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Open Access Article
1 - Determination of Optimum SVMs Based on Genetic Algorithm in Classification of Hyper spectral Imagery
farhad samadzadegan hadise hassaniHyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional MoreHyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional space, they are efficient tool for classification of hyper spectral imagery. However, there are two optimization issues which strongly effect on the SVMs performance: Optimum SVMs parameters determination and optimum feature subset selection. Traditional optimization algorithms are appropriate in limited search space but they usually trap in local optimum in high dimensional space, therefore it is inevitable to apply meta-heuristic optimization algorithms such as Genetic Algorithm to obtain global optimum solution. This paper evaluates the potential of different proposed optimization scenarios in determining of SVMs parameters and feature subset selection based on Genetic Algorithm (GA). Obtained results on AVIRIS Hyper spectral imagery demonstrate superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset. In Gaussian and Polynomial kernels, the classification accuracy improves by about 5% and15% respectively and more than 90 redundant bands are eliminated. For comparison, the evaluation is also performed by applying it to Simulated Annealing (SA) that shows a better performance of Genetic Algorithm especially in complex search space where parameter determination and feature selection are solve simultaneously. Manuscript profile -
Open Access Article
2 - Analyzing the impact of macroeconomic variables on customer churn banking industry With data mining approach
Mehrnaz Motahari niaToday, customer knowledge and understanding of its needs have become a business imperative. Organizations need customer satisfaction to sustain their business and succeed in a competitive market. Knowing customers through customer behavior analysis is possible with the MoreToday, customer knowledge and understanding of its needs have become a business imperative. Organizations need customer satisfaction to sustain their business and succeed in a competitive market. Knowing customers through customer behavior analysis is possible with the use of new technologies such as data mining techniques for organizations. The purpose of this research is to investigate the effective factors on Customers churn in the banking industry. For this purpose, the transaction data of sales terminals of a payment service provider company (PSP) in Iran has been analyzed. In the proposed model using the WRFM method and combining it with the K-Means clustering algorithm, sales terminals are split and loyalty each month. Then, using the additive selection method plus L take R and the multivariate linear regression algorithm, the effective features The percentage of customers discarded is selected from the monthly economic indicators per month. Based on the results of the implementation of the three variables, the index of stock market value, inflation and the price of all coins are the most effective variables among the economic indicators under study. Manuscript profile -
Open Access Article
3 - Determinetion of site selection pattern of municipal waste incineration power plant
sadaf feizi mehdi AalipoorNowadays, environmental hazards that originated from inappropriate management of waste are one of the main problems of the country. Therefore, the development of waste management system and new and advanced technologies entry is necessary. Waste disposal by incinerator MoreNowadays, environmental hazards that originated from inappropriate management of waste are one of the main problems of the country. Therefore, the development of waste management system and new and advanced technologies entry is necessary. Waste disposal by incinerator is an effective method. Unlike landfill, the use of incineration does not require long-term care and there is the possibility of energy extraction. One of the main disadvantages of this method is emission of air pollutants from the stuck of incinerators and management of the residual wastes such as ash. Therefore, selecting the best place for construction of the municipal waste incineration power plant will increase its benefits, reduce costs and eliminate community dissatisfaction by identifying and considering the involved and effective factors such as environmental, economic and social factors. In this paper, literature reviewes, searching for valid universal guidelines and the related articles in databases were used to introduce and compare the criteria mentioned for locating the municipal waste incineration power plant as one of the most important steps in designing a comprehensive waste management system. Manuscript profile -
Open Access Article
4 - An Improved Method for Detecting Phishing Websites Using Data Mining on Web Pages
mahdiye baharloo Alireza YariPhishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is MorePhishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. Then, a two-stage feature reduction method based on feature selection and extraction were proposed to enhance the efficiency of phishing detection systems, which was able to reduce the number of features significantly. Finally, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated{cke_protected_1}{cke_protected_2}{cke_protected_3}{cke_protected_4} on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods. Manuscript profile -
Open Access Article
5 - The Poetic Word Selection of Gheisar Aminpour by Critical Discourse Analysis Approach
پریسا صالحیEach artist may picture his/her political, social and cultural attitude in a different way, so Gheisar Aminpour was not an exception. He was one of the early revolutionary poets, who had involved in a number of ideological and religious values and had a stable belief on MoreEach artist may picture his/her political, social and cultural attitude in a different way, so Gheisar Aminpour was not an exception. He was one of the early revolutionary poets, who had involved in a number of ideological and religious values and had a stable belief on revolution’s principles and public ideality, to the extent that his poetry world was conceivably produced through a certain profound ideology. The interaction between the poet, Aminpour, and the political and social transformation in his society could make any individual study his poetry world by discourse analysis approach. Since any language could be supposed as a mirror of the thought (words as the mirror of the meaning), and the words’ role as well as their selection may certainly be significant in the process of analysis, this research is specified to the study of word selection of Aminpour’s poems by critical discourse analysis. Furthermore, it has focused not only on the study of Aminpour’s poetry world in terms of historical, political and social renovation of his era but also on his words’ selection, studied here, on three descriptive, interpretive and explanatory levels. In conclusion, the hidden meaning of the words are supposed to be revealed by critical discourse analysis tools to illustrate, more or less, the ideological attitude of the poet. Manuscript profile -
Open Access Article
6 - Investigating the Role of Conceptual Metaphors in the Evolution of literary styles (Relying on Body Metaphors in Farrokhi Sistani, Anvari, and Hafiz's poems)
Mohammad Taheri Masoumeh ArchandaniThe conceptual metaphor is the mind’s technique for conceptualizing affairs. The mind tries to conceive and contemplate some intangible affairs for itself with the help of this fundamental construct. Hence, it is expected that by changing it, we will also see a change i MoreThe conceptual metaphor is the mind’s technique for conceptualizing affairs. The mind tries to conceive and contemplate some intangible affairs for itself with the help of this fundamental construct. Hence, it is expected that by changing it, we will also see a change in thought-related macro-systems. Among these macrosystems are literary styles that have many factors involved in changing them. This article tries to answer the question of how the change of conceptual metaphors in time has an effect on the change of literary styles through analytical-descriptive method. To answer it, we trace the conceptual metaphors related to the body, which include the words "hand, eye, heart and head", in a selection of poems by Farrokhi Sistani, Anvari and Hafez. By analyzing the obtained information, we conclude that conceptual metaphors have undergone significant changes over time that are in line with the evolution of literary styles and are variables affecting the evolution of styles. Manuscript profile -
Open Access Article
7 - Economic Theory, Constitutional Law, and Justice
abolfazl pasbaniUsing an interdisciplinary approach and analytical method, the present article attempts to find the possibility of applying justice to the economic theory in the Constitutional Law. The main finding of the study is that although the Constitutional Law has acceptable pot MoreUsing an interdisciplinary approach and analytical method, the present article attempts to find the possibility of applying justice to the economic theory in the Constitutional Law. The main finding of the study is that although the Constitutional Law has acceptable potentials and offers good solutions, its capabilities have been exaggerated and, according to Amartya Sen, it manifests a kind of transcendental institutionalism. However, attention to unofficial institutions can be more fruitful. Manuscript profile -
Open Access Article
8 - Cover Selection Steganography Via Run Length Matrix and Human Visual System
Sara Nazari Mohammad Shahram MoinA novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs run length matrix to select a set of appropriate images from an image database and creates their stego version after e MoreA novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs run length matrix to select a set of appropriate images from an image database and creates their stego version after embedding process. Then, it computes similarity between original images and their stego versions by using structural similarity as image quality metric to select, as the best cover, one image with maximum similarity with its stego. According to the results of comparing our new proposed cover selection algorithm with other steganography methods, it is confirmed that the proposed algorithm is able to increase the stego quality. We also evaluated the robustness of our algorithm over steganalysis methods such as Wavelet based and Block based steganalyses; the experimental results show that the proposed approach decreases the risk of message hiding detection. Manuscript profile -
Open Access Article
9 - Load Balanced Spanning Tree in Metro Ethernet Networks
Ghasem Mirjalily Samira SamadiSpanning 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 MoreSpanning 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
10 - Selecting Enterprise Resource Planning System Using Fuzzy Analytic Hierarchy Process Approach
hojatallah hamidiTo select an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP pro MoreTo select an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP project. In this paper, we propose a fuzzy analytic hierarchy process (FAHP) method to evaluate the alternatives of ERP system. The selection criteria of ERP system are numerous and fuzzy, so how to select an adequate ERP system is crucial in the early phase of an ERP project. The framework decomposes ERP system selection into four main factors. The goal of this paper is to select the best alternative that meets the requirements with respect to product factors, system factors, management factors and vendor factors. The sub-attributes (sub-factors) related to ERP selection have been classified into thirteen main categories of Functionality, Reliability¬, Usability¬, Efficiency¬, Maintainability¬, Portability¬, Cost, Implementation time, User friendliness¬, Flexibility¬, Vendor Reputation¬, Consultancy Services, and R&D Capability¬ and arranged in a hierarchy structure. These criteria and factors are weighted and prioritized and finally a framework is provided for ERP selection with the fuzzy AHP method. Also, a real case study from Iran (PARDIS-LO Company) is also presented to demonstrate efficiency of this method in practice. Manuscript profile -
Open Access Article
11 - Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm
Azar Mahmoodzadeh Hamed Agahi Marzieh SalehiThis paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminat MoreThis paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features. Manuscript profile -
Open Access Article
12 - Graph Based Feature Selection Using Symmetrical Uncertainty in Microarray Dataset
Soodeh Bakhshandeh azmi azmi Mohammad TeshnehlabMicroarray data with small samples and thousands of genes makes a difficult challenge for researches. Using gene selection in microarray data helps to select the most relevant genes from original dataset with the purpose of reducing the dimensionality of the microarray MoreMicroarray data with small samples and thousands of genes makes a difficult challenge for researches. Using gene selection in microarray data helps to select the most relevant genes from original dataset with the purpose of reducing the dimensionality of the microarray data as well as increasing the prediction performance. In this paper, a new gene selection method is proposed based on community detection technique and ranking the best genes. Symmetric Uncertainty is used for selection of the best genes by calculation of similarity between two genes and between each gene and class label which leads to representation of search space as a graph, in the first step. Afterwards, the proposed graph is divided into several clusters using community detection algorithm and finally, after ranking the genes, the genes with maximum ranks are selected as the best genes. This approach is a supervised/unsupervised filter-based gene selection method that minimizes the redundancy between genes and maximizes the relevance of genes and class label. Performance of the proposed method is compared with thirteen well-known unsupervised/supervised gene selection approaches over six microarray datasets using four classifiers including SVM, DT, NB and k-NN. Results show the advantages of the proposed approach. Manuscript profile -
Open Access Article
13 - Energy Efficient Clustering Algorithm for Wireless Sensor Networks
Maryam Bavaghar Amin Mohajer Sarah Taghavi MotlaghIn 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 MoreIn 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
14 - A Fast Machine Learning for 5G Beam Selection for Unmanned Aerial Vehicle Applications
Wasswa Shafik Mohammad Ghasemzadeh S.Mojtaba MatinkhahUnmanned Aerial vehicles (UAVs) emerged into a promising research trend applied in several disciplines based on the benefits, including efficient communication, on-time search, and rescue operations, appreciate customer deliveries among more. The current technologies ar MoreUnmanned Aerial vehicles (UAVs) emerged into a promising research trend applied in several disciplines based on the benefits, including efficient communication, on-time search, and rescue operations, appreciate customer deliveries among more. The current technologies are using fixed base stations (BS) to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. These open gates for the UAVs technology to be used as a mobile alternative to increase accessibility in beam selection with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. This paper presents a first fast semi-online 3-Dimensional machine learning algorithm suitable for proper beam selection as is emitted from UAVs. Secondly, it presents a detailed step by step approach that is involved in the multi-armed bandit approach in solving UAV solving selection exploration to exploitation dilemmas. The obtained results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any mobile networked devices issue based on bandit samples like Thompson sampling, Bayesian algorithm, and ε-Greedy Algorithm. The results further illustrated that the 3-Dimensional algorithm optimizes utilization of technological resources compared to the existing single and the 2-Dimensional algorithms thus close optimal performance on the average period through machine learning of realistic UAV communication situations. Manuscript profile -
Open Access Article
15 - An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram
zahra khalifeh zadeh Mohammad Ali Zare ChahookiIn recent years, data received from social media has increased exponentially. They have become valuable sources of information for many analysts and businesses to expand their business. Automatic document classification is an essential step in extracting knowledge from MoreIn recent years, data received from social media has increased exponentially. They have become valuable sources of information for many analysts and businesses to expand their business. Automatic document classification is an essential step in extracting knowledge from these sources of information. In automatic text classification, words are assessed as a set of features. Selecting useful features from each text reduces the size of the feature vector and improves classification performance. Many algorithms have been applied for the automatic classification of text. Although all the methods proposed for other languages are applicable and comparable, studies on classification and feature selection in the Persian text have not been sufficiently carried out. The present research is conducted in Persian, and the introduction of a Persian dataset is a part of its innovation. In the present article, an innovative approach is presented to improve the performance of Persian text classification. The authors extracted 85,000 Persian messages from the Idekav-system, which is a Telegram search engine. The new idea presented in this paper to process and classify this textual data is on the basis of the feature vector expansion by adding some selective features using the most extensively used feature selection methods based on Local and Global filters. The new feature vector is then filtered by applying the secondary feature selection. The secondary feature selection phase selects more appropriate features among those added from the first step to enhance the effect of applying wrapper methods on classification performance. In the third step, the combined filter-based methods and the combination of the results of different learning algorithms have been used to achieve higher accuracy. At the end of the three selection stages, a method was proposed that increased accuracy up to 0.945 and reduced training time and calculations in the Persian dataset. Manuscript profile -
Open Access Article
16 - Pathology of Selection and Appointment Primary School Principals in District 5 of Isfahan
Elham Ghaderi Zefreh asghar zamani Mahtab PouratashiThe purpose of this study was to investigate the pathology of the system of selection and appointment of primary school principals in the academic year of 2019-2020 in District 5 of Isfahan in an applied method with a combined approach. In the qualitative section, semi- MoreThe purpose of this study was to investigate the pathology of the system of selection and appointment of primary school principals in the academic year of 2019-2020 in District 5 of Isfahan in an applied method with a combined approach. In the qualitative section, semi-structured interviews with snow experts were performed by sampling snowballs with 10 experts in the field of education in Isfahan, and their analysis was performed through content analysis. In a small part of 215 primary schools in Isfahan District 5, 138 schools were selected based on Krejcie and Morgan table as a sample size and data were collected through a researcher-made questionnaire and the results were analyzed at both descriptive and inferential levels. Statistics were analyzed using SPSS software. The results showed that the current situation of the system of selection and appointment of primary school principals in Isfahan District 5 is unfavorable and the main challenges include the lack of specialized fields for training principals in Farhangian University, lack of capable manpower, lack of database. , Lack of knowledge and mastery of managers with specialized courses and required for the elementary course, which can be solved with tools such as placing an internship for new managers, using experienced managers as support, creating a training center for future managers Manuscript profile -
Open Access Article
17 - Identification and explanation effective factors in Fire Stations site selection in run down texture
Ahmad Heydari Hamidreza JoudakiFrom among available uses and services, significant importance is distribution efficient site selection of Fire station, in the case of important and attention to the safety and arrangement in coping on fire and accident with increase Population and density in city ,ser MoreFrom among available uses and services, significant importance is distribution efficient site selection of Fire station, in the case of important and attention to the safety and arrangement in coping on fire and accident with increase Population and density in city ,services, fire organization is duty supply in the coping with fire. In run down and old texture because of supply suitable access and minimum standard time to fire place and in general supply urban safety establish fire station is more important. The basic goal of this research is identification main factors in site selection for establish fire stations in run down texture. In this research ,at first survey and identification main factors in site selection in run down texture with use of AHP method ,this is a multi-criteria decision making and then weighting and prioritization fire station site selection criterion and sub criterion. The results show that between factor such as access, population density, nearness and natural disasters, access factor appropriating access most weight in fire stations site selection in Tehran run down texture. Manuscript profile -
Open Access Article
18 - Feature selection for author identification of Persian online short texts
somayeh arefi mohamad ehsan basiri omid roozmandThe growing use of social media and online communication to express opinions, exchange ideas, and also the expanding use of of this platforms by Persian users has increased Persian texts on the Web. This remarkable growth, along with abusive use of the writer's anonymit MoreThe growing use of social media and online communication to express opinions, exchange ideas, and also the expanding use of of this platforms by Persian users has increased Persian texts on the Web. This remarkable growth, along with abusive use of the writer's anonymity, reveals the need for the author's automatic identification system in this language. In this research, the purpose of the study is to investigate the factors affecting the identification of authors of Persian reviews produced by cell-phone buyers and also to evaluate supervised and unsupervised methods. The factors considered in this research include lexical, syntactic, semantic, structural, grammatical, text-specific, and specific to social networks. After extracting these features, selecting the best features is tested by four algorithms including feature correlation, gain ratio, OneR, and principal components analysis. In the following, K-means, EM and density-based clustering will be used for clustering and Bayesian network, random forest, and Bagging will be used for categorization. The evaluation of the above algorithms on Persian comments of Samsung phone buyers indicates that the best performance among the clustering algorithms is 59/16% obtained by the EM algorithm on top-15 features selected by OneR, while the random forest algorithm using top-90 features selected by gain ratio with 79/57% achieves the best performance among the classification algorithms. Also, the comparison of features showed that syntactic features had the most effect on the identification of the author of short texts, and then, lexical, text-specific, specific to social networks, structural, grammatical and semantic features, respectively. Manuscript profile -
Open Access Article
19 - Islamic indicators for selecting and recruiting human resources
Hossein Taj Abadi Kaveh NamvarIn Islamic teachings, the process for selecting the right people is not directly expressed but there exist some overviews. In Islam, it is mentioned that the decision on hiring the employees should be fair and rational, and since the believers are more honest, employees MoreIn Islamic teachings, the process for selecting the right people is not directly expressed but there exist some overviews. In Islam, it is mentioned that the decision on hiring the employees should be fair and rational, and since the believers are more honest, employees should be obliged to Islamic beliefs such as praying, fasting, zakat and so on. The human resource manager should consider such criteria while hiring the needed employees. The current research is accomplished to develop Islamic indicators of recruiting human resources and to clarify the relations among its components by conducting an explorative mixed method. The first step was a qualitative study (Delphi analysis) in order to propose the research model, and the second one was the quantitative study. To this end, a sample size of 225 employees of a public organization was randomly selected for data gathering. Obtained data resulted in one main aspect, 10 components and 24 indicators based on which a questionnaire was developed and distributed. Findings of the second step validated the proposed model since the construct of "Islamic HR Recruitment" was statistically associated with its components and indicators. Manuscript profile -
Open Access Article
20 - Site Selection of Science and Technology Clusters by AHP Method and Using GIS (Case Study: Yazd Science and Technology Cluster)
Hosein Rahimi Masoud NiksiratThe establishment of each urban element in the specific location of the city follows special principles that if these principles to be considered well, will lead to the success and functional efficiency of that element in that specific location. In this context, the sel MoreThe establishment of each urban element in the specific location of the city follows special principles that if these principles to be considered well, will lead to the success and functional efficiency of that element in that specific location. In this context, the selection of a suitable location for the establishment of science and technology cluster as one of the urban distinctive elements has significant importance since those contain scientific, industrial, manufacturing, laboratory, research facilities, infrastructure and communication environments, institutions, individuals, and information based on creativity and innovation. Therefore, the present study was conducted aimed to find the most suitable location for the establishment of Yazd science and technology cluster. For this purpose after the study of global experiences and regulations and upstream documentation the requirements and necessities Clusters Site Selection were identified. Then, the effective parameters in two levels of main indicators and sub-indicators according to the Analytical Hierarchy Process Model were classified. Then, receiving information according to the experts, managers and professionals ideas analyzed by the Super Decision software and weight of the indicators was determined. In the next step, the satellite image of study area was prepared and using GIS, attempted for preparation of data layers based on requirements that come from the results of questionnaire and interviews. To achieve the final map that is obtained by overlaying weighted maps, first the importance of data layers in terms of distance has been determined and then using layers overlap method, Suitable location for the establishment of Yazd Science and Technology cluster has been proposed. Manuscript profile -
Open Access Article
21 - Risk and Return Evaluation of Information Technology Projects by Real Option and Mean-Variance Theory Perspective
Hosein Rezaei Dolat abadi Samaneh Amir BoshliThe selection of appropriate technology projects has been one of the most significant business challenges of the last decade. Nevertheless, Information technology projects represent the largest capital expenditure items for most firms, but many projects have been unsucc MoreThe selection of appropriate technology projects has been one of the most significant business challenges of the last decade. Nevertheless, Information technology projects represent the largest capital expenditure items for most firms, but many projects have been unsuccessful. Because of the importance of such investments, there is a need for a scientific framework to analyze them. This paper analyses and uses Real Options and Mean-Variance theory for risk and return analysis, and ranking the information technology investment projects. These two models offer a simple, but comprehensive method for managers to evaluate potential information technology projects. Manuscript profile -
Open Access Article
22 - Identifying Priorities for Acquisition of Technology in an Industrial Organization Case Study of Middle-Class Vessels
Hosein Esbati Seyed Habibollah Tabatabaeiyan3000 km of coastline, access to the sea on the north and south of the country, access to the open water (Indian Ocean), are the advantages of the geographical area of marine and maritime industries that made a special place in the economy of the country. Transit maritim More3000 km of coastline, access to the sea on the north and south of the country, access to the open water (Indian Ocean), are the advantages of the geographical area of marine and maritime industries that made a special place in the economy of the country. Transit maritime economy and maximum utilization of sea resources has confirmed the need to replacement of worn out vessels with Landing Crafts. For that matter, the most important goals of this R&D are sea freight, transition and achieving the capacity of producing landing craft vessel. Investors and suppliers to meet the needs of customers in a competitive environment must decide: 1) domestic production, 2) transfer of technology, and 3) a combination of both. This paper has initially studied definitions of technology and technology transfer and different aspects of technology acquisition process, the identification, selection, transfer, exploitation and dissemination. The decision models presented by little and Ford have been studied, based on the data, a model for the study was provided. In the first phase, the project was developed Executive structure of acquisition and division of Duties. The second step is the most important activities, assessment capabilities and industry suppliers that are identified and assessed. Data were analyzed by Ford Models. These results, created clear images for useful decision-making executives, Acquisition of the landing Craft boats were through a joint partnership action. Finally, the advantages and disadvantages of access, internal development and transfer, has been studied. Manuscript profile -
Open Access Article
23 - Designing Model For Prioritize And Selection Projects Portfolio in a Joint Stock Company Based on Analytic Network Process (ANP)
کبری يزدانی Hossein Ali Hassan pourEstablished 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 MoreEstablished 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
24 - Selecting Enterprise Resource Planning System Using of Fuzzy Analytic Hierarchy Process Approach
Hojatollah HamidiSelecting an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP pro MoreSelecting an enterprise resource planning (ERP) system is time consuming due to the resource constraints, the software complexity, and the different of alternatives. A comprehensively systematic selection policy for ERP system is very important to the success of ERP project. In this paper, we propose a fuzzy analytic hierarchy process (FAHP) method to evaluate the alternatives of ERP system. The selection criteria of ERP system are numerous and fuzzy, so how to select an adequate ERP system is crucial in the early phase of an ERP project. The framework decomposes ERP system selection into four main factors. The goal of this paper is to select the best alternative that meets the requirements with respect to “product factors”, “system factors”, “management factors” and “vendor factors”. The sub-attributes (sub-factors) related to ERP selection have been classified into thirteen main categories. These criteria and factors are weighed and prioritized and finally a framework is provided for ERP selection with the fuzzy AHP method. Also, a real case study from PARDIS-LO Company is presented. Manuscript profile -
Open Access Article
25 - The Effect of Environmental Pressure on the Firm Performance With an Emphasis on Green Technology Selection (Case Study: Small and Medium-Sized Enterprises of Chemical Paint Production)
hamid azizmohamadlou sepide mohamadnejad madardiFirm performance is one of the most important criteria for evaluating their success . Given the fact that in recent decades concerns about the environment have increased , small and medium - sized enterprises should implement methods to improve their performance in this MoreFirm performance is one of the most important criteria for evaluating their success . Given the fact that in recent decades concerns about the environment have increased , small and medium - sized enterprises should implement methods to improve their performance in this area. One of the methods is selection and deployment of green technologies . his concept has grown in recent years and many organizations have used it as an environmental strategy. Considering the fact that the most important issue for any organization is to improve revenue and profitability , green technology can take an important step in this direction by reducing energy consumption, waste reduction and increase in efficiency. The purpose of this work is to investigate the effect of environmental pressure on the firm performance with an emphasis on selection of green technology. Data are collected through the questionnaire and 75 samples from managers of small and medium-sized enterprises of chemical paint production . For testing the conceptual model , structural equation modeling based on partial least squares method is used . The results of this research show that pressure of macroeconomic circumstances has the most influence on the selection of the green technology , hence improving the environmental performance leads to growth in firm performance. It is anticipated with increasing the pressure of macroeconomic circumstance and government support , industries have tendency to selection of green technology. Manuscript profile -
Open Access Article
26 - Site Selection Of Knowledge-Based Town within Tehran
Mehdi Gharakhloo Maryam MousaviTehran Province, containing majority of scientists and researchers, plays a significant role in the development of science and technology in the country. Obviously, this number of scientific potential to flourish needs facilities and infrastructures. One of the most imp MoreTehran Province, containing majority of scientists and researchers, plays a significant role in the development of science and technology in the country. Obviously, this number of scientific potential to flourish needs facilities and infrastructures. One of the most important factors in development of knowledge-based businesses is proportionate physical context of firms. Moreover, proximity of knowledge-based companies to the universities, higher education institutes and industries leads to accelerating transforming knowledge into industrial process. Thus, Tehran Province requires more technology parks and towns than the past, to retain the benefit of its whole scientific potentials. In connection with this, at first, we recognized and ranked effective factors on locating Knowledge-Based Town, then used AHP to give weight to criteria and sub-criteria. In the next step, we applied these weights in GIS software and provided maps which are related to each, in the ARCmap. Then, we identified zoned map of the areas via GIS and overlapped the effective factors on locating town of knowledge, with two priorities in the province, and the result indicated that the first priority was better. After preparing the final map, through the study on areas with priority, we have screened and shown places that the standards are higher for locating KBT on the map. At last, for the best performance, we have paid special attention to factors such as proximity to universities and training centers, proximity to industrial areas, infrastructures and Tehran province’s development plans. Manuscript profile -
Open Access Article
27 - Ranking of Supplier Selection Indicators for Outsourcing Services under Uncertainty
Mostafa Ebrahim pour Azbary atefeh abdollahi Mohsen Akbari keramat gholamiNowadays, organizations look at supplier selection with different attitudes and use various methods to evaluate and select suppliers. Most of private, public, profit, and charity businesses as well as educational and research institutes, using the available advantages, MoreNowadays, organizations look at supplier selection with different attitudes and use various methods to evaluate and select suppliers. Most of private, public, profit, and charity businesses as well as educational and research institutes, using the available advantages, have considered outsourcing strategy among their macro-goals at all organizational dimensions. Meanwhile, suitable supplier selection is one of the strategic keys of supply chain for the success of organizations. In terms of purpose and method, the present study is quantitative and descriptive-analytical, respectively. This study aimed to identify the most important criteria and sub-criteria in selecting suppliers and investigate ranking the criteria and sub-criteria of interest to specify which sub-criterion provides the possibility for more improvement in the defined main criteria. For this purpose, supplier selection indicators were obtained investigating literature and interview with experts. Then, data were tested using a combination of multi-criteria decision-making techniquesThe results of this study showed that the main criteria on which a government agency concentrates while selecting suitable supplier for outsourcing services include service quality, flexibility, and delivery followed by the four remaining criteria according to various values that V dedicates to itself. Manuscript profile -
Open Access Article
28 - A Framework for Evaluating and Selecting a Strategic Supplier (Case Study: Yazd Alloy Steel Corporation)
Mohammad Ali Sangbor Fatemeh Javidi Karim Golmohammadi Rasoul AbbasiToday, due to increasing competition and dynamism of economy and business activities, adopting a strategic approach to supply chain management and supplier selection is undeniable necessity. Suppliers are one of the most vital parts of supply chain and supplier selectio MoreToday, due to increasing competition and dynamism of economy and business activities, adopting a strategic approach to supply chain management and supplier selection is undeniable necessity. Suppliers are one of the most vital parts of supply chain and supplier selection issues are complex issues that may have many qualitative and quantitative concerns. The presented frameworks in the previous research are unable to response dynamism strategic and provide an appropriate framework, in this study, strategic cooperation approaches and supplier selection criteria have combined. For this purpose, components of cooperation strategies and supplier selection criteria have been counted and based on quality function deployment approach, the relationship between them has been specified. Finally, using fuzzy TODIM method, strategic weight of criteria has been obtained. in this study, because of expert oriented data, the non-sampling method is used for gathering data. The statistical population consist of supply chain experts and managers of Yazd Alloy Steel corporation. For analyzing data, a the oretical sampling was used and a non-statistical sample including ten experts was selected. As a result, a strategic supplier selection framework and strategic weight of every component, were determined. The results of data analysis show that among the 20 criteria, five criteria including "product quality", "strategic planning", "History of suppliers", "risk prediction" and "R & D Management" are in the first to fifth priorities in terms of importance respectively. Manuscript profile -
Open Access Article
29 - Supplier selection with the approach of combining Fuzzy Delphi Analytic Hierarchy Process (FDAHP) and Grey VIKOR methods
Mohammad Mahdi MozaffariThe supplier selection is one of the most important issues in supply chain management discipline and it is regarded as an effective factor for companies to survive in the competitive environment. Selecting the best supplier among various suppliers, considering a mixture MoreThe supplier selection is one of the most important issues in supply chain management discipline and it is regarded as an effective factor for companies to survive in the competitive environment. Selecting the best supplier among various suppliers, considering a mixture of criteria that are sometimes in conflict, makes it a complicated multi-criteria decision making problem. On the other hand, supplier selection is highly dependent on the assessments of decision makers and according to the environmental dynamics and incomplete information available to decision makers, they are faced with some uncertainty in their assessments. The purpose of this paper is to present a model of multi-criteria decision making under uncertainty by using concepts of fuzzy and grey theories in order to select the optimal supplier. In this research, first, the weights of supplier selection criteria are determined by using fuzzy Delphi and analytic hierarchy process, then suppliers are ranked by using Grey VIKOR which is a newly developed approach in the field of multi-criteria decision making under uncertainty Manuscript profile -
Open Access Article
30 - Evaluation and selection of development projects of steel using grey systems theory, vague sets theory, and zero one goal programming: A hybrid approach
حسین صفری محمد هادی خلوصی سمیه خلوصیOne of issues that managers face is decision about selecting a set of most appropriate projects. Choosing more effective projects and allocating resource optimally is part of strategic decisions in an organization which requires the judgment of specialists in evaluation MoreOne of issues that managers face is decision about selecting a set of most appropriate projects. Choosing more effective projects and allocating resource optimally is part of strategic decisions in an organization which requires the judgment of specialists in evaluation process. In many cases, judgments of decision makers in evaluation have some uncertainty. Methods which take into account the ambiguity in human judgments accurately would be very desirable. The aim of this research is to propose a suitable approach for evaluation development projects and finally select the most suitable ones, based on qualitative and quantitative indices. Proposed approach takes into account the ambiguity in human judgments by using grey systems theory and vague sets theory which both are the fuzzy sets theory expanded. This hybrid approach would make it possible to select the most appropriate projects based on both qualitative and quantitative indices Manuscript profile -
Open Access Article
31 - Introduction of a Robust Model in Order to Select Suppliers in Supply Chain: An Integration of AHP-Bull’s Eye-PAF Approach
علی بنیادی نایینی mohammad hasan kamfirooziNowadays in the markets competitive atmosphere, the companies aren’t able to provide their needs. So they prefer to use supply chain. Selecting the suppliers is introduced as a multi attribute decision making (MADM) problem. The current paper tries to solve this problem MoreNowadays in the markets competitive atmosphere, the companies aren’t able to provide their needs. So they prefer to use supply chain. Selecting the suppliers is introduced as a multi attribute decision making (MADM) problem. The current paper tries to solve this problem based on three parameter interval grey numbers. So, within use of AHP-Bull’s eye integration weighting method, this paper wants to weigh attributes and ranks alternatives by PAF (Projection Attribute Function) method. at the end, in order to show the ability of this method, the robustness of this method is tested in front of SAW method (that is one of the celebrated method). Manuscript profile -
Open Access Article
32 - Comparison of Traditional DEA Approaches to Portfolio Selection by a New Proposed Algorithm with a Case Study of Stock Selection in Tehran Stock Exchange
Abstract Portfolio selection is one of the most important areas in financial decision-making; A portfolio of stocks that could bring the highest rate of return and the lowest risk investment for its owner simultaneously. However in choosing the most prefered portfolio MoreAbstract Portfolio selection is one of the most important areas in financial decision-making; A portfolio of stocks that could bring the highest rate of return and the lowest risk investment for its owner simultaneously. However in choosing the most prefered portfolio just these factors are not decisive and according to the economic environment, many factors can affect this process which should be employed. Therefore, these diversity of factors, bring to the limelight the importance of multi-criteria decision-making approaches. Data Envelopment Analysis (DEA) is one of this approaches. The main purpose of this paper is comparing the traditional DEA approaches to a new proposed algorithm. In traditional approaches simply assumed that return to scale is constant or variable. This simplification may cause large errors. In the new algorithm by analyzing the behavior of return to scale, appropriate model will be used. As a case study, the models have been solved with real data belonging to Tehran stock exchange and the results have been analyzed. Manuscript profile -
Open Access Article
33 - Choose the best suppliers with a decision support system based on self-organizing neural networks in Oil Projects
Meysam Jafari Eskandari mostafa yousefiOne of the most essential activities to create the appropriate supply chain is supplier selection process. The system must be capable of providing buyer's requirements in terms of quality products, at affordable prices and at the appropriate time and volume. The nature MoreOne of the most essential activities to create the appropriate supply chain is supplier selection process. The system must be capable of providing buyer's requirements in terms of quality products, at affordable prices and at the appropriate time and volume. The nature of these decisions is usually complex and unstructured. In this study, self-organizing neural networks for decision making for the supplier selection decision is provided in a decision support system environment. Using self-organizing neural networks as a clustering techniques, suppliers clustered. new supplier standards compared with spikes winner and will decide on accepting or rejecting provider. The output of the model is supplier selection and evaluation by appropriate conditions for new suppliers. Manuscript profile -
Open Access Article
34 - Portfolio Selection under Trading Constraints and Data Uncertainty Using Robust Optimization Approach and NSGA-II Algorithm
Pejman Peykani Alireza JandaghianPortfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered MorePortfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered in the proposed approach for portfolio selection, is uncertainty. Because, one of the most important features of financial markets is their uncertainty. Thus, the purpose of this study is to present a bi-objective model for portfolio selection that is capable to be used under uncertainty of financial data and for this purpose, a robust optimization approach has been used. It should be noted that return and conditional value at risk (CVaR) are considered as model objectives, and the constraints of the number of shares and the purchasing volume of each share have been added to the model. Also, due to the complexity of the proposed model, a NSGA-II meta-heuristic algorithm has been used to solve the suggested model of research. Finally, the presented model was solved by using the actual data of 200 stocks of Tehran stock market for the period of 2017 and the results were analyzed. The results indicate the efficiency of the proposed approach portfolio selection according to the investor's preferences and constraints under uncertainty of financial data. Manuscript profile -
Open Access Article
35 - Provide a multi-objective mathematical programming model for supplier selection in discounted status
Abbas Shoul ali yaghoubipoor laleh abbasloIn the present study, the supplier selection problem from a new perspective is formulated and solved. For this purpose, minimizing cost and minimizing delivery time and maximizing reliability are considered as objectives of the problem. The purchasing cost in discounted MoreIn the present study, the supplier selection problem from a new perspective is formulated and solved. For this purpose, minimizing cost and minimizing delivery time and maximizing reliability are considered as objectives of the problem. The purchasing cost in discounted status, the order cost, the transportation cost, the shortage cost and the carrying cost, the components forming the cost objective function. In this research, the design of reliability in parallel systems is benchmarked for supplier selection, so that the supplier's set is considered as a system and each supplier as a component. After mathematical modeling of the problem, the objective functions are described as fuzzy goals and a fuzzy decision approach has been used to rewrite the proposed three-objective model as a single-objective model. The production of a final solution rather than a set of Pareto's solutions is one of the advantages of the proposed method, which prevents decision-making confusion. In order to describe the function and potential application of the proposed method, the supplier selection problem with the actual data was solved. Manuscript profile -
Open Access Article
36 - A systematic review of artificial neural network applications in supply chain management
Aref Toghroljerdi pooria malekinejadNowadays, 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 MoreNowadays, 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
37 - Cache Point Selection and Transmissions Reduction using LSTM Neural Network
Malihe Bahekmat Mohammad Hossein Yaghmaee MoghaddamReliability 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 MoreReliability 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
38 - Analysing students' learning through morning exercise using data mining techniques
behzad lak narges abbasiSince school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regul MoreSince school has identified as one of the major agents in the socialization process, it has found remarkable position in the educational system of any country. Improving student learning is also a key factor to enhance the educational system quality in schools. As regular exercise has profoundly positive impact on learning, this paper mainly aims to provide an approach to enhance students' learning process through morning exercise based on artificial neural network (ANN) technique and intelligent water drop optimization algorithm. This study is a quantitative research, which is purposefully a descriptive-analytical and methodologically a practical study. To that end, ANN technique was used to classify and extract the results, as well as, intelligent water drop optimization algorithm was employed for feature selection. In ANN, eleven neurons were selected as the appropriate number of hidden layer neurons; a combination of two linear and sigmoidal activation functions were employed as interlayer transmission functions; a training function was applied to train the network; and a maximum 3000 duplicates was proposed for the training algorithm on dataset. The accuracy of the proposed method was 68%, which has improved by about 2.2% compared to the basic method, i.e., exercise has a positive effect on students' learning. The results showed a proper performance of the optimal classification on the dataset with homogeneous parameters as well as a better performance of the artificial neural networks than the novel methods. Accordingly, the proposed method can have an appropriate improvement in terms of output accuracy in strengthening the learning process. Manuscript profile -
Open Access Article
39 - Experimental Modeling of Two-Dimensional Systems with ARMA Structure
M. sadabadi M. shafiee M. karrariIn this paper, experimental modeling of two-dimensional discrete systems with ARMA structure is considered. Therefore two-dimensional model order selection and parameter estimation problems are proposed. This method shows that the information of AR and MA orders are imp MoreIn this paper, experimental modeling of two-dimensional discrete systems with ARMA structure is considered. Therefore two-dimensional model order selection and parameter estimation problems are proposed. This method shows that the information of AR and MA orders are implicitly contained in two different correlation matrices and the AR and MA orders of the 2-D ARMA model can be independently determined before parameter estimation. The two-dimensional model is assumed to be causal, stable, linear, and spatial shift-invariant with quarter plane (QP) support. Numerical Simulations are presented to show the good performance and effectiveness of the proposed method in two-dimensional discrete system with ARMA structure. Manuscript profile -
Open Access Article
40 - Investigation of Sensing Range for High Speed Target Tracking in Wireless Sensor Networks
M. R. Zoghi M. H. KahaeiIn this paper, we propose a new approach for selection of subsets of active sensors with some constraints on energy consumption and estimation error for tracking of a target. The proposed approach exploits the decentralized estimation by using the extended information f MoreIn this paper, we propose a new approach for selection of subsets of active sensors with some constraints on energy consumption and estimation error for tracking of a target. The proposed approach exploits the decentralized estimation by using the extended information filter for target tracking. Furthermore, a cost function is defined using spatial correlation for sensor selection. Consequently, the Spatial Split algorithm is proposed based on spatial correlation coefficients for sensor selection. At last, for high speed targets, we propose a modification on spatial split algorithm by changing the sensing range with respect to the target speed. Simulation results show that the tracking accuracy is analogous to those of optimal estimation methods. It is also found that energy consumption decreases due to activating only necessary sensors. Manuscript profile -
Open Access Article
41 - Ensemble Feature Selection Strategy Based on Hierarchical Clustering in Electronic Nose
M. A. Bagheri Gh. A. MontazerThe redundancy problem of sensor response in electronic noses is still remarkable due to the cross-selectivity of chemical gas sensors which can degrade the classification performance. In such situations, a more efficient multiple classifier system can be obtained in ra MoreThe redundancy problem of sensor response in electronic noses is still remarkable due to the cross-selectivity of chemical gas sensors which can degrade the classification performance. In such situations, a more efficient multiple classifier system can be obtained in random feature space rather than in the original one. Ensemble Feature Selection (EFS) methods assume that there is redundancy in the overall feature set and better performance can be achieved by choosing different subsets of input features for multiple classifiers. By combining these classifiers the higher recognition rate can be achieved. In this paper, we propose a feature subset selection method based on hierarchical clustering of transient features in order to enhance the classifier diversity and efficiency of learning algorithms. Our algorithm is tested on the UCI benchmark data sets and then used to design an odor recognition system. The experimental results of proposed method based on hierarchical clustering feature subset selection and multiple classifier system demonstrate the more efficient classification performance. Manuscript profile -
Open Access Article
42 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
N. Ghanbari S. M. Razavi S. H. Nabavi KariziIn this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, us MoreIn this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, using binary gravitational search algorithm. Implementation results show that the use of intelligent methods is well able to choose the most effective features for this recognition system. The results of the proposed method in comparison with other similar methods based on genetic algorithm and binary particle method of optimizing indicates the effective performance of the proposed method. Manuscript profile -
Open Access Article
43 - Intelligent Bargaining in Market Using Reinforcement Learning
M. A. Saadatjoo V. Derhami فاطمه سعادت جوUsing Information Technology techniques have been increased complication and dynamicity of supply-and-demand systems like auctions. In this paper, we introduce a novel method by applying Reinforcement Learning (RL) price offer as one of the robust methods of agent learn MoreUsing Information Technology techniques have been increased complication and dynamicity of supply-and-demand systems like auctions. In this paper, we introduce a novel method by applying Reinforcement Learning (RL) price offer as one of the robust methods of agent learning which can be used in interactive conditions with minimum level of information in auction and reverse auction. Negotiation as one of the challengeable and complicated behaviors is caused an agreement on price in auctions. The main aim of our method is maximizing seller’s and customer’s profits. We formulate seller and customer selection in form of two different RL problems. All of the RL parameters like states, actions, and reinforcement function are defined. Also, we describe an experimental method to compare with our proposed method for proving advantages of our method. Manuscript profile -
Open Access Article
44 - A New Scheme for Automatic Classification of Power Quality Disturbances Based on Signal Processing and Machine Learning
M. Hajian A. Akbari ForodIdentification and classification of power quality disturbances (PQDs) are one of the most important functions of monitoring and protection of modern power systems. One of the most important issues in PQ analysis is automatic diagnosis of waveforms using an effective al MoreIdentification and classification of power quality disturbances (PQDs) are one of the most important functions of monitoring and protection of modern power systems. One of the most important issues in PQ analysis is automatic diagnosis of waveforms using an effective algorithm. This paper presents an effective method, for extracting features, using integration of discrete wavelet transform (DWT) and hyperbolic S transform (HST). Moreover, an efficient feature selection method namely Orthogonal Forward Selection (OFS) by incorporating Gram Schmidt (GS) procedure and forward selection is applied for selection of the best subset features. Multi support vector machines (MSVM), as famous classifier, is applied. Also, the variable parameters of the classifier are optimized using a powerful method namely particle swarm optimization (PSO). Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of the proposed expert system under different noisy conditions is investigated. Also, efficiency of the proposed methods by comparing the results of this study with the results of other papers is examined. Manuscript profile -
Open Access Article
45 - Introducing a New Version of Binary Ant Colony Algorithm to Solve the Problem of Feature Selection
S. Kashef H. Nezamabadi-pourThe use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of MoreThe use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of binary ant colony optimization, which attempts to solve the problems of ACO and BACO algorithms by combination of these two. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods. Manuscript profile -
Open Access Article
46 - EBONC: A New Energy-Aware Clustering Approach Based on Optimum Number of Clusters for Mobile Wireless Sensor Networks
N. Norouzy N. Norouzy M. FazlaliThe 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 MoreThe 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
47 - Classification and Phishing Websites Detection by Fuzzy Rules and Modified Inclined Planes Optimization
M. Abdolrazzagh-NezhadOne of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibili MoreOne of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibility to effective attribute selection in the procedure of phishing websites detection, non- dynamic behavior of classification algorithm on target websites and also no attention to reduce the amount of computation for the large number of websites are the main gaps of these methods. To achieve the above-mentioned objectives, a new dynamic mechanism is planned to flexible attribute reduction based on designing threshold change of assessment in this paper. Then inclined planes optimization algorithm is memorized based soft reducing the effect of the embedded memory though high iterations and 12 fuzzy rules are defined in a fuzzy inference system for intelligent dynamiting the algorithm. The experimental results of the proposed intelligent algorithm and the comparison the algorithms with the best available algorithms; demonstrate the ability of the modified inclined planes optimization algorithm to detect phishing websites and satisfy the above mentioned objectives. Manuscript profile -
Open Access Article
48 - A Hybrid-Based Feature Selection Method for High-Dimensional Data Using Ensemble Methods
A. Rouhi H. Nezamabadi-pourNowadays, with the advent and proliferation of high-dimensional data, the process of feature selection plays an important role in the domain of machine learning and more specifically in the classification task. Dealing with high-dimensional data, e.g. microarrays, is as MoreNowadays, with the advent and proliferation of high-dimensional data, the process of feature selection plays an important role in the domain of machine learning and more specifically in the classification task. Dealing with high-dimensional data, e.g. microarrays, is associated with problems such as increased presence of redundant and irrelevant features, which leads to decreased classification accuracy, increased computational cost, and the curse of dimensionality. In this paper, a hybrid method using ensemble methods for feature selection of high dimensional data, is proposed. In the proposed method, in the first stage, a filter method reduces the dimensionality of features and then, in the second stage, two state-of-the-art wrapper methods run on the subset of reduced features using the ensemble technique. The proposed method is benchmarked using 8 microarray datasets. The comparison results with several state-of-the-art feature selection methods confirm the effectiveness of the proposed approach. Manuscript profile -
Open Access Article
49 - Next Hop Selection to Configuring the Route in LEAP Protocol Based on Fuzzy Logic in WSNs
Vahid Sattari-Naeini F. MovahhediSince in wireless sensor networks, selection of next hop is critical in attack avoidance and lowering the power consumption, a method based on fuzzy logic is proposed in this paper considering status and report transmission of the nodes. In this method, the next hop is MoreSince in wireless sensor networks, selection of next hop is critical in attack avoidance and lowering the power consumption, a method based on fuzzy logic is proposed in this paper considering status and report transmission of the nodes. In this method, the next hop is selected considering four factors, based on fuzzy logic system. These factors, indicating four optimized parameters; i.e., degree of node proximity to the shortest path, degree of node proximity to the sink, residual energy ratio of each node, and the number of false filtered messages. This method leads to an increase in energy level as well as maintaining security level in comparison with LEAP protocol. Meanwhile, it is possible to identify safe paths. Comparing with other related methods, it is shown that this method leads to significant reduction in energy consumption level and consequently the life-time of the network is increased. Meanwhile with selecting the appropriate next hop, packet drops are reduced as well. Manuscript profile -
Open Access Article
50 - Reduce Dimensions of CDF Steganalysis Approach Using a Graph Theory Based Feature Selection Method
S. Azadifar S. H. Khasteh M. H. EdrisiThe steganalysis purpose is to prevent the pursuit of steganography methods for your goals. In steganography, in order to evaluate new ideas, there should be known steganalysis attacks on them, and the results should be compared with other existing methods. One of the m MoreThe steganalysis purpose is to prevent the pursuit of steganography methods for your goals. In steganography, in order to evaluate new ideas, there should be known steganalysis attacks on them, and the results should be compared with other existing methods. One of the most well-known steganalysis methods is CDF method that used in this research. One of the major challenges in the image steganalysis issue is the large number of extracted features. High-dimensional data sets from two directions reduce steganalysis performance. On the one hand, with the increase in the dimensions of the data, the volume of computing increases, and on the other hand, a model based on high-dimensional data has a low generalization capability and increases probability of overfitting. As a result, reducing the dimensions of the problem can both reduce the computational complexity and improve the steganalysis performance. In this paper, has been tried to combine the concept of the maximum weighted clique problem and edge centrality measure, and to consider the suitability of each feature, to select the most effective features with minimum redundancy as the final features. The simulation results on the SPAM and CC-PEV data showed that the proposed method had a good performance and accurately obtained about 96% in the detection of data embedding in the images, and this method is more accurate than the previously known methods. Manuscript profile -
Open Access Article
51 - Attribute Reduction Based on Rough Set Theory by Soccer League Competition Algorithm
M. Abdolrazzagh-Nezhad Ali AdibiyanIncreasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been con MoreIncreasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer league competition algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low algorithm’s parameters were the motivation of considering the algorithm in the current research. The proposed ideas to modify the algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art algorithms. This comparison shows that the competitive advantages of the proposed algorithm over the investigated algorithms. Manuscript profile -
Open Access Article
52 - Optimizing the Selection and Composition of QoS-Aware Web Services by Considering Dependency, Conflict, and Correlation between Web Services
mahdi farzandway F. ShamsToday, the continuous changes in customer requirements are the main challenges faced by enterprises. Service-oriented architecture is considered as a practical solution to solve this problem for service-oriented enterprises. In the service-oriented architecture, selecti MoreToday, the continuous changes in customer requirements are the main challenges faced by enterprises. Service-oriented architecture is considered as a practical solution to solve this problem for service-oriented enterprises. In the service-oriented architecture, selection and composition of services to quickly respond to complex customer requirements is available to service-oriented enterprises. Enterprises use ready-to-use and outsourced services to respond more quickly to the complex and changing needs of customers. One of the emerging technologies in this area is web services. By expanding the desire of enterprises to use web services, overtime web services providers increased. For this reason, Web services with the same functionality and different qualities were expanded. Therefore, the issue of choosing a web service with the best quality for enterprises is important. On the other hand, enterprises with only one web service cannot meet the complex requirements of customers; therefore, they need to composite multiple web services together. In addition, with the increase of web services with different functions, correlation, dependency and conflict between Web services also expand in their composition. But so far, there is no way to choose the best web services based on the quality of service(QoS) and also their composition does not violate the dependency, conflict and correlation between web services. In this paper, we try to make use of previous methods that consider dependency or conflict or correlation in simple modes of web services composition. We will improve all these methods in a comprehensive approach and support complex situations that may arise from the composition of web services and find the suitable composite web service by considering dependency, conflict, and correlation between Web services. Manuscript profile -
Open Access Article
53 - Robot Path Planning using Clonal Selection Algorithm
S.A. daneshnia S. Golzari A. Harifi A. A. RezaeePath planning of mobile robot is one of the most important topics in mobile robotic discussion. The aim of this study is to find a continuous path from an initial position to the final target; So that, it should be free of collision and optimal or near to optimal. Since MorePath planning of mobile robot is one of the most important topics in mobile robotic discussion. The aim of this study is to find a continuous path from an initial position to the final target; So that, it should be free of collision and optimal or near to optimal. Since path planning problem of robot is one type of optimization problems, the evolutionary algorithms can be used to solve this problem. Nowadays, clonal selection algorithm is frequently used to solve the problems because of having valuable computational characteristics. But very little attempts have been done in the field of using this method to solve robot path planning problem. Few accomplished attempts are actually a kind of improved genetic algorithm. In this research, an efficient method for robot path planning in the presence of obstacles is designed using all the features of the clonal selection algorithm. The proposed method is evaluated in various environments with different runs in terms of the proposed path length criteria and the number of generations needed to generate the path. Based on the results of experiments, the proposed method shows better performance than the genetic algorithm in all environments and all the evaluation parameters. Especially, by increasing the number of obstacles vertices and also concave obstacles, the proposed method shows much more efficient performance than the genetic algorithm. Also, comparing the performance of the proposed method with the BPSO algorithm (presented in another study) indicates the superiority of path planning algorithm based on the clonal selection. Manuscript profile -
Open Access Article
54 - Diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) based on Variable Length Evolutionary Algorithm
M. Ramzanyan Hussain Montazery KordyThe methods used today to investigate brain connections to diagnose brain-related diseases are the imaging method of resting magnetic resonance imaging. In this paper, a new method is proposed using an evolutionary variable-length algorithm to select the appropriate fea MoreThe methods used today to investigate brain connections to diagnose brain-related diseases are the imaging method of resting magnetic resonance imaging. In this paper, a new method is proposed using an evolutionary variable-length algorithm to select the appropriate features to improve the accuracy of the diagnosis of healthy and patient-to-patients with attention deficit hyperactivity disorder based on analysis of rs-fMRI images. The characteristics examined are the correlation values between the time series signals of different regions of the brain. Selection of the variable-length property were based on the honey bee algorithm in order to overcome the problem of feature selection in algorithms with fixed-length vector lengths. The Mahalanubis distance has been used as a bee algorithm evaluation function. The efficiency of the algorithm was evaluated in terms of the value of the evaluation function in the first degree and the processing time in the second degree. The results obtained from the significantly higher efficiency of the variable-length bee algorithm than other methods for selecting the feature. While the best result of the overall categorization accuracy among the other methods with the 26 selected characteristics of the PSO algorithm is 76.61%, the proposed method can achieve a total classification accuracy of 85.32% by selecting 25 features. The nature of the data is such that the increase in the number of attributes leads to a greater improvement in the accuracy of the classification so that by increasing the length of the characteristic vector to 35 and 45, classification accuracy was 91.66% and 95.57% respectively. Manuscript profile -
Open Access Article
55 - A Feature Selection Algorithm in Online Stream Dataset Based on Multivariate Mutual Information
Maryam Rahmaninia Parham MoradiToday, in many real-world applications, such as social networks, we are faced with data streams which new data is appeared every moment. Since the efficiency of most data mining algorithms decreases with increasing data dimensions, analysis of the data has become one of MoreToday, in many real-world applications, such as social networks, we are faced with data streams which new data is appeared every moment. Since the efficiency of most data mining algorithms decreases with increasing data dimensions, analysis of the data has become one of the most important issues recently. Online stream feature selection is an effective approach which aims at removing those of redundant features and keeping relevant ones, leads to reduce the size of the data and improve the accuracy of the online data mining methods. There are several critical issues for online stream feature selection methods including: unavailability of the entire feature set before starting the algorithm, scalability, stability, classification accuracy, and size of selected feature set. So far, existing methods have only been able to address a few numbers of these issues simultaneously. To this end, in this paper, we present an online feature selection method called MMIOSFS that provides a better tradeoff between these challenges using Mutual Information. In the proposed method, first the feature set is mapped to a new feature using joint Random variables technique, then the mutual information of new feature with the class label is computed as the degree of relationship between the features set. The efficiency of the proposed method was compared to several online feature selection algorithms based on different categories. The results show that the proposed method usually achieves better tradeoff between the mentioned challenges. Manuscript profile -
Open Access Article
56 - Feature Selection and Cancer Classification Based on Microarray Data Using Multi-Objective Cuckoo Search Algorithm
kh. Kamari f. rashidi a. KhaliliMicroarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Mo MoreMicroarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy. Manuscript profile -
Open Access Article
57 - Efficient Recognition of Human Actions by Limiting the Search Space in Deep Learning Methods
m. koohzadi N. MoghadamThe efficiency of human action recognition systems depends on extracting appropriate representations from the video data. In recent years, deep learning methods have been proposed to extract efficient spatial-temporal representations. Deep learning methods, on the other MoreThe efficiency of human action recognition systems depends on extracting appropriate representations from the video data. In recent years, deep learning methods have been proposed to extract efficient spatial-temporal representations. Deep learning methods, on the other hand, have a high computational complexity for development over temporal domain. Challenges such as the sparsity and limitation of discriminative data, and highly noise factors increase the computational complexity of representing human actions. Therefore, creating a high accurate representation requires a very high computational cost. In this paper, spatial and temporal deep learning networks have been enhanced by adding appropriate feature selection mechanisms to reduce the search space. In this regard, non-online and online feature selection mechanisms have been studied to identify human actions with less computational complexity and higher accuracy. The results showed that the non-linear feature selection mechanism leads to a significant reduction in computational complexity and the online feature selection mechanism increases the accuracy while controlling the computational complexity. Manuscript profile -
Open Access Article
58 - Outlier Detection in High Dimensional Data Using Entropy-Based Locally Relevant Subspace Selection
Mahboobeh Riahi Madvar Ahmad Akbari B. NasersharifOne of the challenges of high dimensional outlier detection problem is the curse of dimensionality which irrelevant dimensions (features) lead to hidden outliers. To solve this problem, some dimensions that contain valuable information to detect outliers are searched to MoreOne of the challenges of high dimensional outlier detection problem is the curse of dimensionality which irrelevant dimensions (features) lead to hidden outliers. To solve this problem, some dimensions that contain valuable information to detect outliers are searched to make outliers more prominent and detectable by mapping the dataset into the subspace which is constituted of these relevant dimensions/features. This paper proposes an outlier detection method in high dimensional data by introducing a new locally relevant subspace selection and developing a local density-based outlier scoring. First, we present a locally relevant subspace selection method based on local entropy to select a relevant subspace for each data point due to its neighbors. Then, each data point is scored in its relevant subspace using a density-based local outlier scoring method. Our adaptive-bandwidth kernel density estimation method eliminates the slight difference between the density of a normal data point and its neighbors. Thus, normal data are not wrongly detected as outliers. At the same time, our method underestimates the actual density of outlier data points to make them more prominent. The experimental results on several real datasets show that our local entropy-based subspace selection algorithm and the proposed outlier scoring can achieve a high accuracy detection rate for the outlier data. Manuscript profile -
Open Access Article
59 - Analysing the competency criteria of the staff managers of the education headquarter in the level of leadership and management subsystem: a qualitative study report
عباس محمدی Rashid Zolfaghari zaferani Heidar Tourani Mehdi NavidadehmStaff managers of education have a key role in policy-making, macro-planning, and monitoring the performance of the education system, which is why the selection of this category of managers is particularly sensitive. Considering the emphasis of the Fundamental Evolution MoreStaff managers of education have a key role in policy-making, macro-planning, and monitoring the performance of the education system, which is why the selection of this category of managers is particularly sensitive. Considering the emphasis of the Fundamental Evolution Document on the selection of managers at different levels based on meritocracy in the 6-22 strategy of "Leadership and Management Subsystem", this article identifies the competency criteria of education staff managers at the level of this document, from the perspective of the authors of Fundamental Evolution Document, and attitudes of specialists in this field. Due to the nature of the question and the purpose of this research, its approach is qualitative and has been with data-based theory and Charlmers constructivism design. The data required for the study were collected through semi-structured interviews with 22 experts, including nine compilers of Fundamental Evolution Document, seven experts and executives of the leadership and management subsystem, and six educational management faculty members. The text of the interviews was implemented and analysed using thematic analysis. A total of 63 competencies were identified as competencies required for education staff managers in the form of 17 sub-categories and 6 main factors including; values and attitudes, managerial skills and abilities, personality traits, personal knowledge and skills, organizational knowledge and skills, and monitoring and control, and finally two specialized and general dimensions were determined and defined Manuscript profile -
Open Access Article
60 - Introducing Intelligent Mutation Method Based on PSO Algorithm to Solve the Feature Selection Problem
Mahmoud Parandeh Mina Zolfy Lighvan jafar tanhaToday, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection alg MoreToday, with the increase in data production volume, attention to machine learning algorithms to extract knowledge from raw data has increased. Raw data usually has redundant or irrelevant features that affect the performance of learning algorithms. Feature selection algorithms are used to improve efficiency and reduce the computational cost of machine learning algorithms. A variety of methods for selecting features are provided. Among the feature selection methods are evolutionary algorithms that have been considered because of their global optimization power. Many evolutionary algorithms have been proposed to solve the feature selection problem, most of which have focused on the target space. The problem space can also provide vital information for solving the feature selection problem. Since evolutionary algorithms suffer from the pain of not leaving the local optimal point, it is necessary to provide an effective mechanism for leaving the local optimal point. This paper uses the PSO evolutionary algorithm with a multi-objective function. In the proposed algorithm, a new mutation method that uses the particle feature score is proposed along with elitism to exit the local optimal points. The proposed algorithm is tested on different datasets and examined with existing algorithms. The simulation results show that the proposed method has an error reduction of 20%, 11%, 85%, and 7% in the Isolet, Musk, Madelon, and Arrhythmia datasets, respectively, compared to the new RFPSOFS method. Manuscript profile -
Open Access Article
61 - Improving IoT Botnet Anomaly Detection Based on Dynamic Feature Selection and Hybrid Processing
Boshra Pishgoo Ahmad akbari aziraniThe complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of the MoreThe complexity of real-world applications, especially in the field of the Internet of Things, has brought with it a variety of security risks. IoT Botnets are known as a type of complex security attacks that can be detected using machine learning tools. Detection of these attacks, on the one hand, requires the discovery of their behavior patterns using batch processing with high accuracy, and on the other hand, must be operated in real time and adaptive like stream processing. This highlights the importance of using batch/stream hybrid processing techniques for botnet detection. Among the important challenges of these processes, we can mention the selection of appropriate features to build basic models and also the intelligent selection of basic models to combine and present the final result. In this paper, we present a solution based on a combination of stream and batch learning methods with the aim of botnet anomaly detection. This approach uses a dynamic feature selection method that is based on a genetic algorithm and is fully compatible with the nature of hybrid processing. The experimental results in a data set consisting of two known types of botnets indicate that on the one hand, the proposed approach increases the speed of hybrid processing and reduces the detection time of the botnets by reducing the number of features and removing inappropriate features, and on the other hand, increases accuracy by selecting appropriate models for combination. Manuscript profile -
Open Access Article
62 - Multi-Label Feature Selection Using a Hybrid Approach Based on the Particle Swarm Optimization Algorithm
َAzar Rafiei Parham Moradi Abdolbaghi GhaderzadehMulti-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in mul MoreMulti-label classification is one of the important issues in machine learning. The efficiency of multi-label classification algorithms decreases drastically with increasing problem dimensions. Feature selection is one of the main solutions for dimension reduction in multi-label problems. Multi-label feature selection is one of the NP solutions, and so far, a number of solutions based on collective intelligence and evolutionary algorithms have been proposed for it. Increasing the dimensions of the problem leads to an increase in the search space and consequently to a decrease in efficiency and also a decrease in the speed of convergence of these algorithms. In this paper, a hybrid collective intelligence solution based on a binary particle swarm optimization algorithm and local search strategy for multi-label feature selection is presented. To increase the speed of convergence, in the local search strategy, the features are divided into two categories based on the degree of extension and the degree of connection with the output of the problem. The first category consists of features that are very similar to the problem class and less similar to other features, and the second category is similar features and less related. Therefore, a local operator is added to the particle swarm optimization algorithm, which leads to the reduction of irrelevant features and extensions of each solution. Applying this operator leads to an increase in the convergence speed of the proposed algorithm compared to other algorithms presented in this field. The performance of the proposed method has been compared with the most well-known feature selection methods on different datasets. The results of the experiments showed that the proposed method has a good performance in terms of accuracy. Manuscript profile -
Open Access Article
63 - The efficacy of multivariate regression models and GIS in Selecting SuitableSites for Rain Water Harvesting (Case Study: Tajareh Watershed)
maryam aghaie siamak dokhani ebrahim omidvarWater scarcity in arid areas is a serious crisis. The most important step in using rainwater collection systems is to locate suitable areas. In this research, three methods of multivariate regression model and GIS have been used to locate the on-site and off-site rainwa MoreWater scarcity in arid areas is a serious crisis. The most important step in using rainwater collection systems is to locate suitable areas. In this research, three methods of multivariate regression model and GIS have been used to locate the on-site and off-site rainwater collection method in Tejreh watershed. In this study, canopy, litter, rock and gravel, bare soil, CN, precipitation, slope and soil depth as independent variables and influence on in situ rainwater collection and maximum instantaneous discharge for non-in situ rainwater collection method The title of the dependent variable was considered. The multivariate regression model uses stepwise method, backward removal method, and forward method. And the standard step-by-step method, regression removal method, step-by-step method in collecting rainwater, non-in situ method have been used. The final results by matching the results of previous research show in step rainwater collection, stepwise method and between layers CN, soil, percentage of rock and gravel, and in non-in situ rainwater collection stepwise regression method Standard and among layers the percentage of litter, percentage of canopy, CN, slope, percentage of rocks and pebbles, amount of rainfall, percentage of bare soil and soil depth are known to be important in the equation. Finally, the importance of rain collection sites was divided into four classes: very good, good, medium and poor. Manuscript profile -
Open Access Article
64 - Application of Machine Learning in the Telecommunications Industry: Partial Churn Prediction by using a Hybrid Feature Selection Approach
Fatemeh Mozaffari Iman Raeesi Vanani Payam Mahmoudian Babak SohrabiThe telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic d MoreThe telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic decision-making and one of the main objectives of customer relationship management. Although customer churn prediction models are widely studied in various domains, several challenges remain in designing and implementing an effective model. This paper addresses the customer churn prediction problem with a practical approach. The experimental analysis was conducted on the customers' data gathered from available sources at a telecom company in Iran. First, partial churn was defined in a new way that exploits the status of customers based on criteria that can be measured easily in the telecommunications industry. This definition is also based on data mining techniques that can find the degree of similarity between assorted customers with active ones or churners. Moreover, a hybrid feature selection approach was proposed in which various feature selection methods, along with the crowd's wisdom, were applied. It was found that the wisdom of the crowd can be used as a useful feature selection method. Finally, a predictive model was developed using advanced machine learning algorithms such as bagging, boosting, stacking, and deep learning. The partial customer churn was predicted with more than 88% accuracy by the Gradient Boosting Machine algorithm by using 5-fold cross-validation. Comparative results indicate that the proposed model performs efficiently compared to the ones applied in the previous studies. Manuscript profile -
Open Access Article
65 - Power Efficient allocation in C-RAN with Multi access technology selection approach
ALI ASGHAR ANSARI Mohsen Eslami Mohammad Javad Dehghani Saeideh Parsaei Fard: In this paper, we consider an uplink economy-efficient resource allocation in a multicellular virtual wireless network with a C-RAN architecture where a MNO interacts with a number of MVNOs with a predetermined business model. In each cell of this system, two types of More: In this paper, we consider an uplink economy-efficient resource allocation in a multicellular virtual wireless network with a C-RAN architecture where a MNO interacts with a number of MVNOs with a predetermined business model. In each cell of this system, two types of multiple access technologies, namely OFDMA and Massive MIMO, are available for MVNO at two different prices. In this setup, we propose a multi access technology selection approach (MATSA) with the objective to reduce operating costs and maximize the profit of the MVNOs subject to a set of constraints, and formulate this resource allocation problem with the new utility function. Due to the existence of continuous and binary variables in the formulated optimization problem and also the interference between cells in data rate functions, this optimization problem will be non-convex with very high computational complexity. To tackle this problem, by applying the complementary geometric programming (CGP) and the successive convex approximation (SCA), an effective two-step iterative algorithm is developed to convert the optimization problem into two sub problems with the aim to find optimum technology selection and power consumption parameters for each user in two steps, respectively. The simulation results demonstrate that our proposed approach (MATSA) with novel utility function is more efficient than the traditional approach, in terms of increasing total EE and reducing total power consumption. The simulation results illustrate that the profit of the MVNOs is enhanced more than 13% compared to that of the traditional approach. Manuscript profile -
Open Access Article
66 - Evaluation of science and technology parks of selected countries with partner selection approach
mohammadreza rostamkhani Parastoo MohammadiWhich of the existing science and technology parks in the world are the most suitable options for creating international interaction? This article tries to find the answer to this question for one of the university parks in Iran using the partner selection approach. In MoreWhich of the existing science and technology parks in the world are the most suitable options for creating international interaction? This article tries to find the answer to this question for one of the university parks in Iran using the partner selection approach. In order to limit the scope of the evaluation of the existing parks in the world, first, 20 countries that have made up 90% of Iran's foreign trade volume in the past 5 years have been identified, then based on the needs assessment of the firms located in the studied science and technology park that have the capacity and the willingness of international cooperation, the countries under review were ranked from six aspects like as: economic, political, cultural, security, technological and geographical criteria and in 13 sub-criteria using the combined method of The Analytic Hierarchy Process (AHP) and TOPSIS . Seven countries with a significant score among 20 countries were selected as samples to evaluate their science and technology parks. 19 well-known parks in these 7 countries were scored and ranked using 12 criteria, from the perspective of people involved in park affairs, and with the combined method of AHP and TOPSIS. As a result of this ranking, the following parks were identified as a higher priority for cooperation: Dubai Silicon Oasis Park, Tsinghua University Science Park in China, Teknopark İstanbul, Sharjah Research, Technology and Innovation Park, Dubai Science Park, Knowledge Oasis Muscat (Madaen). Manuscript profile -
Open Access Article
67 - Combination of Instance Selection and Data Augmentation Techniques for Imbalanced Data Classification
Parastoo Mohaghegh Samira Noferesti Mehri RajaeiMohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method fo MoreMohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method for decision making and prediction. Classification algorithms usually work well on balanced datasets. However, one of the challenges of the classification algorithms is how to correctly predicting the label of new samples based on learning on imbalanced datasets. In this type of dataset, the heterogeneous distribution of the data in different classes causes examples of the minority class to be ignored in the learning process, while this class is more important in some prediction problems. To deal with this issue, in this paper, an efficient method for balancing the imbalanced dataset is presented, which improves the accuracy of the machine learning algorithms to correct prediction of the class label of new samples. According to the evaluations, the proposed method has a better performance compared to other methods based on two common criteria in evaluating the classification of imbalanced datasets, namely "Balanced Accuracy" and "Specificity". Manuscript profile -
Open Access Article
68 - Designing Service Outsourcing System in Support Organizations (Case Study: Imam Khomeini Relief Committee of Fars Province)
yalda sharafiyan Habib Allah Ranaei Kordshouli Moslem AlimohamadlooAbstract Objective: Economic complexity has led to the inefficiency of organizations, and they seek to strengthen their core competencies in order to achieve this goal by delegating some of their tasks in the form of outsourcing. The purpose of this study is to design MoreAbstract Objective: Economic complexity has led to the inefficiency of organizations, and they seek to strengthen their core competencies in order to achieve this goal by delegating some of their tasks in the form of outsourcing. The purpose of this study is to design a system of outsourcing of services, including the principles, processes and various activities of outsourcing services in Imam Khomeini Relief Committee in Fars province. Method: This research is cross-sectional in terms of practical purpose, type, case study, and time. The qualitative and quantitative analysis method is mixed and quantified in a way that has been proposed based on literature review and data extraction and with observation and in-depth interviews of the focus group of relief committee experts, processes specific to the relief committee and the characteristics of each stage of The process is approved in the proposed model and converted to the final and localized model. Findings: Findings in the form of providing a comprehensive system for outsourcing services in Imam Khomeini Relief Committee (RA) in a process with six steps, including preparing the outsourcing plan, identifying comparable services, identifying the index and selection of service providers, assignment of outstanding services, reorganization of the department after transfer and monitoring, monitoring and control of assigned services and the whole system, from the input stage, process, output and feedback in the form of A standard model of outsourcing is appropriate for this institution, along with the indicators of each stage. Keywords: outsourcing, outsourcing services, provider selection, monitoring of outsourcing services, Imam Khomeini Relief Committee Manuscript profile -
Open Access Article
69 - 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 EdalatpanahCloud 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 MoreCloud 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
70 - Fake Websites Detection Improvement Using Multi-Layer Artificial Neural Network Classifier with Ant Lion Optimizer Algorithm
Farhang Padidaran Moghaddam Mahshid Sadeghi B.In phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social MoreIn phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social engineering methods and persuade them to click on fake links. Phishing attacks have significant financial losses, and most attacks focus on banks and financial gateways. Machine learning methods are an effective way to detect phishing attacks, but this is subject to selecting the optimal feature. Feature selection allows only important features to be considered as learning input and reduces the detection error of phishing attacks. In the proposed method, a multilayer artificial neural network classifier is used to reduce the detection error of phishing attacks, the feature selection phase is performed by the ant lion optimization (ALO) algorithm. Evaluations and experiments on the Rami dataset, which is related to phishing, show that the proposed method has an accuracy of about 98.53% and has less error than the multilayer artificial neural network. The proposed method is more accurate in detecting phishing attacks than BPNN, SVM, NB, C4.5, RF, and kNN learning methods with feature selection mechanism by PSO algorithm. Manuscript profile -
Open Access Article
71 - Principle of Impartiality and Neutrality in the Exercise of Discretionary Powers in the Light of a Decision by the Administrative Court
Seyyed shahaboddin Musavizade merkieDiscretionary power is a power granted by the legislator to public authorities with the aim of serving the public interest. This power is exercised in various areas, including the recruitment of volunteers in government agencies. According to the Universal Declaration o MoreDiscretionary power is a power granted by the legislator to public authorities with the aim of serving the public interest. This power is exercised in various areas, including the recruitment of volunteers in government agencies. According to the Universal Declaration of Human Rights, individuals have the right to equal access to public employment in their own country. According to the Law on the Administration of Public Services, the principle of meritocracy applies to entry into executive agencies. However, placing criteria such as religious beliefs in addition to acceptance in the entrance competition has a legal basis according to the Law on the Administration of Public Services. The question now is how can these two important issues (meritocracy and compliance with selection criteria) be achieved? This article attempts to answer this question by describing and analyzing a decision by an administrative court through a library study using data extraction tools. The results showed that compliance with the principles of impartiality and neutrality by executive agencies and supervision by administrative courts are good tools for achieving the legislative goal of granting discretionary powers to administrative authorities. Manuscript profile -
Open Access Article
72 - Providing a New Solution in Selecting Suitable Databases for Storing Big Data in the National Information Network
Mohammad Reza Ahmadi davood maleki ehsan arianyanThe development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service produ MoreThe development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service productive tools and the need to store the results of large-scale processing resulting from various activities in the national network of information and data produced by the private sector and pervasive social networks has made the process of migrating to new databases 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 needs. Therefore, it is necessary to use data storage systems in new and scalable formats and models. This paper reviews the essential solution regarding the structural dimensions and different functions of traditional databases and modern storage systems and technical solutions for migrating from traditional databases to modern ones suitable for big data. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the national information network are presented and the parameters and capabilities of databases in the standard platform context and Hadoop context are examined. As a practical example, a combination of traditional and modern databases using the balanced scorecard method is presented as well as evaluated and compared. Manuscript profile
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