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Open Access Article
1 - A method for clustering customers using RFM model and grey numbers in terms of uncertainty
azime mozafariThe purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), fre MoreThe purpose of this study is presentation a method for clustering bank customers based on RFM model in terms of uncertainty. According to the proposed framework in this study after determination the parameter values of the RFM model, including recently exchange (R), frequency exchange (F), and monetary value of the exchange (M), grey theory is used to eliminate the uncertainty and customers are segmented using a different approach. Thus, bank customers are clustered to three main segments called good, ordinary and bad customers. After cluster validation using Dunn index and Davis Bouldin index, properties of customers are detected in any of the segments. Finally, recommendations are offered to improve customer relationship management system. Manuscript profile -
Open Access Article
2 - using clustering in AODV routing protocol for vehicular ad-hoc networks on highway scenario
amin feyziVehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network MoreVehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network are considered as a big challenge For routing in these networks, routing protocols must be robust and reliable. AODV Routing protocol is one of the known routing protocols in vehicular ad hoc networks. There are also some problems in applying this routing protocol on the vehicular ad hoc networks. The number of control massages increases with increasing the scale of the network and the number of nodes . One way to reduce the overhead in AODV routing protocol is clustering the nodes of the network. In this paper , the modified K-means algorithm has been used for clustering the nodes and particle swarm optimization has been used for selecting cluster head. The results of the proposed method improved normalized routing load and the increase of the packet delivery rate compared to AODV routing protocol. Manuscript profile -
Open Access Article
3 - Energy-efficient and Privacy preserving Data Aggreration in wireless sensor networks
zahra zareEnergy consumption is ranked among the major problems of research in wireless sensor networks(WSNs). The main reason for nodes failure is the discharge of batteries in sensor networks. Therefore, energy consumption plays an important role in wireless sensor networks. Da MoreEnergy consumption is ranked among the major problems of research in wireless sensor networks(WSNs). The main reason for nodes failure is the discharge of batteries in sensor networks. Therefore, energy consumption plays an important role in wireless sensor networks. Data aggregation can greatly help to reduce this consumption by eliminating redundant data, and using clustering methods for data aggregation helps to further reduce energy consumption. Sensor nodes are prone to node compromise attacks which cause an adversary to change the aggregation result and inject false data into the WSN, hence security issues such as data confidentiality and integrity are extremely important. Because both data aggregation and security are critical for wireless sensor networks, achieving secure data aggregation that protects integrity is a critical issue. In this paper, we present a secure data aggregation method called Energy-Efficient and Privacy-Preserving Data Aggregation using our clustering algorithm which is an improvement on LEACH protocol and Homomorphic Encryption technique. Manuscript profile -
Open Access Article
4 - Historical analysis of institutionalization trends in the field of science and technology policy in Iran
Seyed Kamal Vaezi mehrdad javaherdashtiThis study analyzes the institutions that have been used for science and technology policy in the Islamic Republic of Iran since the Pahlavi era (first and second Pahlavi) until now. Institutions that were created during this period and their duties and powers are brief MoreThis study analyzes the institutions that have been used for science and technology policy in the Islamic Republic of Iran since the Pahlavi era (first and second Pahlavi) until now. Institutions that were created during this period and their duties and powers are briefly mentioned and approaches such as modernization and modernization in the Pahlavi era, redefining values and value creation based on Islamic indicators in the period. After the Revolution of 1978, which were the source of the institutions of their time, using the historical method (prevailing environmental conditions) and the focus group, this trend was analyzed and in the final part in the form of conclusions, policy recommendations that From this analysis, it has been obtained such as merging some institutions, determining the unity mechanism of the same plurality of policy makers and implementers, vertical and horizontal strategic coordination between programs and macro documents in this field, and finally practical suggestions and suggestions for future research. . Manuscript profile -
Open Access Article
5 - Outdoor Color Scene Segmentation towards Object Detection using Dual-Resolution Histograms
javad rasti monadjemi monadjemi abbas vafaeiOne of the most important problems in automatic outdoor scene analysis is the approach of segmentation towards object detection. The special characteristics of such images -like color variety, different luminance effects and color shades, abundant texture details, and d MoreOne of the most important problems in automatic outdoor scene analysis is the approach of segmentation towards object detection. The special characteristics of such images -like color variety, different luminance effects and color shades, abundant texture details, and diversity of objects- lead to major challenges in the segmentation process. In the previous research, we proposed a k-means clustering algorithm in a multi-resolution platform for preliminary color segmentation. In this method, the texture details are deliberately expunged and apparent clusters are gradually removed in the blurred versions of the image to let more detailed classes expose in the more clarified versions. The performance of this step-by-step approach is relatively higher than the traditional k-means in color clustering for outdoor scene segmentation. In this paper, an adaptive method based on the circular hue histogram in a dual-resolution platform is suggested to detect the apparent clusters in the blurred images. Experimental results on two outdoor datasets show about 20% decrease in the pixel segmentation error as well as around 30% increase in both precision and speed in the convergence of the clustering algorithm. Manuscript profile -
Open Access Article
6 - Application of clustering in AODV routing protocol for intercity networks on the highway scenario
amin feyzi Vahid Sattari-Naeini majid mohammadiIntercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network MoreIntercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network topology are a major challenge for routing, for routing in these networks, routing protocols must be robust and reliable. One of the well-known routing protocols in intercity networks is the AODV routing protocol. The application of this routing protocol on intercity networks also has problems that increase the number of control messages in the network by increasing the scale of the network and the number of nodes. One way to reduce overhead in the AODV protocol is to cluster network nodes. In this paper, the modified K-Means algorithm is used to cluster the nodes and the particle swarm algorithm is used to select the cluster head. The results of the proposed method improve the normal routing load and increase the packet delivery rate compared to the AODV routing protocol. Manuscript profile -
Open Access Article
7 - Matched grouping of learners in e-learning environment using council clustering method
malihe kamareiy gholamali montazerDespite the individual differences of learners such as their abilities, goals, knowledge, learning styles and backgrounds, most of the electronic learning systems has presented an equal learning content for all of the learners. This is happening while producing a specia MoreDespite the individual differences of learners such as their abilities, goals, knowledge, learning styles and backgrounds, most of the electronic learning systems has presented an equal learning content for all of the learners. This is happening while producing a specialized content for the individuals. Increasing appliances of artificial memory in teaching the adaptation learning systems will require recommended teaching methods which are appropriate to the learner’s individual differences. In order to grouping learners based on their learning styles in their own similar groups, we are presenting a new method in this text. This method is mainly about combining the result of clustering methods which is certainly reducing choosing an unreliable method. Meanwhile it is preventing method`s complication which is because of using simpler and more useful clustering algorithms that subsequently will cause a better result and it may happen due to the fact that different methods will overlap each other’s defections. In this article we are using Felder- Silverman learning style which consist of 5 dimensions: processing (active-reflective) , input (visual-verbal) , understanding (sequential-global) , perception (sensing-intuitive) and organization (inductive-deductive). Firstly, proper behavioral indicators to different learning style dimension of Silverman-Feedler will recognize and then based on these behaviors learners will be able to be groups by one of these 5 methods. In the case of evaluating the proposed method, utilizing the c++ programming electronic teaching period information is necessary. Learner members of experiment environment were 98 ones which were extracting the expressed indicators connected to their network behaviors in 4 dimensions of Perception , process , input and understanding of Felder- Silverman model. On the other hand students were asked to fill the questionnaire forms and their learning styles were calculated between 0-11 and then based on the behavioral information they were being grouped. We are using 5 clustering grouping methods : k-means , FCM , KNN , K-Medoids and SVM to produce ensemble clustering in generation step and co-occurrence samples or majority votes were used in Integration step. Evaluating the results will require the followings : Davies-bouldin index , Variance index , and gathering purity index. Due to the fact that the expressed methods are not able to indicate automatically the best cluster, clustering 3,4,5,6,7 clusters were using this method. And with calculating Davies-bouldin index the best cluster in each method were selected. In FCM each data were contributed to the cluster which has the most dependence to that . Numerical results of Davies-bouldin index have shown that ensemble clusters have the exact accumulation clusters among the others. Clustering variance in different size is indicating that ensemble clustering has the most accumulation and the least dispersion and also purity-gathering results has shown that proposed grouping method has the ability to gather learners with the similar style in each cluster and has a better efficiency compared to the others. So with this idea while maintaining simplicity, more accurate results based on the Davies-bouldin index , Variance index , and gathering purity index is obtained. Due to the importance of high accuracy and high speed and low computational complexity in the clustering methods, instead of a more complex approach, combining the weaker and easier clustering methods, better and more accurate results reached. Manuscript profile -
Open Access Article
8 - Routing improvement to control congestion in software defined networks by using distributed controllers
saied bakhtiyari Ardeshir AzarnejadSoftware defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way th MoreSoftware defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way that the delay between controllers and switches in wide area networks can be reduced. In this regard, most of the proposed methods have focused on reducing latency. But latency is just one factor in network efficiency and overall cost reduction between controllers and related switches. This article examines more factors to reduce the cost between controllers and switches, such as communication link traffic. In this regard, a cluster-based algorithm is provided for network segmentation. Using this algorithm, it can be ensured that each part of the network can reduce the maximum cost (including delays and traffic on links) between the controller and its related switches. In this paper, using Topology Zoo, extensive simulations have been performed under real network topologies. The results of the simulations show that when the probability of congestion in the network increases, the proposed algorithm has been able to control the congestion in the network by identifying the bottleneck links in the communication paths of each node with other nodes. Therefore, considering the two criteria of delay and the degree of busyness of the links, the process of placing and distributing the controllers in the clustering operation has been done with higher accuracy. By doing so, the maximum end-to-end cost between each controller and its related switches, in the topologies Chinanet of China, Uunet of the United States, DFN of Germany, and Rediris of Spain, is decreased 41.2694%, 29.2853%, 21.3805% and 46.2829% respectively. Manuscript profile -
Open Access Article
9 - context-aware travel recommender system exploiting from Geo-tagged photos
rezvan mohamadrezaei larki Reza Ravanmehr milad amrolahiRecommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, locatio MoreRecommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, location . This paper is presented by the use of geographically tagged photo information which is highly accurate. The distinction point between this thesis and other similar articles is that this paper includes more context (weather conditions, users’ mental status, traffic level, etc.) than similar articles which include only time and location as context. This has brought the users close to each other in a cluster and has led to an increase in the accuracy. The proposed method merges the Colonial Competitive Algorithm and fuzzy clustering for a better and stronger processing against using merely the classic clustering and this has increased the accuracy of the recommendations. Flickr dataset is used to evaluate the presented method. Results of the evaluation indicate that the proposed method can provide location recommendations proportionate to the users’ preferences and their current visiting location. Manuscript profile -
Open Access Article
10 - Robustness of fuzzy c-mean method for delineation of hydrochemical facies distribution of groundwater in Varamin Plain
Mohammad Nakhaei Mehdi Talkhabi Meysam VadiatiIn this paper, classification of a large hydrochemical data set from Varamin plain is done by using fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques. Then its application to hydrochemical facies delineation is discussed. Groundwater samp MoreIn this paper, classification of a large hydrochemical data set from Varamin plain is done by using fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques. Then its application to hydrochemical facies delineation is discussed. Groundwater samples were grouped into three classes according to the optimum number of the classes and fuzziness exponent by using the fuzzy c-mean. The data set includes 90 deep and moderate deep well samples from groundwater data set and 9 hydrochemical variables were used. Results from both FCM and HCA clustering produced cluster centers that can be used to identify the physical and chemical processes creating the variations in the water chemistries. The optimum cluster in FCM method determined by optimization function, but in HCA method by trial and error. The FCM method is potentially useful in establishing hydrochemical facies distribution and may provide a better tool than HCA for clustering large data sets when overlapping or continuous clusters exist. Plotting the cluster membership value contours on a map demonstrated the existence of three spatially continuous, well-defined clusters of groundwater samples. The results showed that the FCM method is more sound for investigating threshold data rather than HCA method (that represents sharp and abrupt variations). Manuscript profile -
Open Access Article
11 - Hybrid fuzzy c-means clustering algorithm and multilayer perceptron for increasing the estimate accuracy of the geochemical element concentration case study: eastern zone of porphyry copper deposit of Sonajil
Moharam Jahangiri SeydReza Ghavami Behzad tokhmechiPattern recognition methods are able to identify the hidden relationships between exploration data, especially in the case of limited number of data. The geochemical distribution patterns of the elements are identified and generalized using these methods. Multilayer per MorePattern recognition methods are able to identify the hidden relationships between exploration data, especially in the case of limited number of data. The geochemical distribution patterns of the elements are identified and generalized using these methods. Multilayer perceptron, MLP, is one of the pattern recognition methods which is used for the estimation of geochemical element concentrations in mineral deposit studies. In the current study, multilayer neural network was used to estimate the concentration of geochemical elements based on 1755 surface and borehole samples, analyzed by ICP. Fuzzy c-means, FCM, clustering algorithm was used to increase the neural network estimation accuracy. The optimal number of clusters in the dataset was identified by validation indices and was used to design estimator. The clustering data on average showed an increase of 13% accuracy compared to normal mode. The average accuracy was increased from 75 percent to 88 percent. Elements with the lowest estimation accuracy showed an acceptable increase on the estimation accuracy by using clustering data. Mean squared error was 0.079 using all data and decreased to 0.025 while using hybrid developed method. Manuscript profile -
Open Access Article
12 - Geomechanical zonation of the Faraghan, Zakeen and Sarchahan formations based on incorporation of rock mechanical and sedimentological studies, in one of the Persian Gulf oil fields
Mehran Kalhori Hooshang Mehrabi Ebrahim Sefidari Hasan Eshraghi Behnam Vaez LivariGeomechanical studies have important applications in various topics such as wellbore stability, well completion, well orientation, hydraulic fracturing plans and operations, sand production and hydrocarbon fields subsidence. It is necessary to prepare earth mechanical m MoreGeomechanical studies have important applications in various topics such as wellbore stability, well completion, well orientation, hydraulic fracturing plans and operations, sand production and hydrocarbon fields subsidence. It is necessary to prepare earth mechanical model of the well in the field. In addition, one of the necessary subjects to prepare mechanical earth model (MEM) is providing continuous rock mechanical parameters in the well. Rock mechanical parameters change by any variation in lithology. In this study, rock mechanical parameters are provided in continuous form, for Faraghan, Zakeen and Sarchahan formations for a field in the Persian Gulf and these parameters are clustered. Clustering resulted in recognizing six clusters with various rock mechanical characteristics. Petrographic study (i.e. determining facies, cementation and diagenesis) recognized five facies with different petrographic and cementation characteristics. These facies include quartz arenite and arkosic sandstones, shales, red mudstone and carbonates. A correlation between sedimentary and geomechanical facies was found. According to petrographic and geomechanical studies, the studied interval was divided into 7 sections. Accordingly, variation of rock mechanical parameters with regard to change in lithology was investigated. In addition, the impact of rock composition, cementation and compaction changes on rock mechanical parameters were evaluated as well. Manuscript profile -
Open Access Article
13 - Referral Traffic Analysis: A Case Study of the Iranian Students' News Agency (ISNA)
Roya Hassanian Esfahani Mohammad Javad KargarWeb traffic analysis is a well-known e-marketing activity. Today most of the news agencies have entered the web providing a variety of online services to their customers. The number of online news consumers is also increasing dramatically all over the world. A news webs MoreWeb traffic analysis is a well-known e-marketing activity. Today most of the news agencies have entered the web providing a variety of online services to their customers. The number of online news consumers is also increasing dramatically all over the world. A news website usually benefits from different acquisition channels including organic search services, paid search services, referral links, direct hits, links from online social media, and e-mails. This article presents the results of an empirical study of analyzing referral traffic of a news website through data mining techniques. Main methods include correlation analysis, outlier detection, clustering, and model performance evaluation. The results decline any significant relationship between the amount of referral traffic coming from a referrer website and the website's popularity state. Furthermore, the referrer websites of the study fit into three clusters applying K-means Squared Euclidean Distance clustering algorithm. Performance evaluations assure the significance of the model. Also, among detected clusters, the most populated one has labeled as "Automatic News Aggregator Websites" by the experts. The findings of the study help to have a better understanding of the different referring behaviors, which form around 15% of the overall traffic of Iranian Students' News Agency (ISNA) website. They are also helpful to develop more efficient online marketing plans, business alliances, and corporate strategies. Manuscript profile -
Open Access Article
14 - A Study on Clustering for Clustering Based Image De-noising
Hossein Bakhshi Golestani Mohsen Joneidi Mostafa SadeghiIn this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of i MoreIn this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. In the present paper, we suggest a method based on global clustering of image constructing blocks. As the type of clustering plays an important role in clustering-based de-noising methods, we address two questions about the clustering. The first, which parts of the data should be considered for clustering? The second, what data clustering method is suitable for de-noising? Then clustering is exploited to learn an over complete dictionary. By obtaining sparse decomposition of the noisy image blocks in terms of the dictionary atoms, the de-noised version is achieved. Experimental results show that our dictionary learning framework outperforms its competitors in terms of de-noising performance and execution time. Manuscript profile -
Open Access Article
15 - Scalable Community Detection through Content and Link Analysis in Social Networks
Zahra Arefian Mohammad Reza Khayyam BashiSocial network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. MoreSocial network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. Using features that are available in the social networks, first discovering a complete and comprehensive communication should be done. Many methods have been proposed to explore the community, which are community detections through link analysis and nodes content. Most of the research exploring the social communication network only focuses on the one method, while attention to only one of the methods would be a confusion and incomplete exploration. Community detections is generally associated with graph clustering, most clustering methods rely on analyzing links, and no attention to regarding the content that improves the clustering quality. In this paper, to scalable community detections, an integral algorithm is proposed to cluster graphs according to link structure and nodes content, and it aims finding clusters in the groups with similar features. To implement the Integral Algorithm, first a graph is weighted by the algorithm according to the node content, and then network graph is analyzed using Markov Clustering Algorithm, in other word, strong relationships are distinguished from weak ones. Markov Clustering Algorithm is proposed as a Multi-Level one to be scalable. The proposed Integral Algorithm was tested on real datasets, and the effectiveness of the proposed method is evaluated. Manuscript profile -
Open Access Article
16 - On-road Vehicle detection based on hierarchical clustering using adaptive vehicle localization
Moslem Mohammadi Jenghara Hossein Ebrahimpour KomlehVehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Im MoreVehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Image processing techniques can overcome these problems.This paper introduces a method based on hierarchical clustering using low-level image features for on-road vehicle detection. Each vehicle assumed as a cluster. In traditional clustering methods, the threshold distance for each cluster is fixed, but in this paper, the adaptive threshold varies according to the position of each cluster. The threshold measure is computed with bivariate normal distribution. Sampling and teammate selection for each cluster is applied by the members-based weighted average. For this purpose, unlike other methods that use only horizontal or vertical lines, a fully edge detection algorithm was utilized. Corner is an important feature of video images that commonly were used in vehicle detection systems. In this paper, Harris features are applied to detect the corners. LISA data set is used to evaluate the proposed method. Several experiments are applied to investigate the performance of proposed algorithm. Experimental results show good performance compared to other algorithms . Manuscript profile -
Open Access Article
17 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
Hamideh Sadat Cheraghchi Ali ZakerolhossieniThe problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social MoreThe problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript profile -
Open Access Article
18 - Coverage Improving with Energy Efficient in Wireless Sensor Networks
Amir Pakmehr Ali GhaffariWireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service qua MoreWireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service quality in WSNs. In most of the applications, the sensor nodes are scattered in the environment randomly that causes the density of the nodes to be high in some regions and low in some other regions. In this case, some regions are not covered with any nodes of the network that are called covering holes. Moreover, creating some regions with high density causes extra overlapping and consequently the consumption of energy increases in the network and life of the network decreases. The proposed approach causes an increase in life of the network and an increase in it through careful selection of the most appropriate approach as cluster head node and form clusters with a maximum length of two steps and selecting some nodes as redundancy nodes in order to cover the created holes in the network. The proposed scheme is simulated using MATLAB software. The function of the suggested approach will be compared with Learning Automata based Energy Efficient Coverage protocol (LAEEC) approach either. Simulation results shows that the function of the suggested approach is better than LAEEC considering the parameters such as average of the active nodes, average remaining energy in nodes, percent of network coverage and number of control packets. Manuscript profile -
Open Access Article
19 - Preserving Data Clustering with Expectation Maximization Algorithm
Leila Jafar Tafreshi Farzin YaghmaeeData mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of data mining techniques can also result in new threats to privacy and informatio MoreData mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of data mining techniques can also result in new threats to privacy and information security. Therefore, a new class of data mining methods called privacy preserving data mining (PPDM) has been developed. The aim of researches in this field is to develop techniques those could be applied to databases without violating the privacy of individuals. In this work we introduce a new approach to preserve sensitive information in databases with both numerical and categorical attributes using fuzzy logic. We map a database into a new one that conceals private information while preserving mining benefits. In our proposed method, we use fuzzy membership functions (MFs) such as Gaussian, P-shaped, Sigmoid, S-shaped and Z-shaped for private data. Then we cluster modified datasets by Expectation Maximization (EM) algorithm. Our experimental results show that using fuzzy logic for preserving data privacy guarantees valid data clustering results while protecting sensitive information. The accuracy of the clustering algorithm using fuzzy data is approximately equivalent to original data and is better than the state of the art methods in this field. Manuscript profile -
Open Access Article
20 - A RFMV Model and Customer Segmentation Based on Variety of Products
Saman Qadaki Moghaddam Neda Abdolvand Saeedeh Rajaee HarandiToday, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major change MoreToday, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major changes in the ability of organizations to collect, store and analyze macro-data. Therefore, over thousands of data can be stored for each customer. Hence, customer satisfaction is one of the most important organizational goals. Since all customers do not represent the same profitability to an organization, understanding and identifying the valuable customers has become the most important organizational challenge. Thus, understanding customers’ behavioral variables and categorizing customers based on these characteristics could provide better insight that will help business owners and industries to adopt appropriate marketing strategies such as up-selling and cross-selling. The use of these strategies is based on a fundamental variable, variety of products. Diversity in individual consumption may lead to increased demand for variety of products; therefore, variety of products can be used, along with other behavioral variables, to better understand and categorize customers’ behavior. Given the importance of the variety of products as one of the main parameters of assessing customer behavior, studying this factor in the field of business-to-business (B2B) communication represents a vital new approach. Hence, this study aims to cluster customers based on a developed RFM model, namely RFMV, by adding a variable of variety of products (V). Therefore, CRISP-DM and K-means algorithm was used for clustering. The results of the study indicated that the variable V, variety of products, is effective in calculating customers’ value. Moreover, the results indicated the better customers clustering and valuation by using the RFMV model. As a whole, the results of modeling indicate that the variety of products along with other behavioral variables provide more accurate clustering than RFM model. Manuscript profile -
Open Access Article
21 - Clustering for Reduction of Energy Consumption in Wireless Sensor Networks by AHP Method
Mohammad Reza Taghva Robab Hamlbarani Haghi Aziz Hanifi Kamran feiziDue to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of t MoreDue to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of these networks. Due to the energy limitation of nodes, energy efficiency is considered as a key objective in the design of sensor networks. In this paper, we present a method in which, in the first phase, nodes obtain their position by using the position of the base station and two other two nodes informed geographic position and are out of covered environment. In the second phase, the optimal location of the base station is determined. In the third phase, we determine the cluster heads based on the criteria such as the remaining energy, the distance (the distance from the cluster head and the distance from the base station), the number of neighbors (the one-step neighbors and the two-step neighbors) and the centrality. Using the multi-as criteria to select optimally cluster heads by decision making method. We implement the proposed method in the NS2 environment and evaluate its effect and compare it with the NEECP E-LEACH protocols. Simulation results show that by reducing energy consumption, the proposed method enhances the network life time expectancy. In addition it improves average packet delivery and the average delay. Manuscript profile -
Open Access Article
22 - 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
23 - Density Measure in Context Clustering for Distributional Semantics of Word Sense Induction
Masood GhayoomiWord Sense Induction (WSI) aims at inducing word senses from data without using a prior knowledge. Utilizing no labeled data motivated researchers to use clustering techniques for this task. There exist two types of clustering algorithm: parametric or non-parametric. Al MoreWord Sense Induction (WSI) aims at inducing word senses from data without using a prior knowledge. Utilizing no labeled data motivated researchers to use clustering techniques for this task. There exist two types of clustering algorithm: parametric or non-parametric. Although non-parametric clustering algorithms are more suitable for inducing word senses, their shortcomings make them useless. Meanwhile, parametric clustering algorithms show competitive results, but they suffer from a major problem that is requiring to set a predefined fixed number of clusters in advance. The main contribution of this paper is to show that utilizing the silhouette score normally used as an internal evaluation metric to measure the clusters’ density in a parametric clustering algorithm, such as K-means, in the WSI task captures words’ senses better than the state-of-the-art models. To this end, word embedding approach is utilized to represent words’ contextual information as vectors. To capture the context in the vectors, we propose two modes of experiments: either using the whole sentence, or limited number of surrounding words in the local context of the target word to build the vectors. The experimental results based on V-measure evaluation metric show that the two modes of our proposed model beat the state-of-the-art models by 4.48% and 5.39% improvement. Moreover, the average number of clusters and the maximum number of clusters in the outputs of our proposed models are relatively equal to the gold data Manuscript profile -
Open Access Article
24 - Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features
Elham Esmaeeli-Gohari Sajjad ZarifzadehQuery recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the MoreQuery recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e.g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e.g., by more than 20% in terms of p@10, compared with some well-known algorithms. Manuscript profile -
Open Access Article
25 - 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
26 - Energy Efficient Cross Layer MAC Protocol for Wireless Sensor Networks in Remote Area Monitoring Applications
R Rathna L Mary Gladence J Sybi Cynthia V Maria AnuSensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless d MoreSensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications. Manuscript profile -
Open Access Article
27 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
Mohammad Pouya Salvati Jamshid Bagherzadeh Mohasefi Sadegh SulaimanyLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj MoreLink prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript profile -
Open Access Article
28 - Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms
Mohammad sedighimanesh Hessam Zandhessami Mahmood Alborzi Mohammadsadegh KhayyatianEnergy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The mos MoreEnergy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks. Manuscript profile -
Open Access Article
29 - Optimal Clustering-based Routing Protocol Using Self-Adaptive Multi-Objective TLBO For Wireless Sensor Network
Ali Sedighimanesh Hessam Zandhessami Mahmood Alborzi Mohammadsadegh KhayyatianWireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to a MoreWireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to apply some methods to reduce the energy consumption of sensors. The use of clustering-based routing is a method that reduces the energy consumption of sensors. In the present article, the Self-Adaptive Multi-objective TLBO (SAMTLBO) algorithm is applied to select the optimal cluster headers. After this process, the sensors become the closest components to cluster headers and send the data to their cluster headers. Cluster headers receive, aggregate, and send data to the sink in multiple steps using the TLBO-TS hybrid algorithm that reduces the energy consumption of the cluster heads when sending data to the sink and, ultimately, an increase in the wireless sensor network’s lifetime. The simulation results indicate that our proposed protocol (OCRP) show better performance by 35%, 17%, and 12% compared to ALSPR, CRPD, and COARP algorithms, respectively. Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime. Manuscript profile -
Open Access Article
30 - Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT
Mohammad sedighimanesh Hessam Zandhessami Mahmood Alborzi Mohammadsadegh KhayyatianBackground: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The p MoreBackground: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The present article's main objective is to assist in improving energy consumption in the sensor-based IoT network and thus increase the network’s lifetime. Cluster heads are used to send data to the base station. Methods: In the present paper, the type-1 fuzzy algorithm is employed to select cluster heads, and the type-2 fuzzy algorithm is used for routing between cluster heads to the base station. After selecting the cluster head using the type-1 fuzzy algorithm, the normal nodes become the members of the cluster heads and send their data to the cluster head, and then the cluster heads transfer the collected data to the main station through the path which has been determined by the type-2 fuzzy algorithm. Results: The proposed algorithm was implemented using MATLAB simulator and compared with LEACH, DEC, and DEEC protocols. The simulation results suggest that the proposed protocol among the mentioned algorithms increases the network’s lifetime in homogeneous and heterogeneous environments. Conclusion: Due to the energy limitation in sensor-based IoT networks and the impossibility of recharging the sensors in most applications, the use of computational intelligence techniques in the design and implementation of these algorithms considerably contributes to the reduction of energy consumption and ultimately the increase in network’s lifetime. Manuscript profile -
Open Access Article
31 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
Ali Ghaffari Seyyed Keyvan MousaviNetwork coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d MoreNetwork coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript profile -
Open Access Article
32 - Word Sense Induction in Persian and English: A Comparative Study
Masood GhayoomiWords in the natural language have forms and meanings, and there might not always be a one-to-one match between them. This property of the language causes words to have more than one meaning; as a result, a text processing system faces challenges to determine the precis MoreWords in the natural language have forms and meanings, and there might not always be a one-to-one match between them. This property of the language causes words to have more than one meaning; as a result, a text processing system faces challenges to determine the precise meaning of the target word in a sentence. Using lexical resources or lexical databases, such as WordNet, might be a help, but due to their manual development, they become outdated by passage of time and language change. Moreover, the lexical resources might be domain dependent which are unusable for open domain natural language processing tasks. These drawbacks are a strong motivation to use unsupervised machine learning approaches to induce word senses from the natural data. To reach the goal, the clustering approach can be utilized such that each cluster resembles a sense. In this paper, we study the performance of a word sense induction model by using three variables: a) the target language: in our experiments, we run the induction process on Persian and English; b) the type of the clustering algorithm: both parametric clustering algorithms, including hierarchical and partitioning, and non-parametric clustering algorithms, including probabilistic and density-based, are utilized to induce senses; c) the context of the target words to capture the information in vectors created for clustering: for the input of the clustering algorithms, the vectors are created either based on the whole sentence in which the target word is located; or based on the limited surrounding words of the target word. We evaluate the clustering performance externally. Moreover, we introduce a normalized, joint evaluation metric to compare the models. The experimental results for both Persian and English test data showed that the window-based partitioningK-means algorithm obtained the best performance. Manuscript profile -
Open Access Article
33 - Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine
Masoumeh Rezaei mansoureh rezaei Masoud RezaeiForeground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., MoreForeground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI. Manuscript profile -
Open Access Article
34 - Identify and Clustering Challenges of knowledge-based Enterprises using ANN and BPMS Approaches; Case study: Yazd KBEs
Mojtaba GholiPour Mohammad Ali Vahdat Zad Mohammad Saleh Oliua Hasan Khademi ZareuKnowledge always is a powerful tool in stabilizing position of individual/community service to the public and excellence approach in current autonomous communities. Value of knowledge has been more necessary if it capable for transfer to the High-Tec and needed Technolo MoreKnowledge always is a powerful tool in stabilizing position of individual/community service to the public and excellence approach in current autonomous communities. Value of knowledge has been more necessary if it capable for transfer to the High-Tec and needed Technologies of humanity societies. Knowledge Based Enterprise (KBE) is a real-law enterprise such as factory that transfer Knowledge to production/services. However KBEs are causing for sustainable knowledge economy and development native knowledge in more countries, but these enterprises havnt optimize occasion in view of quantity, production quality and service extensive according to the 20 years growth view of Iran. Purpose of this study is to identifying encounter challenges of KBEs that located on Yazds Science and Technology Park (STP) and clustering these challenges with ANN method exactly. The Samples contains 137 person such as manager and top employees of these enterprises. Number of reached challenges have been 59 that were attained from literature and experts guidelines were designed and distributed between samples suddenly. According to the PB artificial neural network, reliabilities of samples were confirmed with MSE=2.0332 and priority done with Multilayer Perceptron (MLP) artificial neural network and with inspiration of Business Process Management System (BPMS) approach. According to the BPMS approach and MLP method, Result show that challenges did cluster in three factions known as: management activities, operational activities and support activities. Thus, number of management, operational and support activities in order were 27, 15 and 17 items exactly. Manuscript profile -
Open Access Article
35 - Clasification of custumers of internet services, using data mining algorithms
farid norozi hamed kazemipoorNowadays, the role of customers has shifted from followers of the producers to guiding them.That is why the classification of customers in targeting and customizing services and prioritization of companies products on the basis of profitability makes great help. Interne MoreNowadays, the role of customers has shifted from followers of the producers to guiding them.That is why the classification of customers in targeting and customizing services and prioritization of companies products on the basis of profitability makes great help. Internet service providers in the market, are competing with a lot of competitors because investing in on communications and internet services are profitable.In order to progress in the market, it is necessary to offer new services and innovation.To get broader share of the market, the internet providers has got to have sufficient knowledge of the market and customers, retain existing customers and attract new customers.By classifying and clustering its customers, and while identifying and supporting its active and beneficial customers, such companies can remove their offbeat customers from services providing cycle.Using data mining algorithm, this research detecting and identifying of such customers, make the internet service provider closer to their goals. Manuscript profile -
Open Access Article
36 - Evaluation of the Efficacy of the Combined Viral Marketing Method with the Network Clustering Method and Comparing the Results
fereydoun ohadi mehrnoosh mohammadi Mohammad Jafar TarokhIn a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketi MoreIn a Competitive Market, Understanding Customer Demand and Effective Advertising is one of the most Important Factors in Survival. Extend the Internet and virtual networks have provided a great opportunity for companies to advertise, and thus studying electronic marketing methods and models is of great importance. One of the newest marketing methods is viral marketing that is based on mouth-to-mouth advertising and has a lot of power. Viral marketing relies on the principle that on any social network, a number of users have high power and influence on others, and by identifying them and creating good advertising messages, They can be used to effectively marketing. Therefore, The identification of important users is considered the most important activity in viral marketing. In this regard, various studies have been conducted to identify users using a variety of graph-based and publish-based methods. In this research, the capabilities of both methods have been used and by Using a semi-localized centrality criterion based on graph-based methods and Markov clustering model based on propagation methods, a new hybrid model for user clustering and identification of key users presented. The results show higher correlation between the proposed method and the SIR standard and, therefore, its higher efficiency than other methods used in the research. Manuscript profile -
Open Access Article
37 - Dynamic Tree- Based Routing: Applied in Wireless Sensor Network and IOT
Mehdi KhazaeiThe Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some app MoreThe Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some applications, the IOT is converted into WSN with the same descriptions and limitations. Working with WSN is limited to energy, memory and computational ability of the sensor nodes. This makes the energy consumption to be wise if protection of network reliability is sought. The newly developed and effective hierarchical and clustering techniques are to overcome these limitations. The method proposed in this article, regarding energy consumption reduction is tree-based hierarchical technique, used clustering based on dynamic structure. In this method, the location-based and time-based properties of the sensor nodes are applied leading to provision of a greedy method as to form the subtree leaves. The rest of the tree structure up to the root, would be formed by applying the centrality concept in the network theory by the base station. The simulation reveals that the scalability and fairness parameter in energy consumption compare to the similar method has improved, thus, prolonged network lifetime and reliability. Manuscript profile -
Open Access Article
38 - Providing a New Method for Customer Satisfaction Data Analysis (Case Study: Automotive Industry: Logan Car)
mahnaz ebrahimi sadr abadi ali mohammad kimiagari seyed mahdi seyed esfahaniThis research aims to provide a hybrid model based on econometrics and clustering to analyze customer satisfaction data. The statistical population of the research,Logan car owners and a sample of 177 customers from Pars Khodro agencies. The researcher, along with ISQI' MoreThis research aims to provide a hybrid model based on econometrics and clustering to analyze customer satisfaction data. The statistical population of the research,Logan car owners and a sample of 177 customers from Pars Khodro agencies. The researcher, along with ISQI's research team, identified the current needs of customers of all vehicles from post-sales services and by designing a customer satisfaction questionnaire, the effect of each variable on the overall satisfaction of customers according to the high-income group, middle income and low income is measured. Factors affecting overall customer satisfaction based on all income groups include six variables: Provision of parts on time; Description presented when vehicle clearance; Ease of access to dealers; periodic service quality; Quality of repairs; Cost of payment and in the high-income group, three variables are described: Cost of payment; Record mentioned Items by the receptionist at the time of admission; Description presented when vehicle clearance and In the middle income group, four variables are described: Provision of parts on time; Quality of repairs; Description presented when vehicle clearance; Cost of payment and and in the low-income group, it includes four variables: Description presented when vehicle clearance; Provision of parts on time; Quality of repairs; Ease of access to dealers. To run this research of software EViews and Spss is used. Manuscript profile -
Open Access Article
39 - Developing a hybrid model to clustering Tehran Stock Exchange companies using meta-heuristic algorithms
Ali MohagharInvestment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Acco MoreInvestment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Accordingly, today, there are many ways to analyze the data from this company. One of the ways is clustering that classification of the companies. However, the present study aimed to identify and distinguish successful from unsuccessful companies in Tehran Stock Exchange has been done using K-means clustering. Then this problem is solved using meta-heuristic algorithms. The results indicate that meta-heuristic algorithms compared with conventional methods, more efficient and have led to a global optimum. Also these results of Altman’s bankruptcy model were confirmed results of meta-heuristic algorithms. Manuscript profile -
Open Access Article
40 - Introducing a new optimal energy method for targets tracking in wireless sensor network using a hunting search algorithm
Shayesteh Tabatabaei Hassan Nosrati NahookIn this paper, in order to increase the accuracy of target tracking, it tries to reduce the energy consumption of sensors with a new algorithm for tracking distributed targets called hunting search algorithm. The proposed method is compared with the DCRRP protocol and t MoreIn this paper, in order to increase the accuracy of target tracking, it tries to reduce the energy consumption of sensors with a new algorithm for tracking distributed targets called hunting search algorithm. The proposed method is compared with the DCRRP protocol and the NODIC protocol, which uses the OPNET simulator version 11.5 to test the performance of these algorithms. The simulation results show that the proposed algorithm performs better than the other two protocols in terms of energy consumption, healthy delivery rate and throughput rate. Manuscript profile -
Open Access Article
41 - A Two Step Method for the Recognition of Printed Subwords
E. Kabir A. ebrahimiIn this paper a two step method for the recognition of printed subwords is proposed. Using characteristic loci features, the set of printed subwords are clustered into 300 clusters by k-means algorithm. Each cluster is represented by its mean. In the first step, each in MoreIn this paper a two step method for the recognition of printed subwords is proposed. Using characteristic loci features, the set of printed subwords are clustered into 300 clusters by k-means algorithm. Each cluster is represented by its mean. In the first step, each input is classified into 300 categories by minimum Euclidian distance from the cluster centers, and 10 closest clusters are found. In the second step, Fourier descriptors of the subword contour are used to classify the input subword into the members of these 10 clusters. The training set consists of 12700 Farsi subwords in 4 different fonts, Lotus, Mitra, Yagut and Zar, and 3 sizes of 10, 12 and 14. In a test, a set of 500 subwords was used. Considering the first class, top five and top ten classes, 71.4%, 95%, and 98.2% of these subwords were correctly classified. In the post processing, dots of the subword and their positions were used to improve the recognition results. This improved the recognition rate to 92.6%. Manuscript profile -
Open Access Article
42 - Extraction and Modeling Context Dependent Phone Units for Improvement of Continuous Speech Recognition Accuracy by Phonemes Clustering
Mohammad Bahrani H. SametiThis paper proposes a proper context dependent method for improving the accuracy of a Persian continuous speech recognition system. Due to some constraints in speech recognition system, the multiple phone units approach is utilized for extracting context dependent phone MoreThis paper proposes a proper context dependent method for improving the accuracy of a Persian continuous speech recognition system. Due to some constraints in speech recognition system, the multiple phone units approach is utilized for extracting context dependent phone units. In this approach, each phoneme is clustered to some phoneme variations, and then each phoneme variation is modeled separately. Unsupervised phoneme clustering is done using k-means clustering algorithm. The new effective method is proposed for calculating the centroid of clusters. The proper number of cluster for each phoneme is determined according to amount of training data for that phoneme and recognition accuracy of that phoneme using context independent models. The number of clusters is then optimized by try and error methods. Then each cluster is modeled as a context dependent phone unit. The reduction in word error rate is about 22% using these models. Manuscript profile -
Open Access Article
43 - Novel Automatic Clustering Technique Based on the Artificial Immune Algorithm
Seyed-Hamid ZahiriIn this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been a MoreIn this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the immune algorithm to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed algorithm (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering system. The method has been tested on the different kinds of the complex artificial data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering algorithm (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic algorithm based clustering technique (as an evolutionary clustering algorithm). Manuscript profile -
Open Access Article
44 - Probabilistic Evaluation of Total Transfer Capability of Transmission Networks in the Presence of Wind Farms
M. Ramezani H. Seifi M. Parsa-Moghaddam, wind farms are used to generate electric power in some parts of the world. With increasing penetration level of wind farms in electric power systems, modification of current tools to evaluate and manage the system is an important issue. Evaluation of total transfer ca More, wind farms are used to generate electric power in some parts of the world. With increasing penetration level of wind farms in electric power systems, modification of current tools to evaluate and manage the system is an important issue. Evaluation of total transfer capability (TTC) is one of the considerable tools in restructured power systems which is used to schedule future transactions between areas in multi area power systems to ensure security of network. In this paper, a method is proposed for probabilistic evaluation of TTC of multi area power systems in the presence of wind farms. Firstly, a general approach based on Monte Carlo simulation is used to simulate a system state considering system load and power output of wind farm and optimal power flow (OPF) is used to calculate TTC level for each state. Then risk analysis is used as a decision making tool to determine the appropriate TTC level for a fixed system load level. Finally, both of system load and power output of wind farm are considered and clustered input data are used to accelerate Monte Carlo convergence speed. To demonstrate the effectiveness of the proposed approaches IEEE-RTS is used. Manuscript profile -
Open Access Article
45 - Evaluating Two Approaches for Farsi OCR Based on Sub-Word Shape Recognition
H. Khosravi E. KabirTwo approaches for the recognition of printed Farsi documents based on sub-word shape recognition is proposed. First approach is based on recognition of sub-word shape as a whole and the second is based on the recognition of the body of sub-words. Sub-word body is const MoreTwo approaches for the recognition of printed Farsi documents based on sub-word shape recognition is proposed. First approach is based on recognition of sub-word shape as a whole and the second is based on the recognition of the body of sub-words. Sub-word body is constructed via removing dots and signs of the sub word. In second approach, information of dots and signs will be added after recognition of the body. Both approaches have two phases: training and test. In training phase, sub-words are clustered based on ISODATA algorithm. Initial centers of the clusters are computed through a hierarchical clustering algorithm. In first approach, sub-word recognition is performed in two stages: finding clusters close to the input sub-word and then finding the best match within the sub-words of these clusters. In the second approach another stage is required to find the final sub-word including dots and signs. Experimental results show that on clean images the first algorithm have better performance; 94% versus 93% in word level. But when dealing with low quality and noisy images, both algorithms are suffering from reduced accuracy. Sometimes this reduction is significant. The reasons of this behavior are inspected and some solutions are presented. Finally we compared both methods and inspected pros and cons of Farsi OCR based on sub-word shape. Manuscript profile -
Open Access Article
46 - A Method for Automatic Printing Carpet Map Reading and Comparing to C-Means Clustering
Ahmad Izadipour E. KabirThe subject of this paper is to read carpet pattern automatically by computer. This is composed of two steps: detection of vertical and horizontal lines in the pattern and color reduction. Color reduction is essential because of limitation of the number of colors that i MoreThe subject of this paper is to read carpet pattern automatically by computer. This is composed of two steps: detection of vertical and horizontal lines in the pattern and color reduction. Color reduction is essential because of limitation of the number of colors that is used in a carpet. To accomplish of this process, we must detect the grid lines on the carpet pattern automatically. These lines are two types: thin lines and thick lines. At the first stage, the distance between thin lines is obtained. Having the first thin line detected, the other thin lines are drawn using this distance. We use a Comb method for detection of thick lines. The major problem in line detection is lagging or leading of the lines due to the mismatch between sampling frequency of the scanner and image resolution. We compensate this distortion in various steps in our algorithm. In the second step, we want all the pixels in the same square, to have the same color. This is obtained by mapping colors to the best color in the palette. We propose three methods. In first method the user selects two selections per any colors. Palette is obtained from some processes in these selections. Those pixels that are in the middle of the squares are mapped to the palette. Then color histogram is computed. The color that has the maximum histogram value is assigned to the square. In order to decrease user’s interference, C-means clustering algorithm is used in two types. The centers of initial clusters are determined once with user’s interference and once randomly. Results of these three methods are compared. We tested our methods on 20 samples of carpet patterns, and the error rate was variable from 0.07% to 0.5% between samples. Manuscript profile -
Open Access Article
47 - 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
48 - A New Method for Clustering Wind Speed Data in Wind Power Plants Using FCM and PSO Algorithms
H. Afrakhte Y. Bostani AmlashiFuzzy clustering Method (FCM) is a commonly used method of data clustering. But, when too much data are available, the use of this method usually may lead to non-homogeneous distribution of data. In this paper a new method for clustering of wind speed data in wind farms MoreFuzzy clustering Method (FCM) is a commonly used method of data clustering. But, when too much data are available, the use of this method usually may lead to non-homogeneous distribution of data. In this paper a new method for clustering of wind speed data in wind farms is presented. In this method, using the PSO algorithm, wind speed data is clustered and the obtained results are compared with those of FCM and K-means clustering methods. Simulation results indicate the proposed method has better convergence than K-means and FCM methods, especially in conditions which too much data are not available. Manuscript profile -
Open Access Article
49 - Accuracy and Speed Performance Improvement in Speaker Verification Using Genetic Programming
S. S. Sadat Sadidpour M. M. Homayounpour M. FasanghariIn speaker verification, a system investigates a person's identity and decides whether the person is a true client or an imposter. In this paper, genetic programming (GP) is used as a method for speaker modeling. When GP is used for construction of models for speakers, MoreIn speaker verification, a system investigates a person's identity and decides whether the person is a true client or an imposter. In this paper, genetic programming (GP) is used as a method for speaker modeling. When GP is used for construction of models for speakers, due to long training time to train GP models, training data compression is proposed in this paper. This idea reduced training time for 20 times. Training of several GP trees as a speaker's model is another idea presented in this paper to improve the speaker verification performance. In this method, training data are separated to a few clusters. Then a GP tree is trained for each cluster. Therefore, a speaker is modeled by several genetic programming trees. The verification performance increased from 50% to about 92% using the proposed method. Genetic programming performance was compared to some other discriminative methods such as Multi-Layer Perceptron neural network and Learning Vector quantization, and generative methods such as K-Means, GMM and LBG, GMM-UBM and VQ-MAP. Experiments show that Genetic programming is more effective than the other methods. Manuscript profile -
Open Access Article
50 - Segmentation of Steel Surfaces towards Defect Detection Using New Gabor Composition Method
S. J. Alemasoom A. Monadjemi H. A. AlemasoomThe images of steel surfaces are generally textural images. There are different texture analysis methods to extract features from these images. In those methods using multi-scale/multi-directional analysis, Gabor filters are used for feature extraction. In this paper, w MoreThe images of steel surfaces are generally textural images. There are different texture analysis methods to extract features from these images. In those methods using multi-scale/multi-directional analysis, Gabor filters are used for feature extraction. In this paper, we extract texture features using the optimum Gabor filter bank. This filter bank is designed in a way that diverse filtering frequency and orientation will allow it to extract considerable amounts of texture information from the input images. We also introduce a new method called Gabor composition for segmentation and defect detection of steel surfaces. In this method, using two different algorithms, the input image is decomposed into detail images using an appropriate Gabor filter bank and then selected detail images are re composed. The created feature map illustrates the defective areas well. By calculating data distribution of detail images and comparing them, the second method of Gabor composition can accomplish segmentation without needing the normal images and the number of detail images to re-compose. Furthermore, we did different tests towards optimizing of segmentation by means of classifiers. Using a K-means classifier and adding gray levels to the extracted features, complete the segmentation procedure. The experimental results show that the Gabor composition method in most of the tests has got better defect detection performance than the ordinary K-means classifier and the standard wavelet method; also the Second method of Gabor composition has got the best performance over all. Manuscript profile -
Open Access Article
51 - Extracting Bottlenecks Using Object Recognition in Reinforcement Learning
B. Ghazanfari N. Mozayani M. R. Jahed MotlaghExtracting bottlenecks improves considerably the speed of learning and the ability knowledge transferring in reinforcement learning. But, extracting bottlenecks is a challenge in reinforcement learning and it typically requires prior knowledge and designer’s help. This MoreExtracting bottlenecks improves considerably the speed of learning and the ability knowledge transferring in reinforcement learning. But, extracting bottlenecks is a challenge in reinforcement learning and it typically requires prior knowledge and designer’s help. This paper will propose a new method that extracts bottlenecks for reinforcement learning agent automatically. We have inspired of biological systems, behavioral analysts and routing animals and the agent works on the basis of its interacting to environment. The agent finds landmarks based in clustering and hierarchical object recognition. If these landmarks in actions space are close to each other, bottlenecks are extracted using the states between them. The Experimental results show a considerable improvement in the process of learning in comparison to some key methods in the literature. Manuscript profile -
Open Access Article
52 - Unsupervised Image Clustering Using Central Force Optimization Algorithm Unsupervised Image Clustering Using Central Force Optimization Algorithm
M. H. Mozafari Maref Seyed-Hamid ZahiriCentral Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In MoreCentral Force Optimization (CFO) is a new member of heuristic algorithms which has been recently proposed and added to swarm intelligence algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In the presented method, each probe includes the information of center of the clusters, and fitness function contains both inter-distance and intra-distance of the samples. Extensive experimental results show that the proposed CFO-clustering outperforms other similar clustering algorithms which were designed based on the evolutionary techniques. Manuscript profile -
Open Access Article
53 - Sub-Word Image Clustering in Old Printed Documents Using Template Matching
M. R. Soheili E. KabirDue to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications MoreDue to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications dealing with large uniform documents. We assumed that the whole document is printed in a single font and print quality is not good. To test our method, we created a dataset of all sub-words of a Farsi book. The book has 233 pages with more than 111000 sub-words manually labeled. We use an incremental clustering algorithm. Four simple features are extracted from each sub-word and compared with the corresponding features of each cluster center. If all features' differences lie within certain thresholds, the sub-word and the winner cluster center are finely compared using a template matching algorithm. In our experiments, we show that all sub-words of the book are recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members. Manuscript profile -
Open Access Article
54 - A Hybrid Algorithm for Terrain Simplification
F. Dabaghi Zarandi Mohammad GhodsiTerrain simplification problem is one of fundamental problems in computational geometry and it has many applications in other fields such as geometric information systems, computer graphics, image processing. Terrain is commonly defined by a set of n points in three dim MoreTerrain simplification problem is one of fundamental problems in computational geometry and it has many applications in other fields such as geometric information systems, computer graphics, image processing. Terrain is commonly defined by a set of n points in three dimension space. Major goal of terrain simplification problem is removing some points of one terrain so that maximum error of simplified surface is a certain threshold. There are two optimization goals for this problem: (1) min-k, where for a given error threshold , the goal is to find a simplification with the minimum number of points for which the error is that most , and (2) min-, where for a given number n, the goal is to find a simplification of at most m points that has the minimum simplification error. Simplification problem is NP-hard in optimal case. In this paper we present a hybrid algorithm for terrain simplification that performs in three phases. First, terrain is divided to some clusters, then any cluster is simplified independently and finally, the simplified clusters are merged. Our algorithm solves the problem in . The proposed algorithm is implemented and verified by experiments. Manuscript profile -
Open Access Article
55 - Topology Control in Wireless Sensor Networks Using Two-Level Fuzzy Logic
A. Abdi Seyedkolaei A. ZakerolhosseiniWireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy MoreWireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy consumption and increasing network lifetime. Topology control is one of the methods to reduce energy consumption and increase the lifetime of the network. Since different methods of topology control, to reduce energy consumption and enhance the network lifetime is proposed that including them is the clustering and one of the most famous clustering methods is LEACH. In this paper, we try to present a new clustering method that is superior compared to leach and other improved methods after the LEACH. we use in our clustering method from two-level fuzzy logic that be causing reduce energy consumption and increase the network lifetime compared to other methods and to prove the superiority of our method compared with other methods, we present a comparison using MATLAB software. Manuscript profile -
Open Access Article
56 - Learners Grouping in Adaptive Learning Systems Using Fuzzy Grafting Clustering
M. S. Rezaei Gh. A. MontazerQuality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clusterin MoreQuality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clustering methods by combination of them and improving methods. This work makes the complexity of grouping methods increased and quality of result’s groups decreased. In this paper, new method for selection appropriate clusters based on fuzzy theory is proposed. In this method, each cluster is defined as a fuzzy set and the corresponding clusters are determined. So the best cluster is selected among each corresponding clusters. The results of an empirical evaluation of the proposed method based on two criteria: “Davies-Bouldin” and “Purity and Gathering” indicate that this method has better performance than other clustering methods such as FCM, K-means, hybrid clustering method (HCM), evolutionary fuzzy clustering (EFC) and ART neural network. Manuscript profile -
Open Access Article
57 - Botnet Detection Based on Computing Negative Reputation Score by Use of a Clustering Method and DNS Traffic
R. Sharifnyay Dizboni A. Manafi MurkaniToday, botnets are known as one of the most important threats against Internet infrastructure. A botnet is a network of compromised hosts (bots) remotely controlled by a so-called botmaster through one or more command and control (C&C) servers. Since DNS is one of the m MoreToday, botnets are known as one of the most important threats against Internet infrastructure. A botnet is a network of compromised hosts (bots) remotely controlled by a so-called botmaster through one or more command and control (C&C) servers. Since DNS is one of the most important services on Internet, botmasters use it to resistance their botnet. By use of DNS service, botmasters implement two techniques: IP-flux and domain-flux. These techniques help an attacker to dynamically change C&C server addresses and prevent it from becoming blacklisted. In this paper, we propose a reputation system used a clustering method and DNS traffic for online fluxing botnets detection .we first cluster DNS queries with similar characteristics at the end of each time period. We then identify hosts that generate suspicious domain names and add them to a so-called suspicious group activity matrix. We finally calculate the negative reputation score of each host in the matrix and detect hosts with high negative reputation scores as bot-infected. The experimental results show that it can successfully detect fluxing botnets with a high detection rate and a low false alarm rate. Manuscript profile -
Open Access Article
58 - Classification of Hyperspectral Images Using Cluster Space Linear Discriminant Analysis and Small Training Set
M. Imani H. GhassemianThe hyperspectral images allow us to discriminate between different classes with more details. There are lots of spectral bands in hyperspectral images. On the other hand, the limited number of available training samples causes difficulties in classification of high dim MoreThe hyperspectral images allow us to discriminate between different classes with more details. There are lots of spectral bands in hyperspectral images. On the other hand, the limited number of available training samples causes difficulties in classification of high dimensional data. Since the gathering of training samples is hard and time consuming, feature reduction can considerably improve the performance of classification. So, feature extraction is one of the most important preprocessing steps in analysis and classification of hyperspectral images. Feature extraction methods such as LDA have not good efficiency in small sample size situation. A supervised feature extraction method is proposed in this paper. The proposed method, which is called cluster space linear discriminant analysis (CSLDA), without obtaining the label of testing samples and just with doing a clustering on testing data, finds the relationship between training and testing samples. Then, it uses the power of unlabeled samples together with training samples for estimation of within-class and between-class scatter matrices. The CSLDA improves the classification accuracy particularly in multimodal hyperspectral data. The experimental results on urban and agriculture hyperspectral images show the better performance of CSLDA compared to popular feature extraction methods such as LDA, GDA, and NWFE using limited number of training samples. Manuscript profile -
Open Access Article
59 - 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
60 - Using Contour Information for Body Orientation Estimation in the Image
A. Sebti H. HassanpourPose and orientation of a person relative to the camera are the important and useful information in many applications, including surveillance systems. This information can be used in the behavior analysis of the person. Low quality of the recorded surveillance images, n MorePose and orientation of a person relative to the camera are the important and useful information in many applications, including surveillance systems. This information can be used in the behavior analysis of the person. Low quality of the recorded surveillance images, noisy data and cluttered backgrounds are some of the difficulties in this task. In the existing methods, histogram of orientation gradient (HOG) is used to estimate the orientation. The local properties of HOG is a weakness for orientation estimation. The edge surrounding the object, namely contour, is a useful information for orientation estimation. In this paper we present a general form of a contour. This hyper contour helps us to find the best contour which is matched to image of the person in a hierarchical fashion. These contours generated from a human 3D model. The matched contour as a high-level feature is combined with the low-level feature such as HOG, and considered as the final feature. The proposed feature is a linear combination of several types of contours with respect to different regions of the body. To show the impact of the proposed feature on orientation estimation, a support vector machine is trained on a hybrid feature space and then is evaluated on VIPeR dataset. The experimental results show that the accuracy of the orientation estimation is improved about 4% by using the extended feature. Manuscript profile -
Open Access Article
61 - Automatic Error Detecting in Databases, Based on Clustering and Nearest Neighbor
M. ataeyan n. daneshpourData quality affects on companies decision making, so that decisions based on data without quality incur companies high costs. Data quality has various dimensions and accuracy is the most important of these dimensions. Error detection is needed for data cleaning. Due to MoreData quality affects on companies decision making, so that decisions based on data without quality incur companies high costs. Data quality has various dimensions and accuracy is the most important of these dimensions. Error detection is needed for data cleaning. Due to the huge volume of data, an automatic system is needed to perform this process without user interaction. In this paper an approach is proposed based on k-means clustering for error detection. Firstly data are clustered for each attribute. Then for each data in each cluster a method similar to k-nearest neighbor is used for detecting errors. The proposed method is able to detect multiple errors in one record. Also this approach is able to detect errors in fields with various attribute types. Experimental results show that this approach can detect 91% of errors in data on average. Also the proposed approach is compared with an automatic method which detects errors based on rule in various attribute types. Experimental results show that the proposed approach has on average 25%better performance to detect errors. Manuscript profile -
Open Access Article
62 - Proposing a Density-Based Clustering Algorithm with Ability to Discover Multi-Density Clusters in Spatial Databases
A. Zadedehbalaei A. Bagheri H. AfsharClustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits s MoreClustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits suffers from some issues such as difficulty in determining appropriate values for input parameters and inability to detect clusters with different densities. In this paper, we introduce a new clustering algorithm which unlike DBSCAN algorithm, can detect clusters with different densities. This algorithm also detects nested clusters and clusters sticking together. The idea of the proposed algorithm is as follows. First, we detect the different densities of the dataset by using a technique and Eps parameter is computed for each density. Then DBSCAN algorithm is adapted with the computed parameters to apply on the dataset. The experimental results which are obtained by running the suggested algorithm on standard and synthetic datasets by using well-known clustering assessment criteria are compared to the results of DBSCAN algorithm and some of its variants including VDBSCAN, VMDBSCAN, LDBSCAN, DVBSCAN and MDDBSCAN. All these algorithms have been introduced to solve the problem of multi-density data sets. The results show that the suggested algorithm has higher accuracy and lower error rate in comparison to the other algorithms. Manuscript profile -
Open Access Article
63 - Handover Management between Femtocell and Macrocell Using Geo-Based Spectral Clustering
T. Bahraini M. Zamiri H. Sadoghi YazdiAvailable techniques in handover management in cellular communication networks can’t keep unnecessary events and delay decision at a low level state. The main purpose of this paper is to provide the intelligence method which is able to minimize the number of unnecessary MoreAvailable techniques in handover management in cellular communication networks can’t keep unnecessary events and delay decision at a low level state. The main purpose of this paper is to provide the intelligence method which is able to minimize the number of unnecessary events and allowing the necessary requests to occur and so improves the overall network performance. In order to achieve such a goal, in the proposed method, we have used the geographical knowledge from building maps with spectral clustering in the area covered by femtocell. Therefore, we require to develop the spectral clustering based on geographical information. The experimental results on real dataset and performed simulations indicate that the superiority of the proposed method in allocating the user to appropriate cell and acceptable ability to manage the handover in heterogeneous layer of femtocell-macrocell. Manuscript profile -
Open Access Article
64 - Proposing a New Method for Acquiring Skills in Reinforcement Learning with the Help of Graph Clustering
M. Davoodabadi Farahani N. MozayaniReinforcement learning is atype of machine learning methods in which the agent uses its transactions with the environment to recognize the environment and to improve its behavior.One of the main problems of standard reinforcement learning algorithms like Q-learning is t MoreReinforcement learning is atype of machine learning methods in which the agent uses its transactions with the environment to recognize the environment and to improve its behavior.One of the main problems of standard reinforcement learning algorithms like Q-learning is that they are not able to solve large scale problems in a reasonable time. Acquiring skills helps to decompose the problem to a set of sub-problems and to solve it with hierarchical methods. In spite of the promising results of using skills in hierarchical reinforcement learning, it has been shown in some previous studies that based on the imposed task, the effect of skills on learning performance can be quite positive. On the contrary, if they are not properly selected, they can increase the complexity of problem-solving. Hence, one of the weaknesses of previous methods proposed for automatically acquiring skills is the lack of a systematic evaluation method for each acquired skill. In this paper, we propose new methods based on graph clustering for subgoal extraction and acquisition of skills. Also, we present new criteria for evaluating skills, with the help of which, inappropriate skills for solving the problem are eliminated. Using these methods in a number of experimental environments shows a significant increase in learning speed. Manuscript profile -
Open Access Article
65 - A Distributed Solution for Mixed Big Data Clustering
M. Mahmoudi نگین دانشپورDue to the high-speed of information generation and the need for data-knowledge conversion, there is an increasing need for data mining algorithms. Clustering is one of the data mining techniques, and its development leads to further understanding of the surrounding env MoreDue to the high-speed of information generation and the need for data-knowledge conversion, there is an increasing need for data mining algorithms. Clustering is one of the data mining techniques, and its development leads to further understanding of the surrounding environments. In this paper, a dynamic and scalable solution for clustering mixed big data with a lack of data is presented. In this solution, the integration of common distance metrics with the concept of the closest neighborhood, as well as a kind of geometric coding are used. There is also a way to recover missing data in the dataset. By utilizing parallelization and distribution techniques, multiple nodes can be scalable and accelerated. The evaluation of this solution is based on speed, precision, and memory usage criteria compared to other ones. Manuscript profile -
Open Access Article
66 - Identifying Primary User Emulation Attacks in Cognitive Radio Network Based on Bayesian Nonparametric Bayesian
K. Akbari J. AboueiCognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack MoreCognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack, thus malicious user attempts to send a signal same as primary user's signal to deceive secondary users and prevent them from sending signals in the spectrum holes. Meanwhile, causing traffic in CR network, malicious user obtains a frequency band to send their information. In this thesis, a method to identify primary user emulation attack is proposed. According to this method, primary users and malicious users are distinguished by clustering. In this method, the number of active users is recognized in the CR network by clustering. Indeed, by using Dirichlet process mixture model classification based on the Bayesian Nonparametric method, primary users are clustered. In addition, to achieve higher convergence rate, Chinese restaurant process method to initialize and non-uniform sampling is applied to select clusters parameter. Manuscript profile -
Open Access Article
67 - Improved BIRCH Clustering by Chemical Reaction Optimization Algorithm to Health Fraud Detection
M. Abdolrazzagh-Nezhad M. KheradWith regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are seve MoreWith regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are several fraud detection algorithms. In the paper, the BIRCH clustering algorithm, as one hierarchical clustering algorithm, is hybridized with a chemical reaction optimization algorithm (CRO). The BIRCH with linear time complexity is able for clustering large scale data and identifying their noises and the CRO, as one of new meta-heuristic algorithm inspired by the chemical reactions in the real world, explores the search space with a dynamic population size based on four reactions such as on-wall ineffective collision, decomposition, inter-molecular ineffective collision and synthesis. Due to the improved BIRCH-CRO removes the internal clustering process of the classic BIRCH and determines the optimal values of its main parameters, it causes that the computational time decreases and accuracy and precision of detecting fraud data increase since its experimental results is compared with the exist unsupervised algorithms. Also, the proposed fraud detection algorithm has the ability to perform on online data and large scale data, and given the obtained results, it provides a proper performance. Manuscript profile -
Open Access Article
68 - Propose a New Clustering Algorithm for Data Transmission in Wireless Sensor Networks by Using Apollonius Circle
Sh. Pourbahrami E. Khaledi Alamdari L. Mohammad KhanliWireless sensor networks, as an up-to-date technology, are one of the fastest growing technologies in the world today. Since these networks are used in military and agricultural environments as well as for observation of inaccessible environments, these networks need to MoreWireless sensor networks, as an up-to-date technology, are one of the fastest growing technologies in the world today. Since these networks are used in military and agricultural environments as well as for observation of inaccessible environments, these networks need to be organized to achieve goals such as successful and timely sending of data to the main station. Clustering of wireless sensor networks is one of the most widely used methods for organizing these networks. Various ways to cluster these networks are provided, most of which are aimed at preventing energy loss and increasing the lifetime of sensor nodes. The thesis attempts to present a new geometric method for clustering the nodes of wireless sensor networks. In this geometric method, Apollonius circle is used to draw the abstract shape of the clusters and to assemble the nodes around the cluster head. Due to the high accuracy that it has in determining the fit of node distances, this circle can accurately assign nodes to cluster heads and prevent large single-node clusters or faraway nodes. In this algorithm, a main station, a number of nodes are used as a cluster header and a number of nodes as routers. The goal is to find the most accurate cluster heads and create clusters of high coverage in the network. The proposed method is implemented in MATLAB software and comparison of the results obtained from the view of successful data transmission, clustering accuracy, network lifetime and number of coverage areas, is showing accuracy of this method compared to optimal Leach algorithms and K-means presented in this field. Manuscript profile -
Open Access Article
69 - Anomaly Detection in the Car Trajectories Using Sparse Reconstruction
Reyhane Taghizade Abbas Ebrahimi moghadam M. KhademiIn traffic control and vehicle registration systems a big challenge is achieving a system that automatically detects abnormal driving behavior. In this paper a system for detection of vehicle anomalies proposed, which at first extracts spatio-temporal features form clus MoreIn traffic control and vehicle registration systems a big challenge is achieving a system that automatically detects abnormal driving behavior. In this paper a system for detection of vehicle anomalies proposed, which at first extracts spatio-temporal features form clusters then creates dictionary from these features. This classification stage consists of processes such as, optimized clustering with the bee mating algorithm and sparse processing on spatiotemporal features derived from the training data. Finally the trained classifier is applied to the test data for anomaly detection. The distinction of this study from previous research is using new method of pre-processing to create a dictionary matrix and anomaly detection based on evaluation of matrix that related to each class dependency, which leads to higher accuracy of the proposed method compared to other leading methods. To evaluate the proposed method, UCSD database and video sequences recorded from vehicle traffic on Vakilabad Boulevard at the north side of Ferdowsi University of Mashhad are used and the performance of the proposed method is compare to other competing methods in this field. By analyzing the evaluation standards, we find that the proposed method performance is better than other methods. Manuscript profile -
Open Access Article
70 - An Adaptive Multi-Objective Clustering Algorithm based on Auction_Prediction for Mobile Target Tracking in Wireless Sensor Network
Roghieh Alinezhad Sepideh Adabi arash SharifiOne of the applications of sensor networks is to track moving target. In designing the algorithm for target tracking two issues are of importance: reduction of energy consumption and improvement of the tracking quality. One of the solutions for reduction of energy consu MoreOne of the applications of sensor networks is to track moving target. In designing the algorithm for target tracking two issues are of importance: reduction of energy consumption and improvement of the tracking quality. One of the solutions for reduction of energy consumption is to form a tracking cluster. Two major challenges in formation of the tracking cluster are when and how it should be formed. To decrease the number of messages which are exchanged to form the tracking cluster an auction mechanism is adopted. The sensor’s bid in an auction is dynamically and independently determined with the aim of establishing an appropriate tradeoff between network lifetime and the accuracy of tracking. Furthermore, since the tracking cluster should be formed and activated before the target arrives to the concerned region (especially in high speed of target), avoidance from delay in formation of the tracking cluster is another challenge. Not addressing the mentioned challenge results in increased target missing rate and consequently energy loss. To overcome this challenge, it is proposed to predict the target’s position in the next two steps by using neural network and then, simultaneously form the tracking clusters in the next one and two steps. The results obtained from simulation indicate that the proposed algorithm outperforms AASA (Auction-based Adaptive Sensor Activation). Manuscript profile -
Open Access Article
71 - 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
72 - Using Evolutionary Clustering for Topic Detection in Microblogging Considering Social Network Information
E. Alavi H. Mashayekhi H. Hassanpour B. Rahimpour KamiShort texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must MoreShort texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must be specified beforehand and cannot be changed during time. From this perspective, these methods are not suitable for increasing and dynamic data. In addition, non-parametric topic evolution models lack appropriate performance on short texts due to the lack of sufficient data. In this paper, we present a new evolutionary clustering algorithm, which is implicitly inspired by the distance-dependent Chinese Restaurant Process (dd-CRP). In the proposed method, to solve the data sparsity problem, social networking information along with textual similarity has been used to improve the similarity evaluation between the tweets. In addition, in the proposed method, unlike most methods in this field, the number of clusters is calculated automatically. In fact, in this method, the tweets are connected with a probability proportional to their similarity, and a collection of these connections constitutes a topic. To speed up the implementation of the algorithm, we use a cluster-based summarization method. The method is evaluated on a real data set collected over two and a half months from the Twitter social network. Evaluation is performed by clustering the texts and comparing the clusters. The results of the evaluations show that the proposed method has a better coherence compared to other methods, and can be effectively used for topic detection from social media short texts. Manuscript profile -
Open Access Article
73 - Improving Energy Consumption in Wireless Sensor Networks Using Shuffled Frog Leaping Algorithm and Fuzzy Logic
Shayesteh TabatabaeyWireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the w MoreWireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled Frog Leaping algorithm, which acts based on the Frog food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802.15.4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802.15.4 Protocol and NODIC Protocol due to the use of the criteria listed. Manuscript profile -
Open Access Article
74 - A Semi-Central Method to Improve Energy Saving in Real Wireless Sensor Networks Using Clustering and Mobile Sinks
Fatemeh Sadeghi Sepideh Adabi Sahar AdabiApplying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to criti MoreApplying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to critical regions (i.e., regions those have low remaining energy and thus, high risk of energy hole problem). The limited number of mobile sinks should be utilized due to a high cost. Therefore, allocating the limited number of mobile sinks to the high amount of requests received from the critical regions is categorized as a NP-hard problem. Most of the previous studies address this problem by using heuristic methods which are carried out by sensor nodes. However, this type of solutions cannot be implemented in real WSN due to the sensors’ current technology and their limited processing capability. In other words, these are just theoretical solutions. Consequently, a semi-central genetic algorithm based method using mobile sink and clustering technique is proposed in order to find a trade-off between reduction of computation load on the sensors and increasing accuracy. In our method, lightweight computations are separated from heavyweight computations. While, the former computations are carried out by sensors, the latter are carried out by base station. Following activities are done by the authors: 1) cluster head selection by using effective environmental parameters and defining cost function of cluster membership, 2) mathematical modeling of a region’s chance to achieve mobile sink, and 3) designing a fitness function to evaluate the fitness of each allocation of mobile sinks to the critical regions in genetic algorithm. Furthermore, in our activities minimizing the number and length of messages are focused. In summary, the main distinguishing feature of the proposed method is that it can be implemented in real WSN (due to separation of lightweight computations from heavyweight computations) with respect to early mentioned objectives. The simulation results show the better performance of the proposed method compared to comparison bases. Manuscript profile -
Open Access Article
75 - A New Data Clustering Method Using 4-Gray Wolf Algorithm
Laleh Ajami Bakhtiarvand Zahra BeheshtiNowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in MoreNowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets. Manuscript profile -
Open Access Article
76 - Energy-Aware Data Gathering in Rechargeable Wireless Sensor Networks Using Particle Swarm Optimization Algorithm
Vahideh Farahani Leili Farzinvash Mina Zolfy Lighvan Rahim Abri LighvanThis paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine t MoreThis paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware Data Gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5.6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero Manuscript profile -
Open Access Article
77 - A POI Recommendation Model According to the Behavior Pattern of Users Based on Friends List Using Deep Learning
sadaf safavi mehrdad jalaliThe rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is MoreThe rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is to investigate numerous significant traits of users and POIs. In this work, a novel method is presented for POI recommendation to develop the accurate sequence of top-k POIs to users, which is a combination of convolutional neural network, clustering and friendship. To discover the likeness, we use the mean-shift clustering method and only consider the influence of the most similarities in pattern’s friendship, which has the greatest psychological and behavioral impact rather than all user’s friendship. The new framework of a convolutional neural network with 10 layers can predict the next suitable venues and then select the accurate places based on the shortest distance from the similar friend behavior pattern. This approach is appraised on two LBSN datasets, and the experimental results represent that our strategy has significant improvements over the state-of-the-art techniques for POI recommendation. Manuscript profile -
Open Access Article
78 - A Novel Method Based on Non-Negative Matrix Factorization for Dimensions Reduction
Mehdi Hosseinzadeh Aghdam مرتضی آنالویی Jafar TanhaMachine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative mat MoreMachine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative matrix factorization reduces data dimensions by extracting latent features from large dimensional data. Non-negative matrix factorization only considers how to model each feature vector in the decomposed matrices and ignores the relationships between feature vectors. The relationships between feature vectors provide better factorization for machine learning applications. In this paper, a new method based on non-negative matrix factorization is proposed to reduce the dimensions of the data, which sets constraints on each feature vector pair using distance-based criteria. The proposed method uses the Frobenius norm as a cost function to create update rules. The results of experiments on the data sets show that the proposed multiplicative update rules converge rapidly and give better results than other algorithms. Manuscript profile -
Open Access Article
79 - Clustering Iranian Gas Industry Managers and Ranking Their Competencies via the EFQM Excellence Model-based Evaluation with an Artificial Intelligence Approach
Ali reza Zamanian Majid Jahangirfard Farshad HajalianThis study attempted to lay the ground for linking human resources data based on the results of the organizational excellence model for about 51 parent and subsidiary companies of the National Iranian Gas Company using artificial intelligence (AI) and machine learning m MoreThis study attempted to lay the ground for linking human resources data based on the results of the organizational excellence model for about 51 parent and subsidiary companies of the National Iranian Gas Company using artificial intelligence (AI) and machine learning methods. The goal was to present a model for clustering chief organizational managers based on the companies’ evaluation using the European Foundation for Quality Management (EFQM)-based excellence model. The unique characteristic of this method is that it is formed based on the actual performance and output of successful organizations, headed by successful managers and leaders. Accordingly, a performance-based excellence model can be achieved in the future. The outcomes of model evaluation for 2017, 2018, and 2019 for 51 companies affiliated with the National Iranian Gas Company were first clustered. Clustering was performed for 3776 pieces of data via AI-based methods, and coding was done in Python. This applied study aimed to design and develop a novel method for discovering the experts and scientifically classifying the organization’s human resources based on credible data. It also aimed to integrate novel scientific domains of AI, including clustering, to pave the ground for human resources research. In the applied dimension, the results were used in organizational planning and decision-making to generate a tool whereby the future managerial performance of the organization and staff can be predicted based on appropriate human resources data. Finally, a ranking is presented based on the competency gap by using Fisher discriminant ratio (FDR). Manuscript profile -
Open Access Article
80 - Sonic wave velocity estimation using intelligent system and multi resolution graph base clustering: A case study from one of Iranian south field
مرتضی نوری مینا کریمی خالدیAbstract Compressional and shear velocity are two fundamental parameters, which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using Dipole Sonic Imaging tool (DSI), but unfortunately this tool MoreAbstract Compressional and shear velocity are two fundamental parameters, which have many applications in petrophysical, geophysical, and geomechanical operations. These two parameters can be obtained using Dipole Sonic Imaging tool (DSI), but unfortunately this tool is run just in few wells of a field. Therefore it is important to predict compressional and shear velocity indirectly from the other conventional well logs that have good correlation with these parameters in wells without these logs. Classical methods to predict the mentioned parameters are utilizing correlations and regression analysis. However, the best tool is intelligent systems including Artificial Neural Network, Fuzzy Logic, Adaptive Neuro Fuzzy Inference System, and Multi resolution graph base clustering for performing such tasks. In this paper 1321 data points from Kangan and Dalan formations which have compressional and shear velocity are used. These data are divided into two groups: 995 and 326 data points were used for construction of intelligent systems and model testing, respectively. The results showed that despite differences in concept, all of the intelligent techniques were successful for estimation of compressional and shear velocities. The Multi resolution graph base clustering. The method had the best performance among the others due to precise clustering the data points. Using this method, the compressional and shear velocity were correlated with correlation factor of 0.9505 and 0.9407, respectively. The developed model does not incorporate depth or lithological data as a part of the inputs to the network. This means that utilized methodology is applicable to any field. Manuscript profile -
Open Access Article
81 - Making the lithology log by Clustering-Estimation approach in the wells of an oil field in south of Iran
ساره صدیق مهرنوش علی پور شهسواری حسین معماریان بهزاد تخم چیAbstruct Reservoir lithology determination is one of the main studies used for well correlation and analyzing productive zones of the reservoir. The best way foer lithology determination is using core and cutting information . Nevertheless,in most MoreAbstruct Reservoir lithology determination is one of the main studies used for well correlation and analyzing productive zones of the reservoir. The best way foer lithology determination is using core and cutting information . Nevertheless,in most wells these data is not complete and continual, so in these cases usually use well logging for lithology estimation of petrophysical well data with Clustering- Estimation approach . This method has been generalized according to one well from one of the oil fields in South of Iran that contains core data . Then this method is generalized in un cored wells. Clustering is used as a way for grouping well data in homogenous lithology clusters After ward, percentage of mineral is estimated in each of these clusters. The regression coefficients are calculated 92.93% and 74.99% between real and estimated data respectively for calcite and dolomite in one of the wells. The results with high accuracy show the generalization of this method. Manuscript profile -
Open Access Article
82 - The application of clustering methods (MRGC, AHC, DC, SOM) determining permeability carbonate reservoir rocks Ilam Formation in South West Iran
Seyed Ali Moallemi farhad khoshbakht sakineh naghdiThe permeability of reservoir parameters is important in the calculation and modeling reservoir plays a role. Measured directly via cores taken from the reservoir layer can be achieved. But due to the limited amount of core taken in a field and laboratory methods as wel MoreThe permeability of reservoir parameters is important in the calculation and modeling reservoir plays a role. Measured directly via cores taken from the reservoir layer can be achieved. But due to the limited amount of core taken in a field and laboratory methods as well as high cost; use indirect methods to determine the wells without core permeability is great value. In this study, using clustering methods using petrophysical logs permeability values were measured and analyzed. For this purpose, petrophysical logs Ilam Formation selection of 8 wells and addition of data measured in vitro permeability 3-ring is used to compare the results. Log permeability effective porosity in the well using the parameters A with the core permeability data, estimates and then check the accuracy of estimates, calculations also took place in other fields of study. In the next step, using clustering method, was estimated permeability. Then the results with experimental data and correlation coefficient, the best method is introduced. Manuscript profile -
Open Access Article
83 - Electro-facies analysis of the Sarvak Formation (Middle Cretaceous) and compliance with sedimentary microfacies in an oil field, SW Iran.
zahra Eghbalkiani Taher Goli ALI HOSSEIN JALILIAN Rahym KadghodaiiElectro-facies analysis is a sure method in clustering petrophysical logs analysis that can well indicates the changes of geologic charactereristics of the different lithostratigraphic units as well. Petrographic studies of 120 prepared thin sections of the upper parts MoreElectro-facies analysis is a sure method in clustering petrophysical logs analysis that can well indicates the changes of geologic charactereristics of the different lithostratigraphic units as well. Petrographic studies of 120 prepared thin sections of the upper parts of the Sarvak Formation in studied oilfield, Southwestern Iran, has resulted in definition of 6 microfacies sedimentary types. These data suggested that the middle Creataceous sediments in the studied area were predominantly deposited in two subenviornments of patch organic barrier and lagoon corresponding to inner and shallow a homoclinal ramp. In addition, using petrophysical logs in the studied well, 10 electrical facies were determined. By analyzing the data petrophysical in Geolog software, 10 electerical facies were optimized and then their number was reduced to six. According to good adaptation of optimized electerical facies with sedimentary microfacies, we would generalized obtained dresults to all parts of the Sarvak Formation. Study of porosity changes in the electrical diagrams such as CGR corrected Gamma, and sonic curves in mentioned well, indicated a significant reduction in facies porosity of 1 to 6. In addition, the results showed that the reservoir quality reductionin studied microfacies is corresponded to Bioclastic Rudist debris floatstone/ Rudstone, Benthic foraminifera Rudist debris wackestone / packstone, Benthic Foraminifera peloid Grainstone/Packstone, with High diversity benthic foraminifera wackestone / packstone, Peloid bioclastic wackestone /packstone with Low diversity benthic Foraminifera and Mudestone/Wackestone, respectively. In this study, we concluded that comparison and correlation sedimentary facies with electric microfacies provide an efficient method to study the quality of the hydrocarbon reservoir system that especially can be used in other wells with no core. Manuscript profile -
Open Access Article
84 - Designing a core competency model for Iranian gas industry managers based on the results of EFQM by artificial intelligence method
Ali reza Zamanian Majid Jahangirfard Farshad Haj alianThe success of organizations in achieving strategic goals is highly dependent on how the managers of that organization perform, and this success is more directly related to the selection and appointment of competent and knowledgeable managers at the top of the organizat MoreThe success of organizations in achieving strategic goals is highly dependent on how the managers of that organization perform, and this success is more directly related to the selection and appointment of competent and knowledgeable managers at the top of the organizational pyramid. Competency-based management is a strong approach to developing and maintaining the organization's human capital in the long run, and competency models are a useful tool for identifying and developing the knowledge, skills and abilities needed by managers. In general, competency is a set of knowledge, skills, attitudes, abilities, interests and personality traits that enable employees to perform job-related activities effectively and perform job performance as expected or beyond expectations. Do." In accordance with the competencies defined in the evaluation center of the managers of the National Iranian Gas Company and according to the nature of trainable competencies, three categories of communication competencies, managerial and leadership competencies, cognitive and one category of untrained personality competencies have been defined. In this article, we have tried to achieve a set of core competencies in the field of competencies of gas industry managers, which has been achieved based on the performance of managers in organizational excellence. These core competencies are the beacon of the organization in achieving strategic goals and can be purposefully effective in the shortest time and with the least cost in the development process of the managers of the organization. Manuscript profile -
Open Access Article
85 - Improving Precision of Recommender Systems using Time-, Location- and Context-aware Trust Estimation Based on Clustering and Beta Distribution
Samaneh Sheibani Hassan Shakeri Reza SheybaniCalculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimat MoreCalculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimating trust among users of recommender systems is introduced. In the proposed scheme, first, users of the system are clustered based on their similarities in demographics information and history of ratings. To predict the rating of the active user into a specific item, the value of trust between him and the other users in his cluster is calculated considering the factors i.e. time, location, and context of their rating. To this end, we propose an algorithm based on beta distribution. A novel tree-based measure for computing the semantic similarity between the contexts is utilized. Finally, the rating of the active user is predicted using weighted averaging where trust values are considered as weights. The proposed scheme was performed on three datasets, and the obtained results indicated that it outperforms existing methods in terms of accuracy and other efficiency metrics. Manuscript profile -
Open Access Article
86 - Video Summarization Using a Clustering Graph Neural Networks
Mahsa RahimiResketi Homayun Motameni Ebrahim Akbari Hossein NematzadehThe increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summar MoreThe increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summarization, this task is achieved and the film is summarized into a series of short but meaningful frames or clips. This study tried to cluster the data by an algorithm (K-Medoids) and then with the help of a convolutional graph attention network, temporal and graph separation is done, then in the next step with the connection rejection method, noises and duplicates are removed, and finally summarization is done by merging the results obtained from two different graphical and temporal steps. The results were analyzed qualitatively and quantitatively on three datasets SumMe, TVSum, and OpenCv. In the qualitative method, an average of 88% accuracy rate in summarization and 31% error rate was achieved, which is one of the highest accuracy rates compared to other methods. In quantitative evaluation, the proposed method has a higher efficiency than the existing methods. Manuscript profile -
Open Access Article
87 - Permeability improvement calculated from Stoneley-FZI method in Kangan reservoir, one of Iran's gas fields
hossein rezaie yegane doostPermeability in fluid flow is for a porous rock, which is exactly what causes the problem. core analysis and well testing are two most commonly used methods of permeability measurement, but in-vitro measurement of permeability by applying core analysis on all wells in a MorePermeability in fluid flow is for a porous rock, which is exactly what causes the problem. core analysis and well testing are two most commonly used methods of permeability measurement, but in-vitro measurement of permeability by applying core analysis on all wells in a specific field is very time consuming and costly and even impossible when dealing with Horizontal wells. Wells testing, on the other hand, is not cost-effective for reasons such as; High costs and zero production during the testing process. Therefore, thanks to their low cost, comprehensiveness and availability, permeability estimation methods developed according to conventional logs land DSI diagrams are of critical importance. Taking this into account, in the present study, permeability was first estimated using multi-resolution graph-based clustering (MRGC) and the results were compared with permeability rates obtained from core analysis. In the second stage, permeability was measured by ST-FZI method and the results were compared with permeability rates obtained from core analysis. In the third stage, the multi-resolution graph-based clustering (MRGC) method was used to improve the permeability calculated by the ST-FZI method and overcome the reservoir heterogeneity. First the flow units were identified, and then the ST-FZI method was applied on each flow unit to calculate permeability and finally the calculated permeabilities were combined to obtain an accurate permeability graph of the studied well. The correlation coefficients of permeability rates estimated via core analysis in the multi-resolution graph-based clustering method (R2 = 77), ST-FZI method (R2 = 47) and improved method (R2 = 84) were measured. The afore-mentioned method was able to improve the permeability calculated in the previous step by 37% and was recognized as the best permeability measurement method in the Kangan reservoir of the well subjected to study. Manuscript profile -
Open Access Article
88 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi GolsorkhtabaramiriTo create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension MoreTo create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile -
Open Access Article
89 - Fuzzy Multicore Clustering of Big Data in the Hadoop Map Reduce Framework
Seyed Omid Azarkasb Seyed Hossein Khasteh Mostafa AmiriA logical solution to consider the overlap of clusters is assigning a set of membership degrees to each data point. Fuzzy clustering, due to its reduced partitions and decreased search space, generally incurs lower computational overhead and easily handles ambiguous, no MoreA logical solution to consider the overlap of clusters is assigning a set of membership degrees to each data point. Fuzzy clustering, due to its reduced partitions and decreased search space, generally incurs lower computational overhead and easily handles ambiguous, noisy, and outlier data. Thus, fuzzy clustering is considered an advanced clustering method. However, fuzzy clustering methods often struggle with non-linear data relationships. This paper proposes a method based on feasible ideas that utilizes multicore learning within the Hadoop map reduce framework to identify inseparable linear clusters in complex big data structures. The multicore learning model is capable of capturing complex relationships among data, while Hadoop enables us to interact with a logical cluster of processing and data storage nodes instead of interacting with individual operating systems and processors. In summary, the paper presents the modeling of non-linear data relationships using multicore learning, determination of appropriate values for fuzzy parameterization and feasibility, and the provision of an algorithm within the Hadoop map reduce model. The experiments were conducted on one of the commonly used datasets from the UCI Machine Learning Repository, as well as on the implemented CloudSim dataset simulator, and satisfactory results were obtained.According to published studies, the UCI Machine Learning Repository is suitable for regression and clustering purposes in analyzing large-scale datasets, while the CloudSim dataset is specifically designed for simulating cloud computing scenarios, calculating time delays, and task scheduling. Manuscript profile -
Open Access Article
90 - Improving energy consumption in the Internet of Things using the Krill Herd optimization algorithm and mobile sink
Shayesteh TabatabaeiInternet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging info MoreInternet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol Manuscript profile
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