic convergence is proposed for long-term. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  319 - Convolutional Neural Networks for Medical Image Segmentation and Classification: A Review
  Jenifer S Carmel Mary Belinda M J
  10.61186/jist.37936.11.44.347
  Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep le More

  Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep Convolutional Neural Networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the works exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pre-trained models and General Adversarial Networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on covid-19 detection and child bone age prediction. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  320 - Proposing a Detection and Mitigation Approach for DDoS Attacks on SDN-Based IoT Networks
  fatemeh MotieShirazi Seyedakbar Mostafavi
  Internet of Things (IoT) is a network of objects on which objects can communicate with other objects. The Internet of Things is currently constantly under numerous attacks due to technical, legal and human problems. One of the most important of these attacks is the Deni More

  Internet of Things (IoT) is a network of objects on which objects can communicate with other objects. The Internet of Things is currently constantly under numerous attacks due to technical, legal and human problems. One of the most important of these attacks is the Denial of Service (DoS) attack, in which normal network services are out of service and it is impossible for objects and users to access the server and other resources. Existing security solutions have not been able to effectively prevent interruption attacks in Internet of Things services. Software-oriented network (SDN) is a new architecture in the network based on the separation of the control and data plane of the network. Programmability and network management capability by SDN can be used in IoT services because some IoT devices send data periodically and in certain time intervals. SDN can help reduce or prevent the data flood caused by IoT if properly deployed in the data center. In this article, a method to detect DDoS attacks in Internet of Things based on SDN is presented and then an algorithm to reduce DDoS attacks is presented. The proposed method is based on the entropy criterion, which is one of the most important concepts in information theory and is calculated based on the characteristics of the flow. In this method, by using two new components on the controller to receive incoming packets and considering the time window and calculating entropy and flow rate, a possible attack is detected in the network, and then based on the statistics of the flow received from the switches, the certainty of the attack is determined. Compared to the existing methods, the proposed method has improved 12% in terms of attack detection time and 26% in terms of false positives/negatives. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  321 - Video Summarization Using a Clustering Graph Neural Networks
  Mahsa RahimiResketi Homayun Motameni Ebrahim Akbari Hossein  Nematzadeh
  The increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summar More

  The increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summarization, this task is achieved and the film is summarized into a series of short but meaningful frames or clips. This study tried to cluster the data by an algorithm (K-Medoids) and then with the help of a convolutional graph attention network, temporal and graph separation is done, then in the next step with the connection rejection method, noises and duplicates are removed, and finally summarization is done by merging the results obtained from two different graphical and temporal steps. The results were analyzed qualitatively and quantitatively on three datasets SumMe, TVSum, and OpenCv. In the qualitative method, an average of 88% accuracy rate in summarization and 31% error rate was achieved, which is one of the highest accuracy rates compared to other methods. In quantitative evaluation, the proposed method has a higher efficiency than the existing methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  322 - Renovation of worn-out urban tissues with emphasis on improving the quality of life Case study: Lordegan worn-out tissues
  Abolfazl  Nikbakht ali aram
  Today, the concept of reconstruction and renovation in urban planning and design is aligned with the predictions of improving the quality of textures and creating new facilities. With the reconstruction and renovation of old structures, local security, vitality, clean r More

  Today, the concept of reconstruction and renovation in urban planning and design is aligned with the predictions of improving the quality of textures and creating new facilities. With the reconstruction and renovation of old structures, local security, vitality, clean roads, free traffic and stylish surfing along with well-built buildings are created. Reconstruction and renovation of old structures does not belong to a specific group of society, but is a partnership between government organizations and residents. The old contexts of Lordegan city are determined in three areas A, B, C that area C is the center of the city and it is older that access routes have more problems than others. In this regard, suggestions such as: improvement and renovation of the central hills of the city in area C with the participation of government organizations and residents, improving the quality of Imam Khomeini Square, providing special incentives for public participation, renovation of canals, construction. Parking lots, widening of pedestrians if possible. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  323 - The Genesis of Academic Influencers in Iran
  Abbas  varijkazemi
  The present article studies, influential academic people in Iran and indicate that the position of influencers in the academic environment has also found its place.To conduct this study, we first investigate the concept of fame culture and then review the relationship b More

  The present article studies, influential academic people in Iran and indicate that the position of influencers in the academic environment has also found its place.To conduct this study, we first investigate the concept of fame culture and then review the relationship between university and influencer culture. The next step is to study the typology of academic influencers in Iran through a literature review, as well as the study of seven professors’ Instagram pages. In general, three general types have been identified. The first category is traditional celebrities, who mostly have a small presence on social networks. The second category is professors-influencers, i.e. those who were born within social networks and whose presence, activity and reputation in the virtual world are greater than in the outside world. The third group has a hybrid identity and has an enriching experience in terms of age, but they are not related to the first group. In other words, they are on the border between the first and the second type, but they have a little bit of their former reputation in the world outside of cyberspace, and they have also created a new position for themselves in social networks.In the comparison between these three types of academics, the major distinctions of academic celebrities as emerging figures will be explained.The professors-influencers are born and live in social networks, they owe their fame to these networks, they establish a more horizontal relationship with their audience, they address a more general population, and they are well aware of the strength of weak ties. And finally, they appear in the role of new intellectual leaders in society. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  324 - Pathology of the Effect of Using Social Networks on the Religiosity of Girls and Women
  Tahmineh Shaverdi ReyhanehSadat Sahragard Monfard
  Women in relation to the role they play in the family, influence the formation of the identity of next generation;especially due to their role as a mother whom train the children and socializing the future generation. On the other hand, changes that occurred in the worl More

  Women in relation to the role they play in the family, influence the formation of the identity of next generation;especially due to their role as a mother whom train the children and socializing the future generation. On the other hand, changes that occurred in the world in recent decades have provided opportunities to increase this influence. The main object of current research has been understanding the relationship between the use of virtual social networks and the religiosity of women and girls. A quantitative (survey) method has been applied in this research. The research tool is a questionnaire along with an interview. The statistical population of this research consists of women and girls aged 18 to 65 from the cities of Hamedan, Shar-e-Kurd, Shiraz, Ahvaz, Tehran and Kerman, which was conducted based on the evaluation of the developmental level of the provinces announced by the Management and Planning Organization, as well as, the Internet Penetration Rate. The sample size was 1418 people and themulti-stage sampling method were applied. Findings shows that although social networks have an effect on the religiosity of women and girls of the target community,but the effect coefficients are more on ritual dimensions than on belief dimensions and experiential dimensions of religiosity. Also, finding indicat social networks have some effect on Islamic behavior. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  325 - Reviewing and analyzing the content of separate and integrated curriculum in Iranian schools
  محمد  ایلخانی پور
  Building information modeling as an efficient tool to optimize, manage and plan construction projects has created a revolution in the construction industry in recent years. But the slow expansion of this powerful technology in third world countries, including Iran, has More

  Building information modeling as an efficient tool to optimize, manage and plan construction projects has created a revolution in the construction industry in recent years. But the slow expansion of this powerful technology in third world countries, including Iran, has caused many concerns in this field and it has many problems and obstacles in front of it, and with the passage of several years since the introduction of BIM in it, an effective action to inform and introduce This process has not been done by the government to the country's construction industry, as well as the evaluation and creation of grounds for its implementation and application. Considering the importance of the topic, the purpose of the research was to investigate the reasons for the inefficiency and use of building information modeling (BIM) in Shiraz city. The research method was applied and of the descriptive-correlation type. In this research, using library studies and interviews with experts, the reasons for the ineffectiveness of building information modeling were identified. Then, using the DEMATEL technique, the internal relationship between the identified components was determined. Identified parameters were calculated by applying the network analysis method (ANP) in the form of pairwise comparisons and the weight of each factor, which shows their influence, was calculated in the Superdesign software. The results regarding determining the weight of the criteria and prioritizing the effective factors indicate that among the factors, financial criterion with a weight of 0.31, technical criterion with a weight of 0.38, human resources criterion with a weight of 0.13, and time criterion with a weight of 0.19 respectively have They were the highest ranks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  326 - Spam Detection in Twitter by Ensemble Learning Approach
  Maryam Fasihi Mohammad Javad shayegan zahra hosieni zahra sejdeh
  Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributi More

  Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributing spam posts. This paper employs a combination of machine learning methods to identify spam at the tweet level. The proposed method utilizes a feature extraction framework in two stages. In the first stage, Stacked Autoencoder is used for feature extraction, and in the second stage, the extracted features from the last layer of Stacked Autoencoder are fed into the softmax layer for prediction. The proposed method is compared and evaluated against some popular methods on the Twitter Spam Detection corpus using accuracy, precision, recall, and F1-score metrics. The research results indicate that the proposed method achieves a detection of 78.1%. Overall, the proposed method, using the majority voting approach with a hard selection in ensemble learning, outperforms CNN, LSTM, and SCCL methods in identifying spam tweets with higher accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  327 - Using Sentiment Analysis and Combining Classifiers for Spam Detection in Twitter
  mehdi salkhordeh haghighi Aminolah Kermani
  The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one More

  The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output will detect spam text or normal text. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods to them, we separately calculate the Tweeter spam detection. Eventually, we enter the output of both approaches into a Meta Classifier so that its output specifies the final spam detection or the normality of the tweet text. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  328 - Outage and Throughput Analysis of Bidirectional Cognitive Amplify-and-Forward Relaying Networks with Wireless Power Transfer
  Ehsan Soleimani Nasab
  Cognitive radio is a promising technology which aims to achieve better frequency spectrum utilization. On the other hand, wireless energy harvesting can provide extra energy requirement at the nodes. Two scenarios in a two-way network are assumed where in the first scen More

  Cognitive radio is a promising technology which aims to achieve better frequency spectrum utilization. On the other hand, wireless energy harvesting can provide extra energy requirement at the nodes. Two scenarios in a two-way network are assumed where in the first scenario, relay harvests its required energy from end-sources of secondary network in presence of cognitive radio network and in the second scenario, both end-sources harvest energy from relay in secondary network. Both the Nakagami-m fading caused by signal propagation and the interference at relay caused by primary users in a cognitive radio network are considered. Closed-form expressions for outage probability and throughput of bidirectional cognitive radio amplify-and-forward relaying network using energy harvesting and wireless power transfer techniques over independent and non-identically distributed (i.n.i.d.) Nakagami-m fading channels are proposed. The analytical derivations are validated employing Monte Carlo simulations, where it is demonstrated that the first scenario always outperforms the second one, while both scenarios perform better than no energy harvesting case. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  329 - Explaining the adoption process of software-oriented networks (SDN) using the foundational data method and systems approach
  Elham Ziaeipour ali rajabzadeh ghotri Alireza Taghizadeh
  Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agi More

  Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agility, management and control of current network devices as well as upcoming communication technologies reduces expenses and creates innovative businesses. Although, service providers are very interested in deploying SDN to transform their static infrastructures to a dynamic and programmable platform, they do not consider it as one of their priorities and still depend on traditional methods to manage their network. Therefore, this study highlights the factors affecting the acceptance of SDN architecture and its application by the national telecom operators, and proposes a comprehensive and new paradigm model using a systems approach and Grounded theory (Strauss and Corbin model). This innovative model is provided by systematically reviewing the theoretical foundations and conducting in-depth interviews with managers and experts in telecom industry. During the modeling process, more than a thousand initial codes were determined. Finally, based on the opinion of experts on these codes, a total of 73 open codes, 12 axial codes and 6 main categories have been extracted. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  330 - A Recommender System for Scientific Resources Based on Recurrent Neural Networks
  Hadis Ahmadian Seyed Javad  Mahdavi Chabok Maryam  Kheirabadi
  10.61186/jist.39439.11.44.282
  Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses direct More

  Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses directly affect the number of active participants; on the other hand, it becomes difficult for teachers to guide students in choosing the appropriate learning resource due to the abundance of online learning resources. Students also find it challenging to decide which educational resources to choose according to their condition. The resource recommender system can be used as a Guide tool for educational resource recommendations to students so that these suggestions are tailored to the preferences and needs of each student. In this paper, it was presented a resource recommender system with the help of Bi-LSTM networks. Utilizing this type of structure involves both long-term and short-term interests of the user and, due to the gradual learning property of the system, supports the learners' behavioral changes. It has more appropriate recommendations with a mean accuracy of 0.95 and a loss of 0.19 compared to a similar article. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  331 - A Systematic Overview of Entrepreneurial Marketing: An Analysis of Components and Deterrents in Online Business Using the Hybrid Technique
  mahdi kasegarha Mohammad javad  Taghi porian Javad  Gilanipor Mehran  Mokhtari
  10.61186/jstpi.39530.19.73.8
  The interaction of marketing and entrepreneurship has occupied an important part of marketing research in the last decade. Marketing has a lot to offer entrepreneurs. Entrepreneurial marketing is a concept that has emerged from the level of sharing of two areas of marke More

  The interaction of marketing and entrepreneurship has occupied an important part of marketing research in the last decade. Marketing has a lot to offer entrepreneurs. Entrepreneurial marketing is a concept that has emerged from the level of sharing of two areas of marketing and entrepreneurship. Entrepreneurial marketing in the field of online business is always growing and its fields are expanding day by day. Entrepreneurial marketing requires a valid and reliable scale to measure the underlying dimensions and factors in any type of business environment, which requires that it be done in a principled manner and in accordance with scientific methods. Therefore, by understanding this issue, by conducting this research, we are seeking to identify the components and inhibiting factors with the help of hybridization method. The considered articles and researches were considered from among the studied articles and books, using the CASP method, and the main indicators were extracted through the document analysis method and using the MaxQD software. Necessary aggregation and clustering were done, then they were organized in the form of concepts and components. The results showed that the factors inhibiting entrepreneurial marketing include two main components of internal factors (environmental disturbance, government problems, limited market share, lack of customer commitment, technological change, and market problems) and external factors (management challenges, human barriers, lack of resources, and limitations) are classified. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  332 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
  Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem
  10.61186/jist.39680.11.44.359
  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More

  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  333 - Choosing the most suitable personality questions in the measurement of personality dimensions: combining the latent trait theory and network data analysis
  Maryam Mohtashami Mohammad Hossein  Zarghami Beheshteh Niooshah
  <p>The word personality refers to the uniqueness, individuality and subjectivity of the subject being studied. The measurement of such a dynamic and complex concept is considered a fundamental challenge in the field of methodology for the measurement of psychological co More

  <p>The word personality refers to the uniqueness, individuality and subjectivity of the subject being studied. The measurement of such a dynamic and complex concept is considered a fundamental challenge in the field of methodology for the measurement of psychological constructs. The aim of this research is to present a new method in two different parts of personality questionnaire question analysis: a) personality questionnaire question dimensions obtained from the implementation of questionnaires on independent samples through correspondence analysis and b) question prioritization using from the network data analysis method based on the importance of questions in each dimension. To achieve these goals, 32 personality questionnaires - which cover most of the application areas of personality questionnaires - were implemented on 82,988 volunteers via web-based forms. Correspondence analysis results show that personality has two dominant dimensions that explain about 75% of personality variance. The results of network data analysis show that the important questions in different indexes are not necessarily the same and the selection of questions based on a specific index should be based on the meaning of that index, however, according to the correlation structure of the priority of questions in the index network, a general index was defined based on which questions were prioritized in two dimensions of personality. The result of the present research led to the presentation of an algorithm for selecting personality questions in personality dimensions.</p> Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  334 - Analysis of Thematic Trends of Smart City Studies in the Recent Decade (From the Emergence of the Fourth Industrial Revolution to 2021)
  Alireza Noruzi Mohammad Reza Vasfi Somayyeh Jafari Baghiabadi
  10.52547/jstpi.39816.19.73.10
  The fourth industrial revolution, with the convergence of new technologies, has led to the emergence of smart cities. While the smartening of cities by governments has been happening at a high speed, the concept and implementation of the smart city are still being updat More

  The fourth industrial revolution, with the convergence of new technologies, has led to the emergence of smart cities. While the smartening of cities by governments has been happening at a high speed, the concept and implementation of the smart city are still being updated and changed. In this regard, the current research aims to analyze the thematic trends of publications on smart city in the Web of Science (WoS) database from the emergence of the fourth industrial revolution to 2021, and track the thematic trends of these studies. This scientometric research was carried out descriptively with the content analysis method, using the techniques of co-occurrence analysis and social network analysis. The trends of publications and citation influence of studies in the field of smart cities in the last decade has had an upward growth of 38.78% and 69.49%, respectively. In the three time periods, "IoT, City, Internet, and Wireless Sensor Network" have the most frequency, "IOT, City, Internet, and Wireless Sensor Network" have the most connections, and "ipv6, Sustainable City, and Urban Development" in 2012–2015, "Taxonomy, cloud objects and distributed computing" in 2016–2018 and "Literature review, urban informatics, and sustainable urban development" in 2019-2021 have received the most citations. In 2012–2015, clusters of sensor networks, smartphones, genetic algorithms, advanced sensors, ubiquitous city, and m2m; in 2016-2018 clusters of deep learning, participation, evolutionary algorithms, ubiquitous computing, and smart cities; and in 2019-2021 blockchain clusters, participation citizenship, IoT, topics, technologies, and tools.multi-agent systems, and Brazil were identified. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  335 - Software-Defined Networking Adoption Model: Dimensions and Determinants
  Elham Ziaeipour Ali Rajabzadeh Ghotri Alireza Taghizadeh
  10.61186/jist.40088.11.44.368
  The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Know More

  The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Knowledge-based network. In this regard, telecom operators are interested in deploying SDN to migrate their infrastructure from a static architecture to a dynamic and programmable platform. However, it seems that they do not consider SDN as one of their priorities and still depend on traditional methods to manage their network (especially in some developing countries such as Iran). Since the first step in applying new technologies is to accept them, we have proposed a comprehensive SDN adoption model with the mixed-method research methodology. At first, the theoretical foundations related to the research problem were examined. Then, based on Grounded theory, in-depth interviews were conducted with 12 experts (including university professors and managers of the major telecom operators). In result, more than a thousand initial codes were determined, which in the review stages and based on semantic commonalities, a total of 112 final codes, 14 categories and 6 themes have been extracted using open, axial and selective coding. Next, in order to confirm the indicators extracted from the qualitative part, the fuzzy Delphi method has been used. In the end, SPSS and SmartPLS 3 software were used to analyze the data collected from the questionnaire and to evaluate the fit of the model as well as confirm and reject the hypotheses. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  336 - Data-Driven Sliding Mode Control Based on Projection Recurrent Neural Network for HIV Infection: A Singular Value Approach
  Ashkan  Zarghami mehdi  Siahi Fereidoun Nowshiravan Rahatabad
  In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infect More

  In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infection mathematical formulae and ensures that the physical limits of the actuator are reached. This is accomplished by creating the concepts of model-free adaptive control, in which the relation between input and output is described using local dynamic linearized models based on quasi-partial derivatives. To determine the DDSMC law, a performance index is first defined based on the fulfillment of a discrete-time exponential reaching condition. By turning this index into a quadratic programming problem, the dynamics of the PRNN are extracted based on projection theory. The closed-loop system is explicitly determined using the optimizer output equation and the closed-loop stability analysis is evaluated using the singular value approach. The simulation results reveal that the proposed algorithm has robust performance in conducting the state variables of HIV infection to the healthy equilibrium point in the face of model uncertainty and external disturbances when compared to one of the newest control techniques. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  337 - Identification of Cancer-Causing Genes in Gene Network Using Feedforward Neural Network Architecture
  مصطفی اخوان صفار abbas ali rezaee
  Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interact More

  Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interactions, and in this way, they cause cell dysfunction and the occurrence of disease and cancer. So far, various methods have been proposed to predict and classify cancer-causing genes. These methods mostly rely on genomic and transcriptomic data. Therefore, they have a low harmonic mean in the results. Research in this field continues to improve the accuracy of the results. Therefore, network-based methods and bioinformatics have come to the aid of this field. In this study, we proposed an approach that does not rely on mutation data and uses network methods for feature extraction and feedforward three-layer neural network for gene classification. For this purpose, the breast cancer transcriptional regulatory network was first constructed. Then, the different features of each gene were extracted as vectors. Finally, the obtained vectors were given to a feedforward neural network for classification. The obtained results show that the use of methods based on multilayer neural networks can improve the accuracy and harmonic mean and improve the performance compared to other computational methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  338 - A Horizon for Sentiment Analysis in Social Networks based on Interpreting Contents
  Maryam Tayefeh Mahmoudi َAmirmansour  Yadegari Parvin Ahmadi Kambiz Badie
  Interpreting contents in social networks with the aim of analyzing the sentiment of their narrators is of particular significance. In this paper, we present a framework for such a purpose, which is able to classify the messages hidden in contents based on using some rul More

  Interpreting contents in social networks with the aim of analyzing the sentiment of their narrators is of particular significance. In this paper, we present a framework for such a purpose, which is able to classify the messages hidden in contents based on using some rule-type protocols with high abstraction level. According to this framework, items such as prosodic of a content's narrator, context of disseminating a content and the key propositions in a content's text are regarded in the condition part of a protocol, while the possible classes for the message in a content are considered as its action part. It is to be noted that the proposed rule-type protocols can equally be used for other languages due to the generic-ness of the above-mentioned items. Results of computer simulations on a variety of different contents in the social networks show that the proposed framework is sufficiently capable of analyzing the sentiment of the contents' narrators in these networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  339 - Multi-level ternary quantization for improving sparsity and computation in embedded deep neural networks
  Hosna Manavi Mofrad Seyed Ali ansarmohammadi Mostafa Salehi
  Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and co More

  Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and computational resources. Network compression techniques can overcome these challenges. Quantization and pruning methods are the most important compression techniques among them. One of the famous quantization methods in DNNs is the multi-level binary quantization, which not only exploits simple bit-wise logical operations, but also reduces the accuracy gap between binary neural networks and full precision DNNs. Since, multi-level binary can’t represent the zero value, this quantization does’nt take advantage of sparsity. On the other hand, it has been shown that DNNs are sparse, and by pruning the parameters of the DNNs, the amount of data storage in memory is reduced while computation speedup is also achieved. In this paper, we propose a pruning and quantization-aware training method for multi-level ternary quantization that takes advantage of both multi-level quantization and data sparsity. In addition to increasing the accuracy of the network compared to the binary multi-level networks, it gives the network the ability to be sparse. To save memory size and computation complexity, we increase the sparsity in the quantized network by pruning until the accuracy loss is negligible. The results show that the potential speedup of computation for our model at the bit and word-level sparsity can be increased by 15x and 45x compared to the basic multi-level binary networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  340 - Friendship Selection Based on Social Features in Social Internet of Things
  Mohammad Mahdian S.Mojtaba Matinkhah
  The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right More

  The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  341 - A New Hybrid Method Based on Intelligent Algorithms for Intrusion Detection in SDN-IoT
  Zakaria Raeisi Fazlloah Adibnia Mahdi Yazdian
  In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networ More

  In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networks and the Internet of Things have made SDN-IoT security one of the most important concerns. On the other hand, the use of intelligent algorithms has been an opportunity that these algorithms have been able to make significant progress in various cases such as image processing and disease diagnosis. Of course, intrusion detection systems for SDN-IoT environment still face the problem of high false alarm rate and low accuracy. In this article, a new hybrid method based on intelligent algorithms is proposed. The proposed method integrates the monitoring algorithms of frequent return gate and unsupervised k-means classifier in order to obtain suitable results in the field of intrusion detection. The simulation results show that the proposed method, by using the advantages of each of the integrated algorithms and covering each other's disadvantages, has more accuracy and a lower false alarm rate than other methods such as the Hamza method. Also, the proposed method has been able to reduce the false alarm rate to 1.1% and maintain the accuracy at around 99%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  342 - A Novel Multi-Step Ahead Demand Forecasting Model Based on Deep Learning Techniques and Time Series Augmentation
  Hossein Abbasimehr Reza Paki
  In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formula More

  In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formulated as a time series forecasting task. In the context of time series forecasting, deep learning methods have demonstrated good accuracy in predicting complex time series. However, the excellent performance of these methods is dependent on the amount of data available. For this purpose, in this study, we propose to use time series augmentation techniques to improve the performance of deep learning methods. In this study, three new methods have been used to test the effectiveness of the proposed approach, which are: 1) Long short-term memory, 2) Convolutional network 3) Multihead self-attention mechanism. This study also uses a multi-step forecasting approach that makes it possible to predict several future points in a forecasting operation. The proposed method is applied to the actual demand data of a furniture company. The experimental results show that the proposed approach improves the forecasting accuracy of the methods used in most different prediction scenarios Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  343 - Increasing Total Throughput, Reducing Outage to Zero, and Reducing Power Consumption in a Cellular Network
  Mohsen Seyyedi Saravi Mohammadreza Binesh Marvasti Seyedeh Leili Mirtaheri Seyyed Amir Asghari
  Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by as More

  Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by assuming a limited transmission capacity for mobile nodes in a wireless cellular network as limitations in the transmission capacity often exist both in terms of hardware, battery limitations, and regulatory rules in the real world. In presenting this framework, a new idea was proposed once the existing methods were examined and their advantages and disadvantages were compared, respectively. After the formula was proved, the idea's simulation steps were performed via MATLAB. Present methods either increased the throughput by assuming unlimited transmission power or prevented some nodes from accessing the communication service. The simulation results showed that the proposed algorithm reduced the power consumption of mobile nodes in the network by a quarter in addition to increasing the throughput by 27%, and further operated in a way that no node would lose communication service Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  344 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
  Vahid Safari dehnavi masoud shafiee
  Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the vo More

  Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  345 - A comprehensive survey on the influence maximization problem in social networks
  mohsen taherinia mahdi Esmaeili Behrooz Minaei
  With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency a More

  With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  346 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
  Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi Golsorkhtabaramiri
  10.61186/jist.41465.12.45.62
  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension More

  To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  347 - Presenting the ICT Policies Implementation Model of the 6th Development Using the Neural Network Method
  Nazila Mohammadi Gholamreza   Memarzadeh Tehran Sedigheh Tootian Isfahani
  It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the eff More

  It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the effective factors on the implementation of Iran's ICT policies with the help of the neural network technique and based on Giddens' constructive theory. From the point of view of conducting it, this research is of a survey type and based on the purpose, it is of an applied type because it is trying to use the results of the research in the Ministry of Communication and Information Technology and the Iranian Telecommunications Company. Data collection is based on library and field method. The tool for collecting information is research researcher-made questionnaire. The statistical population of the research is information and communication technology experts at the headquarters of Iran Telecommunication Company (810 people), of which 260 people were randomly selected as a sample based on Cochran's formula. MATLAB software was used for data analysis. According to the findings, the best combination for development is when all input variables are considered at the same time, and the worst case is when the infrastructure development variable is ignored, and the most important based on network sensitivity analysis is related to infrastructure development and the least important is related to content supply. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  348 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh Ali Movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  349 - Detection of Quantized Sparse Signals Using Locally Most Power Full Detector in Wireless Sensor NetworkS
  Abdolreza Mohammadi Amin Jajarmi
  This paper addresses the problem of distributed detection of stochastic sparse signals in a wireless sensor network. Observations/local likelihood ratios in each sensor node are quantized into 1-bit and sent to a fusion center (FC) through non-ideal channels. At the FC, More

  This paper addresses the problem of distributed detection of stochastic sparse signals in a wireless sensor network. Observations/local likelihood ratios in each sensor node are quantized into 1-bit and sent to a fusion center (FC) through non-ideal channels. At the FC, we propose two sub-optimal detectors after that the data are fused based on the locally most powerful test (LMPT). We obtain the quantization threshold for each sensor node via an asymptotic analysis of the performance of the detector. It is realized that the quantization threshold depends on the bit error probability of the channels between the sensor nodes and the FC. Simulation results are carried out to confirm our theoretical analysis and to depict the performance of the proposed detectors. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  350 - Sociological explanation of the impact of modernization and social networks on ethnic tendencies Case study: Ilam province)
  Abdolhossein  Rahmati Nabiallah  Ider Abdolreza  Hashemi
  This research was conducted with the aim of investigating modernization and social networks, two important components of globalization and its effect on the ethnic tendencies of the people of Ilam province, to show how the identities of the people of Ilam province relat More

  This research was conducted with the aim of investigating modernization and social networks, two important components of globalization and its effect on the ethnic tendencies of the people of Ilam province, to show how the identities of the people of Ilam province relate to global identities. In this research, the statistical population is all people living in the province. Ilam with a population of 580,158 people, based on Cochran's formula, the statistical sample size is 270 people, and the sampling method is simple probability, the survey method and the data collection tool of Hop Rashnameh. Cronbach's alpha coefficient was used to measure the reliability of the data and face validity method was used to measure the validity. The results of the research were analyzed using Pearson's correlation coefficient, variance analysis and regression analysis. They are an important component of globalization, there is a significant relationship with ethnic tendencies. This means that traditional communities like Ilam province have become dependent on the modern world, which has caused a decrease in the sense of belonging and ethnic attachment of the people of Ilam province. Based on the findings, it can be concluded that the ethnic trends in Ilam province, unlike the past, in the contemporary era, have gone out of a solid and uniform state, and modernization and social networks have had an impact on it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  351 - Investigating Twitter campaigns of Violence Against Women
  shima nasertork ali delavar afsaneh mozaffari tahmores shiri
  One of the most important topics that social networks -Twitter- have paid attention to is violence against women in Iran. The most important theoretical support in the emergence of these campaigns is the reflection of the texts about violence against women. One of the m More

  One of the most important topics that social networks -Twitter- have paid attention to is violence against women in Iran. The most important theoretical support in the emergence of these campaigns is the reflection of the texts about violence against women. One of the most important factors in the emergence of such waves is the important activists in the field of these social networks, who use their followers to reflect these contents. The most important campaigns in terms of the number of tweets and retweets was the "Girls of the Revolution" campaign. The aim of the research is to analyze the performance of Twitter in reflecting the content of violence against women in Iran based on the most important campaigns that have emerged in the last decade. This issue was analyzed based on the theoretical foundations of types of violence and networked movements. The research method is the content analysis of tweets and hashtags related to campaigns from 2016 to 2016. The research results showed that the most important themes are about social and political violence against women. In this regard, some campaigns and hashtags have caused insecurities in the society the most important of which was the campaign of the girls of Enghelab Street in terms of the number of tweets and retweets. the analysis of the themes of tweets showed the absence of news, posts and tweets of people related to campaigns and hashtags. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  352 - Assessment of organization efficiency using an integrated model of EFQM-network data envelopment analysis
  Alireza Khosravi Mohammad Fallah Esmaeil Najafi
  10.61186/mf.42379.22.77.57
  Establishment of the modern managerial systems is one of the most important steps for the excellence of organizations. The European Foundation of Quality Management (EFQM) is one of the technics that has started from Europe during the two recent decades and nowadays man More

  Establishment of the modern managerial systems is one of the most important steps for the excellence of organizations. The European Foundation of Quality Management (EFQM) is one of the technics that has started from Europe during the two recent decades and nowadays many organizations are in the process of implementing this model with- in their managerial domain. Data Envelopment Analysis (DEA) is a known nonparametric tool for evaluating the organization’s efficiency. In addition to having the capability to measure the total efficiency of a system, network DEA is able to calculate the efficiency of system components. Since EFQM can be considered as a multi-stage system, existence of network DEA along with it can improve the analysis. In this paper, an integrated model has been suggested based on EFQM and Network DEA for assessing the organization’s operation. For this purpose, at first the EFQM has been considered as a ‘four-stage system’ including leadership (the first stage), activities related to employees, policy, strategy and partnership (the second stage), processes (the third stage), and results (the fourth stage). Then considering 32 sub-criteria in EFQM, input and output variables have been determined for each one of the four stage s and on this basis math form for Network DEA model has been developed for this 4-satge system. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  353 - A semantic sentiment recognition model based on ontology and cellular deep learning automata
  Hoshang Salehi Reza Ghaemi maryam khairabadi
  Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to More

  Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to the news of the day, current events, etc., and recognizing these feelings faces many challenges. So far, various methods such as machine learning, statistical approaches, artificial intelligence, etc., have been proposed for the purpose of detecting emotions, which despite their many applications; But they have not yet been able to have acceptable accuracy, transparency and accuracy. Therefore, in this article, an ontology-based semantic analysis model using cellular deep learning automata based on GMDH deep neural network is presented. Ontology approach is used to select salient features based on production rules and cellular deep learning automata is used to classify user sentiments. The main innovation of this article is the proposed algorithm that a deep learning method is developed to process only one expression and then by transferring it to the field of cellular automata, parallel or distributed processing is provided. In this article, the data sets of Amazon customers, Twitter, Facebook, fake news of COVID-19, Amazon and fake news network are used. By simulating the proposed method, it was observed that the proposed method has an average improvement of 3% compared to other methods Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  354 - Investigating the Sociological Causes of the Tendency to Use Mobile Social Networks
  Saeed  Mohammadi Sadegh hossein ebrahim
  Social networks have a significant impact on the collective and individual lives of people, and the amount and scope of their use increases every day. Social factors are among the most decisive factors in the field of using these types of networks. Therefore, in this re More

  Social networks have a significant impact on the collective and individual lives of people, and the amount and scope of their use increases every day. Social factors are among the most decisive factors in the field of using these types of networks. Therefore, in this research, an attempt has been made to study the effect of these types of factors including family, friends, relatives, ethnicity, media and social base on the use of social networks and its effect on the interpersonal and family relationships of Zahedan youth. The method of this research is the survey and the method of gathering information using a structured interview-based questionnaire tool, and the sample size is 300 people. In order to extract the data of this research from spss software and to analyze the data from the statistical tests of analysis of variance, the correlation coefficient was used for the intensity of the link between the variables along with the significance level of the test (sig). This research aims to answer the questions of how much the youth of Zahedan use mobile social networks? And what are the social factors affecting the use of these mobile social networks by the youth of Zahedan. The results of this research showed that: 49% of respondents use mobile social networks. Social factors such as: interpersonal relationships, family and family relationships, etc. can predict a total of 88.2% of the variance of the dependent variable, i.e. the use of mobile social networks Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  355 - Presenting a risky investment model in the insurance industry based on the fuzzy network analysis process
  Yazdan Gudarzi Farahani Amir Mohammadzade Mohsen Mehrara Zulikha  Morsali Aruznaq
  The purpose of this article is to identify and present a high-risk investment model for insurance institutions in order to establish a risky insurance investment fund. The insurance industry is one of the strongest and most important supporting institutions of financial More

  The purpose of this article is to identify and present a high-risk investment model for insurance institutions in order to establish a risky insurance investment fund. The insurance industry is one of the strongest and most important supporting institutions of financial institutions, through risk coverage and investment of technical reserves in financial and real assets and the implementation of profitable economic activities, it plays a very important role in the dynamics of financial markets. Creating a decision-making model in a fuzzy environment provides this possibility for decision-makers so that they can more easily comment on the evaluation of options and determining the importance of decision-making criteria. In this article, it has been tried to identify and introduce the most important factors and effective factors of venture capital (VC) using the technique of fuzzy network analysis process in the insurance industry. The data needed for the research has been collected through in-depth interviews with a number of experts and researchers in the insurance industry, as well as a questionnaire. Due to the lack of independence and the existence of dependence between the effective factors, the method of fuzzy network analysis process was used to identify the possible dependencies between factors and measure them for the development of the VC model, and the results were prioritized with the method of non-fuzzy network analysis process. Is. The findings of the research show that operating cycle indicators, total asset turnover ratio, total investment return, loss factor, asset-liability ratio are ranked first to fifth among VC indicators. VCs help the insurance industry establish internal accounting rules and standardize their financial statements. In other words, VC support modifies the "hard" and "soft" information required for insurance.. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  356 - Petrophysical Modeling of Lower Zone of Ratawi Formation, using Neural Network Method in Assimilating Seismic and Geological Well Log Data
  Javid Hanachi Alireza Bashari
  Esfandiar field is located at the northern part of the Persian . This field is a single large anticline with Lulu field of Saudi Arabia, with , 20 KM length and 7 KM width. The field was discovered in 1966 by drilling of well E1, on the northern culmination of t More

  Esfandiar field is located at the northern part of the Persian . This field is a single large anticline with Lulu field of Saudi Arabia, with , 20 KM length and 7 KM width. The field was discovered in 1966 by drilling of well E1, on the northern culmination of the field. wells E3 and E2 were drilled at the top of structure in the southern part of the field. DSTs tests results of E1 proved that the top of Lower Ratawi formation contain 15 m oil column. E3 well test result regards as a dry hole DSTs test results of E2 were not conclusive due to inadequate testing plans . E4 Appraisal well contained, 14 m oil column at the Lower Ratawi. Log interpretations results indicated, E2 and E3 wells contains oil in Yamama formation in the southern part of the field which has not been tested properly. Lower Ratawi (Top oil-bearing zone ), Zone 'B' of Lower Ratawi (Oil bearing zone at bottom), Yamama were constructed based on the existing data. Petrophysical and geophysical data has been used for the Lower Ratawi reservoir, as a result the geological models (structural and porosity models), with applying, related software’s and neural network geophysical method are generated . At the conclusion, the recommended plan consists of horizontal drilling wells for oil production in Lower Ratawi in the north of the field has been proposed. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  357 - A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
  Mohammad Javad shayegan mohadeseh valizadeh
  <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of perso More

  <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.</span></p> Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  358 - Modeling of Solar Power Plant Using a Neural Network Based on the Equivalent of a Single Diode
  Ali Reza reisi Rohollah  Abdollahi
  <p style="padding-right: 30px; text-align: justify;"><!-- [if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Norma More

  <p style="padding-right: 30px; text-align: justify;"><!-- [if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>FA</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:EnableOpenTypeKerning/> <w:DontFlipMirrorIndents/> <w:OverrideTableStyleHps/> </w:Compatibility> <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> <m:mathPr> <m:mathFont m:val="Cambria Math"/> <m:brkBin m:val="before"/> <m:brkBinSub m:val="&#45;-"/> <m:smallFrac m:val="off"/> <m:dispDef/> <m:lMargin m:val="0"/> <m:rMargin m:val="0"/> <m:defJc m:val="centerGroup"/> <m:wrapIndent m:val="1440"/> <m:intLim m:val="subSup"/> <m:naryLim m:val="undOvr"/> </m:mathPr></w:WordDocument> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:LatentStyles DefLockedState="false" DefUnhideWhenUsed="false" DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> <w:LsdException Locked="false" Priority="0" QFormat="true" Name="Normal"/> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 1"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 9"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 1"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 2"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 3"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 4"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 5"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 6"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 7"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 8"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 9"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="header"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footer"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index heading"/> <w:LsdException Locked="false" Priority="35" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="caption"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="table of figures"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope return"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="line number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="page number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="table of authorities"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="macro"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="toa heading"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 5"/> <w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Closing"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Signature"/> <w:LsdException Locked="false" Priority="0" SemiHidden="true" UnhideWhenUsed="true" Name="Default Paragraph Font"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Message Header"/> <w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Salutation"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Date"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Note Heading"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Block Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Hyperlink"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="FollowedHyperlink"/> <w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/> <w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Document Map"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Plain Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="E-mail Signature"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Top of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Bottom of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal (Web)"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Acronym"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Cite"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Code"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Definition"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Keyboard"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Preformatted"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Sample"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Typewriter"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Variable"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Table"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation subject"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="No List"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Contemporary"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Elegant"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Professional"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Balloon Text"/> <w:LsdException Locked="false" Priority="39" Name="Table Grid"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Theme"/> <w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/> <w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading"/> <w:LsdException Locked="false" Priority="61" Name="Light List"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/> <w:LsdException Locked="false" SemiHidden="true" Name="Revision"/> <w:LsdException Locked="false" Priority="34" QFormat="true" Name="List Paragraph"/> <w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/> <w:LsdException Locked="false" Priority="30" QFormat="true" Name="Intense Quote"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 1"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/> <w:LsdException Locked="false" Priority="19" QFormat="true" Name="Subtle Emphasis"/> <w:LsdException Locked="false" Priority="21" QFormat="true" Name="Intense Emphasis"/> <w:LsdException Locked="false" Priority="31" QFormat="true" Name="Subtle Reference"/> <w:LsdException Locked="false" Priority="32" QFormat="true" Name="Intense Reference"/> <w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/> <w:LsdException Locked="false" Priority="37" SemiHidden="true" UnhideWhenUsed="true" Name="Bibliography"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> <w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/> <w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/> <w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/> <w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/> <w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/> <w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 1"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 2"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 3"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 4"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 5"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 6"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 1"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 1"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 1"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 1"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 1"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 2"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 2"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 2"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 2"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 2"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 3"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 3"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 3"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 3"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 3"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 4"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 4"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 4"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 4"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 4"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 5"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 5"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 5"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 5"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 5"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 6"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 6"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 6"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 6"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 6"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 6"/> </w:LatentStyles> </xml><![endif]--><!-- [if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0mm 5.4pt 0mm 5.4pt; mso-para-margin:0mm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif;} </style> <![endif]--></p> <p class="Abstract" style="padding-right: 30px; text-align: justify;"><span style="mso-bidi-font-weight: normal;">Various methods have been proposed for modeling solar panels, but modeling solar power plants using them is associated with challenges. In equivalent circuit-based methods, the modeling depends on factory data that changes over time. Modeling of voltage-current characteristic using intelligent methods such as neural network was less considered due to the low accuracy of modeling. In this article, a method independent of the manufacturer's data for modeling the solar power plant is presented, so that it is possible to accurately model the solar power plants that have been installed for several years. The proposed method consists of two steps, in the first step, open circuit voltage, maximum power point and short circuit current are modeled according to atmospheric conditions using neural network. In the second step, the unknown parameters of the equivalent circuit are determined by circuit analysis relations and using neural network outputs. Finally, to evaluate the proposed method, a 3-kW solar power plant was modeled, and the results show the appropriate accuracy of the proposed method for modeling the solar power plant.</span></p> <p style="padding-right: 90px; text-align: justify;">&nbsp;</p> Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  359 - The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data
  Sogand Dehghan shahriyar mohammadi rojiar pirmohamadiani
  Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of inform More

  Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  360 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
  Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad Edalatpanah
  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More

  Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  361 - Improving energy consumption in the Internet of Things using the Krill Herd optimization algorithm and mobile sink
  Shayesteh Tabatabaei
  Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging info More

  Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  362 - Identify and analyze decision points and key players in procurement process in the EPC companies
  Seyedeh Motahareh  Hosseini Mohammad aghdasim
  Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in o More

  Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in organizations and using tools such as process mining, a platform is provided for the use of data analysis approaches and better analysis of decisions, and managers can act in agile decision making. Selecting a supplier in the process of purchasing in complex projects is one of the basic and key decisions that affect the quality, cost and performance of the project. In this article, with a process perspective, the decision points in the purchasing process in a complex construction project in an EPC company have been discovered and the key players in the implementation of the process have been identified and analyzed through social network analysis. The results of this research have led to the investigation of decision points in the process, the performance of decision points and the identification of key people in decision making, which can be used to improve the company's future performance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  363 - Criticism and analysis of the philosophical foundations of Izutsu's method of semantics
  Najmeh  Rahnama falavarjani Mohammadreza Hajiesmaili mehdi motia
  Semantic research, semantic research has been based on the Izutsu method, and a large number of articles have been published based on this method. But less research has been paid attention to the etymology of this method. And most of the researches conducted in this fie More

  Semantic research, semantic research has been based on the Izutsu method, and a large number of articles have been published based on this method. But less research has been paid attention to the etymology of this method. And most of the researches conducted in this field have only been satisfied with the semantic aspect of this method, so this question remains unanswered, which philosophical theory is this semantic method based on? Is it a modern or postmodern view? In the meantime, the importance of answering this question is that it clarifies what was the paradigm of Izetsu in the design of this model and whether this view is consistent with what the Qur'an itself is trying to express or is this theory merely a way to answer the criteria of philosophy. The West is based on holy texts such as the Quran. In this field, it is not possible to obtain a research that can directly answer the mentioned question, therefore, the mentioned article is responsible for answering this question. And it has been concluded that Izutsu's method is neither a purely modern view nor an absolutely post-modern view. Rather, it is an eclectic view of both philosophies, which has accepted the postmodern relativistic view in ontology and used the structuralism and utilitarianism of modern philosophy in methodology. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  364 - The Model of Barriers for Machine-Made Carpet Supply Chain Resilience
  Esmaeil Mazroui Nasrabadi Amirhossein  Fallahinezhad2
  The machine-made carpet supply chain faces multiple disruptions and must be resilient. To be resilient, identifying barriers holds significant importance. Previous research has examined barriers without considering the stages of supply chain resilience. In this study, b More

  The machine-made carpet supply chain faces multiple disruptions and must be resilient. To be resilient, identifying barriers holds significant importance. Previous research has examined barriers without considering the stages of supply chain resilience. In this study, barriers to supply chain resilience were identified based on the five stages of supply chain resilience, and their model was presented. The research population consisted of experts in the supply chain of the machine-made carpet industry, using judgmental and snowball sampling methods. The sample size for identifying factors based on theoretical saturation was 14, while in the modeling stage, it was 10. The results indicated 20, 20, 16, 16, and 16 barriers in each of the five stages. Variables such as lack of trust, network complexity, lack of top management commitment, cultural challenges, and low human resource competence are of high importance based on their frequency in the five stages and the roles they play, requiring special attention. To overcome these barriers, it is suggested to focus on developing information infrastructure, employing innovative technologies, conducting training courses, and enhancing human resource management systems Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  365 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
  farinaz sanaei Seyed Abdollah  Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri
  10.61186/jict.44093.15.57.1
  Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of More

  Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  366 - An Intrusion Detection System based on Deep Learning for CAN Bus
  Fatemeh Asghariyan Mohsen Raji
  In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bu More

  In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bus lacks security systems such as authentication and encryption to deal with cyber-attacks, the need for an intrusion detection system to detect attacks on the CAN bus seem to be very necessary. In this paper, a deep adversarial neural network (DACNN) is proposed to detect various types of security intrusions in CAN buses. For this purpose, the DACNN method, which is an extension of the CNN method using adversarial learning, detects intrusion in three stages; In the first stage, CNN acts as a feature descriptor and the main features are extracted, and in the second stage, the discriminating classifier classifies these features and finally, the intrusion is detected using the adversarial learning. In order to show the efficiency of the proposed method, a real open source dataset was used in which the CAN network traffic on a real vehicle during message injection attacks is recorded on a real vehicle. The obtained results show that the proposed method performs better than other machine learning methods in terms of false negative rate and error rate, which is less than 0.1% for DoS and drive gear forgery attack and RPM forgery attack while this rate is less than 0.5% for fuzzy attack. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  367 - Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
  mohammadreza zeraatkarmoghaddam majid ghayori
  Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or i More

  Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or intrusions. In deep learning, the most important challenge for training neural networks is determining the hyperparameters in these networks. To overcome this challenge, we have presented a hybrid approach to automate hyperparameter tuning in deep learning architecture by eliminating the human factor. In this article, an IDS in IIoT based on convolutional neural networks (CNN) and recurrent neural network based on short-term memory (LSTM) using metaheuristic algorithms of particle swarm optimization (PSO) and Whale (WOA) is used. This system uses a hybrid method based on neural networks and metaheuristic algorithms to improve neural network performance and increase detection rate and reduce neural network training time. In our method, considering the PSO-WOA algorithm, the hyperparameters of the neural network are determined automatically without the intervention of human agent. In this paper, UNSW-NB15 dataset is used for training and testing. In this research, the PSO-WOA algorithm has use optimized the hyperparameters of the neural network by limiting the search space, and the CNN-LSTM neural network has been trained with this the determined hyperparameters. The results of the implementation indicate that in addition to automating the determination of hyperparameters of the neural network, the detection rate of are method improve 98.5, which is a good improvement compared to other methods. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  368 - FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare System
  Nasibeh Mohammadi Afshin Rezakhani Hamid Haj Seyyed Javadi Parvaneh asghari
  10.61186/jist.44500.12.46.90
  Giving access permission based on histories of access is now one of the security needs in healthcare systems. However, current access control systems are unable to review all access histories online to provide access permission. As a result, this study first proposes a More

  Giving access permission based on histories of access is now one of the security needs in healthcare systems. However, current access control systems are unable to review all access histories online to provide access permission. As a result, this study first proposes a method to perform access control in healthcare systems in real time based on access histories and the decision of the suggested intelligent module. The data is used to train the intelligent module using the LSTM time series machine learning model. Medical data, on the other hand, cannot be obtained from separate systems and trained using different machine-learning models due to the sensitivity and privacy of medical records. As a result, the suggested solution employs the federated learning architecture, which remotely performs machine learning algorithms on healthcare systems and aggregates the knowledge gathered in the servers in the second phase. Based on the experiences of all healthcare systems, the servers communicate the learning aggregation back to the systems to control access to resources. The experimental results reveal that the accuracy of history-based access control in local healthcare systems before the application of the suggested method is lower than the accuracy of the access control in these systems after aggregating training with federated learning architecture. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  369 - Evaluation and analysis of the use of virtual power plant simulation in the power grid
  Farid timuri Seyed Majid  Keshavarz
  Distributed generation refers to the generation of electricity near the consumer's location using small-scale generation units. Scattered energy sources are renewable energies and combined production (simultaneous production of electricity and heat). A virtual power pla More

  Distributed generation refers to the generation of electricity near the consumer's location using small-scale generation units. Scattered energy sources are renewable energies and combined production (simultaneous production of electricity and heat). A virtual power plant refers to a set of distributed production facilities such as wind turbines, solar power plants, small hydropower plants and any power generation sources that have the ability to cooperate with each other in a local area and are all controlled by a central control unit. The virtual power plant has been subjected to various validation tests and network connections (connected to the network and off the network) and has a high level of credibility and competence. This research includes an overview of some virtual power plant ideas that express a better understanding and a more general vision in this field, some general control structures are described and experimental fields are introduced. The distribution network is represented by the virtual power plant. The result shows that with the presence of storage resources and the possibility of being in the reservation market, the virtual power plant will benefit more. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  370 - Nonlinear Fractional Intelligent Controller for Photovoltaic Inverters
  Hadi Delavari Sara Arjmandpour
  <p style="text-align: justify; padding-right: 30px;"><!-- [if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Norma More

  <p style="text-align: justify; padding-right: 30px;"><!-- [if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:TargetScreenSize>800x600</o:TargetScreenSize> </o:OfficeDocumentSettings> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>FA</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:EnableOpenTypeKerning/> <w:DontFlipMirrorIndents/> <w:OverrideTableStyleHps/> </w:Compatibility> <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> <m:mathPr> <m:mathFont m:val="Cambria Math"/> <m:brkBin m:val="before"/> <m:brkBinSub m:val="&#45;-"/> <m:smallFrac m:val="off"/> <m:dispDef/> <m:lMargin m:val="0"/> <m:rMargin m:val="0"/> <m:defJc m:val="centerGroup"/> <m:wrapIndent m:val="1440"/> <m:intLim m:val="subSup"/> <m:naryLim m:val="undOvr"/> </m:mathPr></w:WordDocument> </xml><![endif]--><!-- [if gte mso 9]><xml> <w:LatentStyles DefLockedState="false" DefUnhideWhenUsed="false" DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> <w:LsdException Locked="false" Priority="0" QFormat="true" Name="Normal"/> <w:LsdException Locked="false" Priority="9" QFormat="true" Name="heading 1"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> <w:LsdException Locked="false" Priority="9" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index 9"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 1"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 2"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 3"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 4"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 5"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 6"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 7"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 8"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 9"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="header"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footer"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index heading"/> <w:LsdException Locked="false" Priority="35" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="caption"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="table of figures"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope return"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="line number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="page number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="table of authorities"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="macro"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="toa heading"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 5"/> <w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Closing"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Signature"/> <w:LsdException Locked="false" Priority="0" SemiHidden="true" UnhideWhenUsed="true" Name="Default Paragraph Font"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Message Header"/> <w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Salutation"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Date"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Note Heading"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Block Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Hyperlink"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="FollowedHyperlink"/> <w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/> <w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Document Map"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Plain Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="E-mail Signature"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Top of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Bottom of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal (Web)"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Acronym"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Cite"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Code"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Definition"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Keyboard"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Preformatted"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Sample"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Typewriter"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Variable"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Table"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation subject"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="No List"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Outline List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Simple 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Classic 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Contemporary"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Elegant"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Professional"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Balloon Text"/> <w:LsdException Locked="false" Priority="39" Name="Table Grid"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Theme"/> <w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/> <w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading"/> <w:LsdException Locked="false" Priority="61" Name="Light List"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/> <w:LsdException Locked="false" SemiHidden="true" Name="Revision"/> <w:LsdException Locked="false" Priority="34" QFormat="true" Name="List Paragraph"/> <w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/> <w:LsdException Locked="false" Priority="30" QFormat="true" Name="Intense Quote"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 1"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/> <w:LsdException Locked="false" Priority="19" QFormat="true" Name="Subtle Emphasis"/> <w:LsdException Locked="false" Priority="21" QFormat="true" Name="Intense Emphasis"/> <w:LsdException Locked="false" Priority="31" QFormat="true" Name="Subtle Reference"/> <w:LsdException Locked="false" Priority="32" QFormat="true" Name="Intense Reference"/> <w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/> <w:LsdException Locked="false" Priority="37" SemiHidden="true" UnhideWhenUsed="true" Name="Bibliography"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> <w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/> <w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/> <w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/> <w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/> <w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/> <w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 1"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 2"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 3"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 4"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 5"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light Accent 6"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 1"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 1"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 1"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 1"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 1"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 1"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 2"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 2"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 2"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 2"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 2"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 2"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 3"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 3"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 3"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 3"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 3"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 3"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 4"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 4"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 4"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 4"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 4"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 4"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 5"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 5"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 5"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 5"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 5"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 5"/> <w:LsdException Locked="false" Priority="46" Name="List Table 1 Light Accent 6"/> <w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 6"/> <w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 6"/> <w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 6"/> <w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 6"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 6"/> </w:LatentStyles> </xml><![endif]--><!-- [if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0mm 5.4pt 0mm 5.4pt; mso-para-margin:0mm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif;} </style> <![endif]--><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">At present, with the significant growth of energy consumption, increase of greenhouse gases and environmental pollutants, more attention is directed toward renewable energies. Renewable energies include geothermal, wind, photovoltaic energy and etc. Among the advantages of photovoltaic energy, its wide range and easy access, helping to preserve the environment, compatibility with distributed power networks, low noise, quick installation and lower cost compared to other energies can be noted. Important challenges facing photovoltaic systems are changing climatic conditions and parameters variation that affect the performance of the system. In this paper, to track the maximum power point in a photovoltaic system, a fuzzy fractional order sliding mode controller based on disturbance observer and uncertainty estimator using neural network is designed. The sliding mode control is used to reduce chattering, neural network to estimate the system uncertainties, fuzzy system to estimate the coefficient of the signum function in the control law and disturbance observer to approximate the disturbances in the system. Also, the stability of the system has been proven using the Lyapunov method. The simulation results of the photovoltaic system confirm the effectiveness of the proposed method and shows satisfactory performance.</span></p> Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  371 - Sociological explanation of the impact of modernization and social networks on ethnic tendencies Case study: Ilam province
  Abdolhossein  Rahmati نبی اله  ایدر Abdolreza  Hashemi
  This research was conducted with the aim of investigating modernization and social networks, two important components of globalization and its effect on the ethnic tendencies of the people of Ilam province, to show how the identities of the people of Ilam province rel More

  This research was conducted with the aim of investigating modernization and social networks, two important components of globalization and its effect on the ethnic tendencies of the people of Ilam province, to show how the identities of the people of Ilam province relate to global identities. In this research, the statistical population is all people living in the province. Ilam with a population of 580,158 people, based on Cochran's formula, the statistical sample size is 270 people, and the sampling method is simple probability, the survey method and the data collection tool of Hop Rashnameh. Cronbach's alpha coefficient was used to measure the reliability of the data and face validity method was used to measure the validity. The results of the research were analyzed using Pearson's correlation coefficient, variance analysis and regression analysis. They are an important component of globalization, there is a significant relationship with ethnic tendencies. This means that traditional communities like Ilam province have become dependent on the modern world, which has caused a decrease in the sense of belonging and ethnic attachment of the people of Ilam province. Based on the findings, it can be concluded that the ethnic trends in Ilam province, unlike the past, in the contemporary era, have gone out of a solid and uniform state, and modernization and social networks have had an impact on it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  372 - Presenting a risky investment model in the insurance industry based on the fuzzy network analysis process
  Amir Mohammadzade Yazdan Gudarzi Farahani مریم  پورفرزام Zulikha  Morsali Aruznaq Mohsen Mehrara
  The purpose of this paper is to identify and present a high-risk investment model for insurance institutions in order to establish a risky insurance investment fund. In this paper, it has been tried to identify and introduce the most important factors and effective fact More

  The purpose of this paper is to identify and present a high-risk investment model for insurance institutions in order to establish a risky insurance investment fund. In this paper, it has been tried to identify and introduce the most important factors and effective factors of venture capital (VC) using the technique of fuzzy network analysis process in the insurance industry. The data needed for the research has been collected through in-depth interviews with a number of experts and researchers in the insurance industry, as well as a questionnaire. Due to the lack of independence and the existence of dependence between the effective factors, the method of fuzzy network analysis process was used to identify the possible dependencies between factors and measure them for the development of the VC model, and the results were prioritized with the method of non-fuzzy network analysis process. Is. The findings of the research show that operating cycle indicators, total asset turnover ratio, total investment return, loss factor, asset-liability ratio are ranked first to fifth among VC indicators. VCs help the insurance industry establish internal accounting rules and standardize their financial statements. In other words, VC support modifies the "hard" and "soft" information required for insurance. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  373 - The Relationship Between Adolescent Disobedience and Dependence on Social Networks virtual in the Family Environment (Case study: Secondary School Students of District 17 of Tehran)
  Tahmineh Shaverdi zeinab Maghferati Shamsabad
  The aim of this research is to analyze the disobedience of adolescents in the family environment based on addiction to virtual social networks among secondery school students of the 17th district of Tehran. The study method is survey and the statistical population is th More

  The aim of this research is to analyze the disobedience of adolescents in the family environment based on addiction to virtual social networks among secondery school students of the 17th district of Tehran. The study method is survey and the statistical population is the lower secondery school of the 17th district of Tehran. The sample size is 347 individuals selected using simple random sampling method based on Cochran's formula. The data collection tools consist of the Addiction to Social Networks questionnaire (Yang 1998) and the Adolescents' Disobedience questionnaire (Motamedi and colleagues 2021).The results of the Pearson correlation test showed that the relationship between daily activity disorder, family-social functioning, mental health, and self-control with adolescent disobedience is equal to 21%, 31%, 25%, and 25% respectively. Furthermore, regression model analyses showed that the impact of addiction to social networks on adolescent disobedience is 33%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  374 - A Framework for Sentiment Analysis in Social Networks based on Interpreting Contents
  Maryam Tayfeh-Mahmoudi َAmirmansour  Yadegari Parvin Ahmadi kambiz badie
  Interpreting contents with the aim of analyzing the sentiment of their narrators in social networks, holds a high significance due to the role of a content in disseminating information to the corresponding human groups. In this paper, we propose a framework for analyzin More

  Interpreting contents with the aim of analyzing the sentiment of their narrators in social networks, holds a high significance due to the role of a content in disseminating information to the corresponding human groups. In this paper, we propose a framework for analyzing sentiment on complex contents in a social network according to which a set of if-then type rules defined at high abstraction level, would be able to classify the messages behind these contents. According to this framework, items such as prosodic, context and key propositions are considered in the condition part of a rule and possible classes of message are taken into account in a rule’s action part. It is to be noted that the rules proposed for interpreting a content do not depend on the considered language due to the very inherent property of the items which are considered in interpretation. Results of experiments on a wide range of different contents in a social network support the fact that the proposed framework is sufficiently capable of analyzing the sentiments of contents’ narrators. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  375 - Multi-Level Ternary Quantization for Improving Sparsity and Computation in Embedded Deep Neural Networks
  Hosna Manavi Mofrad ali ansarmohammadi Mostafa Salehi
  Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and co More

  Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and computational resources. Network compression techniques can overcome these challenges. Quantization and pruning methods are the most important compression techniques among them. One of the famous quantization methods in DNNs is the multi-level binary quantization, which not only exploits simple bit-wise logical operations, but also reduces the accuracy gap between binary neural networks and full precision DNNs. Since, multi-level binary can’t represent the zero value, this quantization does not take advantage of sparsity. On the other hand, it has been shown that DNNs are sparse, and by pruning the parameters of the DNNs, the amount of data storage in memory is reduced while computation speedup is also achieved. In this paper, we propose a pruning and quantization-aware training method for multi-level ternary quantization that takes advantage of both multi-level quantization and data sparsity. In addition to increasing the accuracy of the network compared to the binary multi-level networks, it gives the network the ability to be sparse. To save memory size and computation complexity, we increase the sparsity in the quantized network by pruning until the accuracy loss is negligible. The results show that the potential speedup of computation for our model at the bit and word-level sparsity can be increased by 15x and 45x compared to the basic multi-level binary networks. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  376 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh movaghar movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  377 - Presenting the ICT Policies Implementation Model of the 6th Development Using the Neural Network Method
  Nazila Mohammadi Gholamreza  Memarzadeh sedigheh tootian
  It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the eff More

  It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the effective factors on the implementation of Iran's ICT policies by the neural network technique and based on Giddens' constructive theory. From the point of view of conducting it, this research is of a survey type and based on the purpose, it is of an applied type because it is trying to use the results of the research in the Ministry of Communication and Information Technology and the Iranian Telecommunications Company. Data collection is based on library and field method. The tool for collecting information is researcher-made questionnaire. The statistical population of the research is ICT experts at the headquarters of Iran Telecommunication Company (810 people), of which 260 people were randomly selected as a sample based on Cochran's formula. MATLAB software was used for data analysis. According to the findings, the best combination for development is when all input variables are considered at the same time, and the worst case is when the infrastructure development variable is ignored, and the most important based on network sensitivity analysis is related to infrastructure development and the least important is related to content supply. Manuscript profile