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Open Access Article
1 - Analysis of Kuroshio Current Converter as Renewable and Environmentally Friendly Power Plant
امیر قائدیIntroduction: Among different renewable energy sources, ocean is considered as one of the renewable energy sources that has a wide geographical range. Marine currents have different categories that tidal currents are among these currents. Near Japan, there is a current MoreIntroduction: Among different renewable energy sources, ocean is considered as one of the renewable energy sources that has a wide geographical range. Marine currents have different categories that tidal currents are among these currents. Near Japan, there is a current in the ocean known as the Kuroshio Current, which has a high potential for generating electricity. These currents have speed and consequently kinetic energy and can generate electricity by using turbines installed deep in the ocean. One of the problems that these currents have is that they change over time and therefore the power generation of energy converters of Kuroshio currents also varies. Therefore, the effect of these changes on different aspects of these converters such as reliability should be investigated. Materials and Methods: In the reliability model of the current converter, both the component failure and output power variations, which are caused by the change in the speed of ocean currents are considered. Results and Discussion: In this part, a Kuroshio Current energy converter that includes a turbine with a diameter of 2 meters is considered. In this part, the reliability indices of a sample test system are obtained, and then the effect of Kuroshio Current power plants on these indices is evaluated. The results show that as the peak load of the system increases, the reliability of the system deteriorates. Conclusion: In this paper, the reliability model of Kuroshio Current converters is obtained. Numerical results conclude that the Kuroshio Current converters can improve reliability indices of power system. Manuscript profile -
Open Access Article
2 - Extending the Lifetime of Wireless Sensor Networks Using Fuzzy Clustering Algorithm Based on Trust Model
Farshad Kiyoumarsi Behzad Zamani DehkordiWireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very MoreWireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase. Manuscript profile -
Open Access Article
3 - Clustering and mapping the 40-year trend of foresight research
mohammad yousefi khoraem hakem gasemi farhad se talani Einollah Keshavarz Turk Morteza MousakhaniAbstract foresight are considered as an effective tool to reduce the complexity of complex and dynamic systems. Defeating the business environment can be very dangerous for organizations, prospecting provides a structured opportunity to look at the future. Therefore, du MoreAbstract foresight are considered as an effective tool to reduce the complexity of complex and dynamic systems. Defeating the business environment can be very dangerous for organizations, prospecting provides a structured opportunity to look at the future. Therefore, due to the diversity and variety of issues related to futurism, organizations and researchers have conducted numerous research in this field. purpose: Considering the large volume of researches as a challenge for identifying research trends and emerging areas, in this research, using scientific methods, the research process of researchers in the field of foresight analysis And analyzed. Methods: In this research 3883 articles published between 1979 and June 2019 in the field of foresight in the WOS database were analyzed using VOSviewer software. The article's network of referrals, both the occurrence of keywords, the co-authorship of the authors And references to magazines analyzed and identified by leading authors, journals and fields of research. Findings: According to the results, futures, technological forecasting and social change, journal of political economy, were recognized as the most influential magazines in the field of futures studies. Also, Suddendorf, T, Saritas, O, Sarpong, D, and Rohrbeck, r were recognized as authors with the highest share in science production. Among the keywords used in all reviewed articles, the words decision-making, corporate foresight, climate-change have been the highest rank among repetitions between articles in the field of foresight. And economics, regional urban planning, business, as the research area with the highest number of papers. Manuscript profile -
Open Access Article
4 - A Comparative Analysis of Mission and Vision Statements of Top Fortune Companies and Iranian Companies in Food Industry using Text Mining and Clustering
Mohsen Shafei nik abadi Ata Karbasi kheyrMission and vision statement have an important role in company’s strategic goals and direction, so content analysis of such documents has a positive influence on company’s success. Food industry, as a major means of meeting one of people’s most essenti MoreMission and vision statement have an important role in company’s strategic goals and direction, so content analysis of such documents has a positive influence on company’s success. Food industry, as a major means of meeting one of people’s most essential needs, requires to be improved. Therefore, we aimed to analyze the mission and vision statement of top food industry companies in Iran and the world to understand about similarities and differences in their strategic alignments. The current research is conducted on 53 companies from top 500 fortune list and 42 Iranian pioneer companies based on ministry of industry and mining list. We used text mining technique to process the documents and clustered them into distinct groups by K-Means algorithm. Finally, we ranked the important roots in each cluster using SAW method. Then, we compared the clusters based on their essential components. The findings indicate that while foreign companies focus on customers and image creation, Iranian companies care about products, development and stating general concepts. Manuscript profile -
Open Access Article
5 - Cluster Analysis of Iran's Position in the World and Future Trends Based on Good Governance Components
Mona Ahani Morteza Mosakhani Reza Najafbeigi Mohammad Ali AfsharkazemiThe Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory qualit MoreThe Study of good governance and the quality of government institutions is a debate that began in the 90's. Good governance, which consists of six components: control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, rule of law, voice and accountability, is a model for development. In this study, the World Bank's assessments and statistics on the six-fold good governance indicators published each year, were used to survey 186 countries worldwide. The aims of this research were studying the status of countries based on good governance and determining the status of Iran among other countries, using clustering technique; And analyzing the trend of Iran's position in the 2021 horizons using time series analysis. Using the clustering method of the countries of the world, based on the good governance and the frequent clustering of Iran with other countries, they were separated. Then, from the time series method using of the exponential smoothing based on the ARIMA's method was investigate to predict the six's good governance indicators and the situation of the country in the next five years. Findings of the research show that in the 2021 horizons, the accountability index will be problematic in the country and the rule of law and control of corruption ratios will remain almost unchanged, and, on the other hand, the rest of the indicators show a slight improvement. Manuscript profile -
Open Access Article
6 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad PourzarandiDecision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla MoreDecision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile -
Open Access Article
7 - Using Clustering and Genetic Algorithm Techniques in Optimizing Decision Trees for Credit Scoring of Bank Customers
Mahmood Alborzi Mohammad Khanbabaei M. E. Mohammad PourzarandiDecision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy incla MoreDecision trees technique as one of the data mining techniques, is used in credit scoring ofbank customers to classify them in order to offer credit facilities. The main problem is incomplexity of decision trees, excessive size, lack of flexibility and low accuracy inclassification. The purpose of this paper is to propose a compound model in the optimization ofdecision trees by using genetic algorithm technique. It appears that genetic algorithm can chooseappropriate features and build decision trees to reduce complexity and increase flexibility indecision trees. In the proposed compound model, the credit data is initially divided into twoclusters by Simple means clustering technique. On the next step, the important credit scoringfeatures in the data set are selected using genetic algorithm and the five feature selectionalgorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decisiontrees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desiredoptimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machinelearning tool and GATree software were used to in this purpose. Results show that using theproposed compound model in building decision trees leads to increased classification accuracy,compared to other algorithms in this paper. However the algorithm complexity of the proposedcompound model is more than some of the classification algorithms compared in this paper. Manuscript profile -
Open Access Article
8 - Experimental Evaluation of Algorithmic Effort Estimation Models using Projects Clustering
Farzaneh Famoori Vahid Khatibi bardsiri Shima Javadi Moghadam Fakhrosadat Fanian -
Open Access Article
9 - Using fuzzy c-means clustering algorithm for common lecturer timetabling among departments
hamed babaei Jaber Karimpour Sajjad Mavizi -
Open Access Article
10 - Energy optimization based on routing protocols in wireless sensor network
Zoleikha Azizi Kambiz Majidzadeh -
Open Access Article
11 - Fuzzy Clustering Based Routing in Wireless Body Area Networks to Increase the Life of Sensor Nodes
Mohsen Abdollahzadeh Aghbolagh Mohammad Ali Pourmina -
Open Access Article
12 - A Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
Jensi R -
Open Access Article
13 - An Improved Bat Algorithm based on Whale Optimization Algorithm for Data Clustering
Neda Damya Farhad Soleimanian Gharehchopogh -
Open Access Article
14 - An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering
Farhad Soleimanian Gharehchopogh Sevda Haggi -
Open Access Article
15 - An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms
Mohammad Hassanzadeh farshid keynia -
Open Access Article
16 - A New Model-based Bald Eagle Search Algorithm with Sine Cosine Algorithm for Data Clustering
Farhad Soleimanian Gharehchopogh Berivan Rostampnah -
Open Access Article
17 - Measuring the efficiency of a three-stage network using data envelopment analysis approach considering dual boundary
Ehsan. Vaeezi S. Esmail. Najafi Seyed Mohammad. Haji Maulana Farhad, Hosseinzadeh Lotfi Mahnaz. Ahadzadeh NaminThis paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about MoreThis paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable and undesirable inputs and outputs and utilize the cooperative approach to measure the efficiency of the overall system. Due to the fact that, a conclusion implying only one of these two, optimistic or pessimistic views is one-sided and incomplete, so, in this paper we used the double-frontier to analyze the network. Moreover, a heuristic technique was used to convert non-linear models into linear models. After obtaining the effective and inefficient points of the network, the DMUs are classified into several clusters by the k-means algorithm.Finally, in this article, in order to apply the proposed model a factory producing dairy products with a production area, warehouse premises and a delivery point are simulated. This factory has been regarded as a dynamic network with a time period of 24 intervals. The results of the ranking showed that, the time periods, (10) and (1) were the best and poorest respectively, in context to the efficiency within 24 phases of time. Manuscript profile -
Open Access Article
18 - Clustering with Intelligent Linexk-Means
نرگس Ahmadzadehgolia M.H. Behzadi A. MohammadpourThe intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the c MoreThe intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or underestimate the centers which helps to assign some entities into a special cluster. We check the performance of the algorithm on some real and artificial datasets and evaluate the results according to some internal and external indexes. Manuscript profile -
Open Access Article
19 - An Effective Algorithm in order to solve the Capacitated Clustering Problem
N. Mahmoodi Darani P. Bassiri M. YousefikhoshbakhtThe capacitated clustering problem (CCP) is a data mining technique utilized to categorize a number of objects with known demands into k distinct clusters such that the capacity of each cluster is not violated, every object is allocated to exactly one cluster and sum of MoreThe capacitated clustering problem (CCP) is a data mining technique utilized to categorize a number of objects with known demands into k distinct clusters such that the capacity of each cluster is not violated, every object is allocated to exactly one cluster and sum of distances from all cluster centers to all other nodes is minimized. The CCP is an NP-hard combinatorial optimization problem. Therefore, practical large-scale instances of this problem cannot be solved by exact solution methodologies within acceptable computational time. Our interest was therefore focused on meta-heuristic solution approaches. For this reason, a modified imperialist competitive algorithm (MICA) is proposed for the CCP In this paper. The proposed MICA iterates steps between three basic phases, i.e., the random assignment phase to form clusters, the seed relocation phase to find a better median, and the local improvement phase to make a revision of the solution. The proposed algorithm is tested on several standard instances available from the literature. The computational results confirm the effectiveness of the presented algorithm and show that the proposed algorithm is competitive with other meta-heuristic algorithms for solving the CCP. Manuscript profile -
Open Access Article
20 - Providing a model for tail risk estimation using extreme Value mixture models (Parametric, semi-parametric and non-parametric)
ali soori bahman esmaeili vahid nobakhtFinancial market participants are constantly exposed to uncertainty and investment risk. Predicting and calculating risk is one of the most important issues in the field of financial issues. Reviewing the financial crises of recent years, it can be inferred that one of MoreFinancial market participants are constantly exposed to uncertainty and investment risk. Predicting and calculating risk is one of the most important issues in the field of financial issues. Reviewing the financial crises of recent years, it can be inferred that one of the reasons for these crises is the excessive attention to the repetitive central data and the lack of attention to the extreme data. In other words, in the analysis of financial data, the end part of the distribution should also be considered. The purpose of this study is to provide a model for tail risk estimation using extreme value mixture models. Accordingly, four one-tailed models and one two-tailed model in two simple functions and GARCH have been used. Modeling is based on three categories of data. The studied data include total index, price index (homogeneous) and index of top 50 companies. According to the obtained results, simulation of models with GARCH significantly improves the performance of models and reduces the error rate of simulated data in GARCH-based models. The findings also indicate that two-tailed models are more accurate than one-tailed models. Manuscript profile -
Open Access Article
21 - Codification of Dendrograms Portfolio Based on Euclidean Distance Measure (A Comparison Between Different Methods of Hierarchical Clustering)
Hojatollah Sadeghi Sharifeh Forooghi DehnaviToday analysis of financial markets as a part of the capital market and its impact on development and portfolio design and investment strategy of each country has become an important and most critical issue. The aim of this study was to investigate how the connection an MoreToday analysis of financial markets as a part of the capital market and its impact on development and portfolio design and investment strategy of each country has become an important and most critical issue. The aim of this study was to investigate how the connection and distribution of stocks related to 30 large companies index of Tehran Stock Exchange and the effects of relationship between clusters of related stocks to every industry. In this study, using a variety of methods of hierarchical clustering, structure, classification and hierarchy of the stocks in the year 1393 reviewed. The results showed that With a focus on each of the hierarchical clustering methods and their implementation on the target stocks, were identified different clusters of stocks due to the similarity and economic relationships and also the key clusters and the vital stocks in the desired set were obtained. The results indicate that the choice best hierarchical clustering algorithm for clustering stocks depends on the desired purpose of cluster analysis and consideration of the advantages and disadvantages of each method. Manuscript profile -
Open Access Article
22 - A hybrid Model on the Basis of Data Envelopment Analysis and Data Mining Techniques to Analyze the Investment Behavior in Stock Exchange: A Real Case Study in Tehran Stock Exchange
Saiedeh Molla Hosseinagha Kaveh Khalili-Damghani -
Open Access Article
23 - Analyzing and Drawing the Co-word Map of Competitive Intelligence in First-Level Technical Universities of Tehran
Mandana Yavari Parivash Jafari Nadergholi Gorchian -
Open Access Article
24 - Identify and Prioritize the Critical Success Factors for Implementing Electronic Logistics in the Isfahan Health City
Saied Darabi Mehdi Iranpoor AtefeH AmindoustIntroduction: The organizations providing health services have a significant role in the society. Organizations the utilization of the electronic process can improve the quality and capacity of services and so the level of in the health society. This study identifies an MoreIntroduction: The organizations providing health services have a significant role in the society. Organizations the utilization of the electronic process can improve the quality and capacity of services and so the level of in the health society. This study identifies and prioritizes the key factors to implement the electronic logistics in the Isfahan Health City. Methods: by reviewing the literature, 28 potential factors for implementation of E-logistics were extracted. After that, the degree of significant of these factors was determined according to the expert using group decision making. to screen the in significant factors a method based on K-means clustering was used. Then using a recent method for ranking the fuzzy numbers, which is based on the interval of the fuzzy numbers and the center of gravity, the space the factors were prioritize according to the overall cost effectiveness index. Results: by K-Means clustering method 8 factors have been screened and the 20 remaining factors were prioritize according to the overall cost effectiveness index. After calculating the distance of each factor with maximum and minimum range of factors, the vision factor with score 3.761 and the Maintenance factor with score 0.093 were identified as the most important and the least important factors respectively. Conclusion: In this study it was found that vision, create project teams, strategy and management support are the most importance factors for the implementation of E-logistics. Manuscript profile -
Open Access Article
25 - Ranking Pharmaceutics Industry Using SD-Heuristics Approach
S. H. Ahmadi S. R. Alizadeh Shani -
Open Access Article
26 - An Investigation into Factors that Affect Brand Choice Using Factor Analysis Approach
M. Samiei Nasr S. M. Alavi M. Nadjafi SiahroudiNowadays, the importance of brand concept is so high that some researchers believe that brand is a perfect product and argue that most of the time customers instead of buying a product, buy the brand. Therefore, recognizing the factors influencing brand choice and inves MoreNowadays, the importance of brand concept is so high that some researchers believe that brand is a perfect product and argue that most of the time customers instead of buying a product, buy the brand. Therefore, recognizing the factors influencing brand choice and investigating the specific features of brand is of importance and requires various studies. The present study aimed at investigating into factors that affect brand choice. The methodology of this study was applied research and for data collection, survey approach was used. The statistical population consisted of buyers in Shiraz and statistical sample based on Factor Analysis were 400 buyers (sampling method: regional method). Based on statistical analyses, 30 factors that affect brand choice were categorized in 9 classifications; five factors, namely the simplicity of brand's pronunciation, non-ambiguity of brand, simplicity of brand memorization; writing language and understandability of brand, were classified as the important ones. Further, based on qualitative analyses, result revealed that “past experience of buying” for respondents affects in brand choice to a great extent. Manuscript profile -
Open Access Article
27 - Investigating the Effect of Different Data Clustering Methods on the Accuracy of Models Related to Accounting Estimates by Comparing Traditional and Classical Clustering Methods
S. Mohsen Salehi Vaziri Jamal Barzaghi KhaneghahToday, the use of accounting information estimation is the same as other disciplines because of the lack of access to all information. For this reason, in this research, we tried to study the accuracy of accounting estimation models using different clustering methods to MoreToday, the use of accounting information estimation is the same as other disciplines because of the lack of access to all information. For this reason, in this research, we tried to study the accuracy of accounting estimation models using different clustering methods to determine how different clustering methods increase the accuracy of the desired models and the preferred method Among the different clustering methods, which method can be used to increase the accuracy of the models. The research sample consisted of 99 companies listed in the Tehran Stock Exchange. In order to collect the required data, the financial statements and notes of the 9-year period (2008-2017) were used by the companies. The results of the research showed that the use of different clustering methods increases the accuracy of accounting estimates models in most cases. However, among the clustering methods used in the research, the classic clustering method is a more appropriate method than the method The traditional approach is to increase the accuracy of accounting estimates models. Manuscript profile -
Open Access Article
28 - The effect of distance measurement methods on the classification of ecological groups in Hyrcanian forests
Naghmeh Pakgohar Javad Eshaghi Rad Gholamhossein Gholami Ahmad Alijanpour David W. RobertsNowadays, the application of clustering methods is widely increased, although choosing the right method due to the existence of different method and effective factors is difficult. The present study aimed to compare the results of widely used clustering algorithms and t MoreNowadays, the application of clustering methods is widely increased, although choosing the right method due to the existence of different method and effective factors is difficult. The present study aimed to compare the results of widely used clustering algorithms and to determine the most effective methods according to the different evaluators and evaluate the effective distance measurement method for clustering algorithms. The data of Hyrcanian beech forests were examined in an area protected by the department of natural resources of Nowshahr. Random-systematic sampling method with regular grid of 100×200 m was used for determining the center of sample plots; 100-m2 (10×10 m) sample plots had been used to check the shrub species and 400-m2 (20×20 m) to check the herbaceous species. A total of 120 sample plots were measured. The abundance and coverage of tree, shrub and herbaceous species were estimated based on Braun-Blanquette scale. Three distance methods of measuring distance Bray Curtis, Hellinger and Manhattan were used and five clustering methods (Average method clustering methods, Ward method, flexible beta method with beta values of -0.1, -0.25, -0.4) with six evaluation indicators (silhouette evaluation criterion, PARATNA criterion, Indval criterion, ISAMIC criterion, MRPP criterion and Phi correlation coefficient) were examined. Different clustering algorithms were arranged from best to worst for each dataset. The comparison analysis revealed that Ward’s and flexible-beta with beta value of -0.1 had the best performance. The present findings illustrated that Hellinger distance measurement method is better in homogeneous data than other distance measurement methods. Manuscript profile -
Open Access Article
29 - Spatial Analysis and Evaluation of Development Indices in Kohgiluyeh and Boyer-Ahmad Province (Using Factor Analysis and Cluster Analysis)
Asghar Zarrabi Seyed Ali Mousavi Noor -
Open Access Article
30 - The Evaluation of Development rural areas of kurdestan Province by Cluster Analysis
علی زنگی آبادی طاهر پریزادیIntroduction and purpose of research: Target all the planning, to achieve optimal development and balance, and the first step in any planning process, including the regional planning in particular, understand that this situation is identified, require separate study are MoreIntroduction and purpose of research: Target all the planning, to achieve optimal development and balance, and the first step in any planning process, including the regional planning in particular, understand that this situation is identified, require separate study area to areas of planning and evaluation of each area by index development (economic, social, cultural, health and ....) and analyze and ranking each area in terms of development is having. Process of development the Kurdistan and its infrastructures, the poor and concentration planning of the past, development balance in the level of rural areas is difficult. In order to solve problems caused by lack of balance, first step recognition and classification level of villages form part of the political divisions (in terms of having educational - cultural, infrastructure and communications, spatial - physical, health , economy and services ...) and the next step is providing good policy, to establish balance . Research method: In the present study, a combination of the methods of procedure documentation, analytical. This study used 18 statistical indicators in 5 district - cultural, infrastructure and communications, space - physical, health, economy and services active , especially in rural areas of Kurdistan 26 section , were studied. And analysis techniques of statistical Z-score and clustering analysis. Results: Based on research findings, the development level of Kurdistan sections, differences and inequality, there is, so that rural areas in seven sections (developed), seven section (partly developed), six sector-level (less developed) at the level of six section are excluded. Therefore is recommended to resolve imbalances, priority planning and investment sectors deprived rural areas (Alvt, Srshyv Marivan, Zyvyh, Nnvr, Nmh milk). Manuscript profile -
Open Access Article
31 - Machine learning clustering algorithms based on Data Envelopment Analysis in the presence of uncertainty
Reza Ghasempour Feremi Mohsen Rostamy-Malkhalifeh -
Open Access Article
32 - Determination of Homogeneous Hydrological Regions for Estimating Runoff in Ungauged Catchments Using Cluster Analysis (Case Study: Ardabil Province)
Batoul Poorseifollahi Amin Kanooni Mohammadreza Nikpour javad ramezani moghadamGrouping of catchments based on their climatic factors and physiographic characteristics is a prerequisite for regional analysis of runoff and its use for estimating discharge of catchments without discharge measurement station. In this study, catchments located in Arda MoreGrouping of catchments based on their climatic factors and physiographic characteristics is a prerequisite for regional analysis of runoff and its use for estimating discharge of catchments without discharge measurement station. In this study, catchments located in Ardabil province were separated into homogeneous hydrological zones using discharge, mean annual precipitation and physiographic characteristics of catchments and hierarchical clustering method. Due to the large number of parameters, by using principal component analysis, the first four components with 83.6% of total variance were selected as inputs for cluster analysis. Then, the optimal number of clusters was determined by using hierarchical method and drawing the tree diagram, and finally the final clustering was done by K-means method. Subsequently, the sub-catchments that followed a hydrological process were identified using the Dalrymple uniformity test. The results of the uniformity test showed that by excluding stations outside the confidence limits of each cluster, catchments that were similar in terms of annual discharge and other physiographic and meteorological parameters were clustered. Therefore, the number of sub catchments located in clusters 1, 2, 3 and 4 were 8, 4, 9 and 9 catchments, respectively, which can be used in regional analysis to estimate runoff and floods in catchments without discharge measurement data. Manuscript profile -
Open Access Article
33 - Prediction of Drying Time and Moisture Content of Wild Sage Seed Mucilage during Drying by Infrared System Using GA-ANN and ANFIS Approaches
Ghazale Amini Fakhreddin Salehi Majid Rasouli -
Open Access Article
34 - Spatial and Temporal Analysis of Noise Pollution Based On GIS, Agglomerative Hierarchical Clustering and Principal Component Analysis (Case Study: Tehran)
Amir Esmael Forouhid Mohsen RostamiBackground & Objective: Noise is an adverse factor in the living environments of today’s communities. This type of pollution has drawn attention to itself in the three recent decades, being a major problem in larger cities and seen as one of the significant en MoreBackground & Objective: Noise is an adverse factor in the living environments of today’s communities. This type of pollution has drawn attention to itself in the three recent decades, being a major problem in larger cities and seen as one of the significant environmental problems, blood pressure leading to cardiovascular disorders.Material and Methodology: The paper studied Tehran, Iran. The study areas consisted of Tajrish Square, Tohid and Sattarkhan crossroads, Hakim Freeway and Hemmat Freeway of Tehran. The study areas were selected based on their traffic and urban importance. The survey measured sound levels, road slope, road width, traffic, and land use (residential, commercial, administrative, and green space). In the field method, noise pollution level was measured using a sound level meter. Due to the role of traffic parameters, slope and residential, commercial, office and green space usage parameters, the parameters were recorded for each map of harvest. The spatial and temporal dependencies were extracted using "Agglomerative hierarchical clustering" and "principal components analysis".Findings: The results indicate the critical significance of urban traffic in noise pollution, as by a large difference it had the highest contribution to noise level, followed by green space, administrative, and commercial land use; road width, and road slope.Discussion & Conclusions: It is recommended that for future roads or revamping the existing ones, more lanes be implemented to produce wider roads, prevent the construction of tall buildings on the sided of main roads, and maintaining a standard distance between buildings and main roads, freeways, and other motorways. Manuscript profile -
Open Access Article
35 - Clustering of volatility and its asymmetry in Tehran Stock Exchange
زهرا شیرازیان hashem NIKOUMARAM Taghi´´´ TORABIThe purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other ha MoreThe purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other hand, higher volatility fluctuations, They tend to form more clusters than small fluctuations, which are referred to as clustering oscillations of oscillations. The volatility of return on assets can directly affect the price of transaction options and the risk of stocks and portfolios. This research is a practical and quantitative research. The statistical society of the time series of the index of Tehran Stock Exchange and the sample used in the time series of return on the total index in the period from the beginning of 2008 to August 2012 is. The index values are extracted from the new rational software and then the logarithmic yield is calculated and analyzed with the Eviews software. Based on the Box and Jenkins approach, the mean ARMA equation was obtained and ARCH test confirmed the existence of clustering fluctuations. The TGARCH model showed asymmetry in volatility and leverage effect. According to the AKIC statistic, the best GARCH model was used for extraction of fluctuations, ETGARCH was introduced. Manuscript profile -
Open Access Article
36 - Portfolio Optimization Using Hierarchical and Denotation Clustering in Tehran Stock Exchange.
mojtaba mirlohi nima mohammadi TodeshkiThe need to achieve optimal portfolio optimization criteria, which, in addition to the accuracy of investment decisions quickly, goes beyond academic needs, as investors, investment companies and investment managers need to reduce their investment losses, increase Risk- MoreThe need to achieve optimal portfolio optimization criteria, which, in addition to the accuracy of investment decisions quickly, goes beyond academic needs, as investors, investment companies and investment managers need to reduce their investment losses, increase Risk-adjusted returns have always been discussed. However, it is very difficult to achieve a portfolio optimization method that fills the gap between applied requirements and theoretical models. Considering that one of the most important factors in achieving optimal returns is diversification. This research, with the theme of "Formation of optimal investment portfolio in Tehran Stock Exchange using hierarchical and K-means clustering methods", attempts to present a suitable method for portfolio optimization using market data and clustering. The result of this comparison will clarify the success rate of cluster optimization compared to the index portfolio. Manuscript profile -
Open Access Article
37 - Investigating of effect herding behavior types among analysts on stock price by network analysis in Tehran Stock Exchange
Zahra ShirazianHerding behavior among security analysts is described as similar behavior by analysts when forecasting main finance ratios of public companies and when giving investment recommendations. This type of behavior can be divided into two categories based on the different dri MoreHerding behavior among security analysts is described as similar behavior by analysts when forecasting main finance ratios of public companies and when giving investment recommendations. This type of behavior can be divided into two categories based on the different driving forces behind the analysts’ herding behavior.. For instance, if one analyst lacks the ability to research and provide recommendations, he or she may follow, or even copy, a famous analyst’s reports. Such action will result in herding behaviorIn this paper, we build undirected weighted networks to study herding behavior among analysts and to analyze the characteristics and the structure of these networks. We then construct a new indicator based on the average degree of nodes and the average weighted clustering coefficient to research the various types of herding behavior. Our findings suggest that every industry has, to a certain degree, herding behavior among analysts. Furthermore, we relate the two types of herding behavior to stock price and find that uninformed herding behavior has a positive effect on market prices, whereas informed herding behavior has a negative effect. Manuscript profile -
Open Access Article
38 - Stock Price Clustering and Factors Affecting on It in Iran Capital Market
Moslem Peymany Foroushany Amir Hossein Erza Mohammad Mahdi BahrololoumPrice clustering is the tendency of prices to be round numbers. This phenomenon is studied in different countries and in various financial variables and several hypotheses have been put forth to explain that. In this study, existence of this price clustering and factors MorePrice clustering is the tendency of prices to be round numbers. This phenomenon is studied in different countries and in various financial variables and several hypotheses have been put forth to explain that. In this study, existence of this price clustering and factors affecting on it is tested in Iran capital market. To this, high frequency data is used and by means of statistical tests, the existence of price clustering is observed in Iran capital market. Furthermore, among all variables, only size, price and a dummy variable to distinguish between Tehran stock exchange and Iran Farabourse exchange, were effective on the intensity of price clustering and despite the expectations, size variable has a positive coefficient. Results are corresponded to odd-pricing, price resolution (just for price variable and not for size) and negotiation hypothesis. Manuscript profile -
Open Access Article
39 - A cultural algorithm for data clustering
M. R. Shahriari -
Open Access Article
40 - Application of 3D-QSAR on a Series of Potent P38-MAP Kinase Inhibitors
Reihaneh Safavi-Sohi Jahan B. Ghasemi -
Open Access Article
41 - High-Scale Image Clustering with Semantic Cues Modeling and Spatial Simulation
Mahdi JalaliIn recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve MoreIn recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve the highest efficiency, the clustering results of six systems with different color space and similarity criteria are cooperatively combined with the majority vote. When the number of votes for an image is low, relevant feedback is used to annotate it. One of the most important parts of the image retrieval system and clustering algorithm is determining the appropriate similarity criteria between images. Nowadays, the linear similarity criterion is mostly used to determine the similarity between images, but the nonlinear models can have much better performance due to their proximity to the human vision system, for this purpose, the KMRBF nonlinear similarity criterion is used to simulate vision. Humans and improvement of recovery results are suggested. Experiments on the Corel image database and satellite images show that the proposed method has good performance. According to the results obtained in the satellite image database, the YIQ color space has a higher accuracy (82.5%). Also, the three color spaces CIELab, HSV and YIQ have higher efficiency, because in these color spaces, luminance is separated from chrominance and these color spaces are closer to the human vision system. Manuscript profile -
Open Access Article
42 - A Combined Method for Dynamic Routing in Mobile Ad-Hoc Networks
Fatemeh Shabih Jalil azimpour Marziye DadvarWireless sensor networks are a large number of sensor nodes with limited energy in a scattered geographically limited area. Due to limited resources in wireless sensor networks, increasing the lifetime of the networks by reducing energy consumption is always considered. MoreWireless sensor networks are a large number of sensor nodes with limited energy in a scattered geographically limited area. Due to limited resources in wireless sensor networks, increasing the lifetime of the networks by reducing energy consumption is always considered. More nodes to send data to the central station energy consumption. Sequential routing based on clustering, this responsibility falls on the head, and this increases the energy consumption of cluster heads. In recent years later all the energy of cluster heads, routing protocols and a lot of clustering is proposed. The purpose of this study, the combination of clustering and routing in order to extend the lifetime of this type of network. For clustering of genetic algorithm with fixed and harmony search algorithm is used for routing. Customize search algorithm for routing in harmony, three criteria neighborhood, reducing energy consumption and proper distribution of energy consumption is taken into account. Harmony algorithm is proposed to establish a proper balance between the criteria listed will generate more efficient routes. Finally change the routing cluster heads in each round will be balancing energy consumption between nodes per cluster. The results of the tests show the superiority of 2.14% proposed increase in messaging as well as 24.84% Lifetime network protocol is DEEC. Manuscript profile -
Open Access Article
43 - Image Segmentation using spectral clustering based SuperPixel
Fatemeh Afsari Sholi Jalil azimpour Marziye DadvarOne of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in MoreOne of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in an image So that these pixels, the same qualities have with each other And the pixels adjacent parts, have different characteristics. The most important feature used in image segmentation, colors and features. In monochrome images, the gray level is considered as properties But color images, different color spaces used as a color feature. In this study, the color and texture features for image segmentation is considered. Clustering-based methods of are used in image segmentation methods and Gaussian function is similar measure in clustering images. Spectral clustering requires has high computational cost. To save time and accelerate the segmentation of images Using clustering with Super pixels will achieve optimal results And to achieve reliable results approximate and fuzzy algorithm is used. The proposed algorithm is applied on several standard image And the evaluation criteria,Evaluated and evaluated by the indicators are evaluated and compared. The results of the experiments were compared to other fragmentation methods, suggesting a 3.4% superiority in the segmentation accuracy of the proposed algorithm, and all the evaluation indicators of the study have increased to a satisfactory level. Manuscript profile -
Open Access Article
44 - Energy-Efficient Wireless Sensor Networks Using Flat Cluster-based Routing Protocol and Evolutionary Algorithms
masoud negahdari Marziye DadvarWireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a conc MoreWireless sensor networks have a large number of limited-energy sensor nodes dispersed in a finite area. Most node energies are used to send data to the central station. Due to the energy constraints in this type of grid, increasing life expectancy has always been a concern with decreasing energy consumption. The aim of this study is to provide surface clustering based on genetic algorithm in order to increase the life span of these networks. In proposed surface clustering, the geographic area is divided into three levels according to the radio range and the clustering of the nodes of each level is done individually. The cluster heads use more energy than other nodes to send information, so the proposed algorithm aims to reduce the number of cluster heads in order to increase the network lifetime. Finally, by changing the clusters in each routing round, there is a greater energy balance between the nodes. The results from the experiments indicate the superiority of the proposed algorithm in transmitting messages and network lifetimes over other similar protocols. Manuscript profile -
Open Access Article
45 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Mohsen Hamed Fatemeh HajianiIn the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that firs MoreIn the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well defined and does not overlap between the borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm. Manuscript profile -
Open Access Article
46 - A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Ebrahim Alibabaee Rouhollah AghajaniIn the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the W MoreIn the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well-defined and does not overlap between borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm. Manuscript profile -
Open Access Article
47 - The Effects of Graphic Organizer Strategy on Improving Iranian EFL Learners’ Vocabulary Learning
الناز شعاری Farahman Farrokhi -
Open Access Article
48 - Clustering Methods Survey in VANET on Roads and Mountain Trails with High Reliability
Mohammad Ali Pourmina Babak Mosharmovahed Sam Jabbehdari -
Open Access Article
49 - Efficient Modified-LEACH Protocol for Enhancing WSNs’ Lifetime
Mehdi Masoodi Mohsen Maesoumi Ehsan Akbari Sekehravani -
Open Access Article
50 - Improved Cuckoo Search-based Clustering Protocol for Wireless Sensor Networks
Hossein Sadeghian Mohammadreza Soltan Aghaei -
Open Access Article
51 - Distributed Routing Protocol in Wireless Sensor Networks through Mimetic Algorithm and Time-Sharing Approach to Select Cluster Head
Sahar Nassirpour Shayesteh Tabatabaei -
Open Access Article
52 - Biogeography-based Optimized Adaptive Neuro-Fuzzy Control of a Nonlinear Active Suspension System
Ali Fayazi Hossein Ghayoumi Zadeh -
Open Access Article
53 - A Survey on Applications of Machine Learning in Bioinformatics and Neuroscience
Narges Habibi Shahla Mousavi -
Open Access Article
54 - On the Applications and Techniques of Vehicular Ad-Hoc Networks
mohsen Norouzi ali arshaghi navid Razmjooy Mohsen ashourian -
Open Access Article
55 - Customer Behavior Analysis using Wild Horse Optimization Algorithm
Raheleh Sharifi Mohammadreza Ramezanpour -
Open Access Article
56 - Toward a High-Accuracy Hybrid System for Cardiac Patient Data Analysis using C-Means Fuzzy Clustering in Neural Network Structure
Mahmood Karim Qaseem Razieh Asgarnezhad -
Open Access Article
57 - Evaluation of soil salinity by analyzing Landsat-8 images and field Observations (Case study: Behesht-e- Gomshodeh, Fars province)
Mohamad Kazemi Fariborz Mohammadi Ali Reza NafarzadeganSoil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling MoreSoil salinity is considered as one of the potential environmental hazards. The purpose of this study was to find the best index and the most suitable relationship for estimating soil salinity and its mapping using remote sensing data. At the first step, random sampling was performed using fishnet method and surface soil electrical conductivity (EC) measurements. Then, the threshold levels (92%, 95%, and 98%) were applied to the output images of each indicator. The methodology included using the least squares fitting (LS-fit) technique and principal components analysis (PCA) for halite and gypsum minerals, determining the correlation between the output of indices and ground data, and performing clustering and factor analysis between EC and output images. In order to select the best model derived from Landsat-8 band combinations and the amount of salinity, collinearity test, Durbin-Watson test, and backward multivariate regression were employed. The Cohen‘s kappa coefficient was also applied to evaluate the multivariate regression formed by Landsat-8 bands. The performance of the indicators was evaluated based on four criteria of root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and R-squared (R2). The results of the factor analysis showed the smallest distance between the EC, salinity index (SI) and brightness index (BI). The SI with an amount of 0.89 had the highest Pearson correlation with EC. In the dendrogram diagram, SI index with EC was placed in a cluster, and the RMSE, MBE, MAE and R2 values of the SI index were estimated to be 0.16, 0.11, 0.12, and 0.76, respectively. Compared to the rest of the indicators and linear, multivariate regression (with Cohen‘s kappa coefficient of 60%,), the SI index has provided better outcomes. Manuscript profile -
Open Access Article
58 - Determination of homogenous areas for ecosystem services supply in the central part of Isfahan province
Sedighe Abdollahi Alireza Ildoromi Abdolrassoul Salmanmahini Sima FakheranDetermining and identifying homogeneous regions for ecosystem services supply is an effective and useful step in improving land management. Therefore, in this study, after quantifying and mapping ecosystem services, aesthetic value, recreational value, and noise polluti MoreDetermining and identifying homogeneous regions for ecosystem services supply is an effective and useful step in improving land management. Therefore, in this study, after quantifying and mapping ecosystem services, aesthetic value, recreational value, and noise pollution reduction, the K-Means clustering method was used to identify homogeneous areas of ecosystem service supply and homogeneous areas zoning was prepared in the GIS environment. To investigate the effective parameters on ecosystem services supply, the slope, altitude, population density, distance from access routes, distance from the river, percentage of available land uses and distance from the centre of the largest urban region were extracted for each homogeneous area or cluster. Based on the Davis-Bouldin validation index, the optimal number of clusters was 4. Cluster number two with the area of 686.27 Km2 was the largest, while cluster number one with the area of 119.75 Km2 was the smallest in the area. Investigation of environmental-social parameters showed that land use has the highest impact on ecosystem services supply. The results showed that there is a direct relationship between these parameters and ecosystem services supply in each cluster. Based on the results of this study, investigation of homogeneous areas of ecosystem services can be effective to improve land use planning and management. Manuscript profile -
Open Access Article
59 - Use data mining to identify factors affecting students' academic failure
Mahmood Najafi Mehdi Afzali Mahmood MoradiKnowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is applic MoreKnowledge extraction is one of the most significant problems of data mining. The principles raised in if-then format can be turned into real numbers in each section- as values which could be included in dataset. The suggested method in the present dissertation is application of decision tree algorithms, clustering and forum rules for extraction of final rules. In the suggested method, extraction of rules is defined as an optimization problem and objective was obtaining a rule of high confidence, generalization and understandability. The suggested algorithm for extraction of rules was obtained from and tested based on a dataset of educational failure of 256 art school students living in Zanjan. The results suggested that the j48 algorithm in decision tree and accuracy of 0.95 is the choice for the dataset of educational failure. Data clustering was done by K-Main algorithm with confidence coefficient of 0.95. After all, obtaining rules of high confidence coefficient was done based on forum rules from Apriori algorithm for the whole datasets. The results of present study could be used for inhibition of educational failure of students, improved quality of relationship of parents and authorities with students and enhancing the education they receive. Manuscript profile -
Open Access Article
60 - Presenting a model for Multi-layer Dynamic Social Networks to discover Influential Groups based on a combination of Developing Frog-Leaping Algorithm and C-means Clustering
lida naderloo Mohammad Tahghighi SharabyanIntroduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, MoreIntroduction: The current research examines a more complex social network called a multi-layered social network. Recently, the concept of the multilayer network has emerged from the area of complex networks, under the domain of complex systems. In the field of big data, simple and multi-layered social networks can be found everywhere and in all fields. The estimation of the importance of each node in these two types of networks is not the same, and weighting the nodes is very necessary to control the network. For this purpose, the relationship between the characteristics of the nodes and the relationship with the network structure should be examined. To find the degree of each node in the system function, parameters like reliability, controllability, and power should be considered. In this paper, a model for dynamic multi-layer social networks to discover influential groups, based on the combination of evolutionary frog jump algorithm and C-means clustering, has been presented.Method: Once data are collected, they were cleaned and normalized so that the desired data leads to the identification of effective individuals and groups. The decision matrix is constructed based on these data. Using this matrix, identification, and clustering (based on fuzzy clustering) were conducted, and the importance of the groups was also determined to determine influential people and groups in social networks. The Jumping frog algorithm was used to improve the detection of influential parameters.Results: In the evaluation and simulation of the clustering part, the proposed method was compared with the K-means method and the balance value of the method in cluster selection was 5. It should be noted that the proposed method showed better improvement compared to the compared methods. Also, the evaluation of the accuracy criterion of the proposed method has improved by 3.3 compared to similar methods and recorded an improvement of 3.3 compared to the basic M-ALCD method.Discussion: In this paper, a multi-layer local community detection model is proposed, which is based on structure and feature information. This model can use the information of the characteristics of the nodes and the information of the strength of similarity that is revealed by social exchanges and improves the accuracy of community detection in Improve multilayer networks. Due to its modularity and computational efficiency, this algorithm performs better on most data sets, unlike the classic multi-layer and global community detection algorithms Manuscript profile -
Open Access Article
61 - A New Approach to The University Course Timetabling Problem based on Clustering Algorithms & Fuzzy Multi-Criteria Decision Making
behzad mohammadkhani hamed Babaei, davod eskandari mohammadreza hasanzadeh Introduction: The UCTTP problem is a hybrid optimization problem that belongs to the NP-hard class, hence determining the optimal or analytical solution of this problem is challenging. This problem, which occurs at the beginning of the university semester, involve More Introduction: The UCTTP problem is a hybrid optimization problem that belongs to the NP-hard class, hence determining the optimal or analytical solution of this problem is challenging. This problem, which occurs at the beginning of the university semester, involves allocating events (courses, faculty, and students) to a number of time slot and specific number of rooms. The UCTTP problem must satisfy both hard and soft constraints so that feasible time tables are obtained after complete and correct satisfaction of all hard constraints. Satisfaction of soft constraints is merely for the quality improvement of the produced feasible time tables and unlike hard constraint their satisfaction is not mandatory. Another important issue associated with this problem is the multiplicity and variety of constraints (hard and soft) that are completely case dependent. The soft constraints considered by each solution (schedule) are evaluated by the penalty function, which is obtained by a summation operator. In this operator, a weight is assigned to each soft constraint, and according to these weights, a penalty function is obtained, the output of the penalty function is used in the objective function which yields final solutions. After obtaining all the final solutions, the schedule tables that have no collisions, that is, satisfy all the strict constraints, and secondly, have a higher value in terms of the value of the objective function are selected.Method: According to the simulation results, it can be said that in using clustering algorithms, the efficiency of fuzzy C-clustering algorithm in minimizing resource loss (surplus) and descending satisfaction of soft constraints of common lecturers of faculties is higher than funnel clustering algorithm and the K-mean.Findings: The optimal ratio of the number of applied penalties for the violations of lecturers’ soft constraints and the percentage of violations of lecturers among the fuzzy multi-criteria decision comparison algorithms, local search and genetics, as well as the combination of these algorithms are related to the combination of two comparison algorithms (i.e. fuzzy multi-criteria decision making and local search).Discussion and Conclusion: We observed that with regards to the percentage of satisfaction of soft constraints of common lecturers, the combination of local search algorithms with C-fuzzy clustering shows the best performance and f fuzzy multi-criteria decision comparison algorithm has the worst performance. Manuscript profile -
Open Access Article
62 - Unsupervised Domain Adaptation for image classification based on Deep Neural Networks
Amirfarhad Farhadi Mitra Mirzarezaee Arash Sharifi Mohammad TeshnehlabIntroduction: Domain adaptation has become an important issue today. A high percentage of data processing domain adaptation is done with a significant percentage of studies related to deep learning. Traditional methods often ignore the distance between the intra-class i MoreIntroduction: Domain adaptation has become an important issue today. A high percentage of data processing domain adaptation is done with a significant percentage of studies related to deep learning. Traditional methods often ignore the distance between the intra-class in source domain and target domain. As a result, models can be sensitive to outliers and noisy data, additionally increasing the negative transfer in the model. This method applied GAN to extract appropriate features and then used Fuzzy c-means to cluster train datasets in the target domain. Finally, based on the WMMD metric and CNN, the model estimates the final label data. Five real datasets are selected to generate eight transfer tasks. The results show that the superiority of the proposed model lies in transferring more knowledge from the source domain to the target domain.Method: In this approach, firstly based on GAN extracting features from source domains and the target domain (without labels), then label estimation by Fuzzy c-means clustering, finding the center of Fuzzy c-means on target domain data, new data points with labels in target domain as a new input to feature extraction module and regenerate features by GAN based on new pseudo labels. Afterward, we apply WMMD metrics based on CNN to ultimately assign labels for the target domain. Consequently, classification tasks have been done.Results: Empirical results on various benchmark datasets showcase the exceptional performance of the proposed method compared to state-of-the-art DA approaches, validating the proposed Deep-Learning Unsupervised Domain Adaptation approach efficacy. Overall, the approach shows potential for advancing domain adaptation research by offering an efficient and resilient approach for addressing domain shifts in real-world applications. Experimental results on visual object recognition and a digit dataset reveal that the proposed algorithm is robust, flexible, and significantly superior regarding accuracy compared to the baseline DA approaches. Based on the three and combined digit datasets, 1.7% and 2.4% accuracy improvement are achieved, respectively, compared to the best baseline DA approach results.Discussion: In this research, we addressed the challenging issues of outlier and negative transfer in the context of domain adaptation. Despite significant progress in domain adaptation techniques, outliers and negative transfer instances continue to hinder models' generalization performance across different domains. Based on DNNs and the WMMD metric, our proposed method was designed to mitigate these issues and effectively enhance knowledge transfer between domains. Manuscript profile -
Open Access Article
63 - An algorithm for clustering of insurance products and users in a collaborative filtering-based insurance recommender system and evaluating its performance based on the insurance recommendation
Marzieh Amini Shirkoohi Mohammadreza YamaghaniIntroduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance be MoreIntroduction There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance because of various type of insurance and lack of enough information of insurance service. Choosing the proper insurance service always related to people personal and social features Method Prediction of customer’s insurance selection according to people personal and social property especially thier financial condition play vital role. On one hand Prediction of insurance type can help people who want to utilize insurance service. On the other hand this prediction can facilitate process of insurance for Insurers too. There are multiple important mechanisms and factors like customers clustring, analyze each class feature, detection of popular insurance in each class and using Collaborative filtering technique to offer best insurance that can influence on process of decision and selection the suitable insurance. Results The total precision value of the proposed method is 89.98% for joint insurances of similar users. Also, the total value of the F-measure of the proposed method for joint insurances between similar customers is 87.13%. Discussion Customer behavior can be predicted by available data of people’s personal and social features and type of insurance that they are chosen and rate of their satisfactions. K-means clustring algorithm and recommender systems Techniques like Collaborative filtering are two significant mechanisms to implement prediction of customer’s behaviors. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Manuscript profile -
Open Access Article
64 - Temporal Graph Partioning for Clustering in Tagging Systems
Ali Akbar Alah Daghi Mehdrad Jalali Seyyed Javad Seyed Mahdavi ChabokToday, information growth in the world of Web 2.0, due to the vast amount of data and change of some concepts over time, there is a lot of unnecessary and irrelevant information to what users are looking for. In this paper, we for solve this problem, propose temporal cl MoreToday, information growth in the world of Web 2.0, due to the vast amount of data and change of some concepts over time, there is a lot of unnecessary and irrelevant information to what users are looking for. In this paper, we for solve this problem, propose temporal clustering of tags for systems that use tags as a metadata and are changing over time. The way we use for clustering, is temporal graph partitioning tags by changing the tag similarity weights during the time, then clustering will change and adapt itself with the changes. To demonstrate the effectiveness of this approach, we implemented it on a data set of MetaFilter site and compared it with similar methods. The results show that our proposed methods improved F-Measure out 24% compared to best clusters in the same way, over time, has improved and its concept is associated both with the past concepts and the newsletter. Manuscript profile -
Open Access Article
65 - The Effect of JCPOA on the Network Behavior Analysis of Tehran Stock Exchange Indexes
Salman Abbasian-Naghneh Reza Tehrani Mohammad Tamimi -
Open Access Article
66 - An Integrated Entropy/VIKOR Model for Customer Clustering in Targeted Marketing Model Design (Case Study: IoT Technology Services Companies)
Hossein Teimouri Jalil Gharibi Ali Hossein Zadeh Alireza Pooya -
Open Access Article
67 - Computing the Efficiency of Bank Branches with Financial Indexes, an Application of Data Envelopment Analysis (DEA) and Big Data
Fahimeh Jabbari-Moghadam Farhad Hosseinzadeh Lotfi Mohsen Rostamy-Malkhalifeh Masoud Sanei Bijan Rahmani-ParchkolaeiIn traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increa MoreIn traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and …. may not prove to be economical. The key objective of the proposed algorithm is to segregate the efficient units from that of the other units. In order to gain access to this objective, effectual indexes were created; and taken to assist, in regards the DEA concepts and the type of business (under study), to survey the indexes, which were relatively operative. Subsequently, with the help of one of the clustering techniques and the ‘concept of dominance’, the efficient units were absolved from the inefficient ones and a DEA model was developed from an aggregate of the efficient units. By eliminating the inefficient units, the number of units which played a role in the construction of a DEA model, diminished. As a result, the speed of the computational process of the scores related to the efficient units increased. The algorithm designed to measure the various branches of one of the mercantile banks of Iran with financial indexes was implemented; resulting in the fact that, the algorithm has the capacity of gaining expansion towards big data. Manuscript profile -
Open Access Article
68 - Designing a Model to Investigate the Process of Forming Cluster Fluctuations According to the Fractal Structure in Financial Markets
Amin Amini Bashirzadeh Shahrokh Bozorgmehrian Bahareh Banitalebi DehkordiCluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current researc MoreCluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current research, by using factor-based model design and considering a new interactive mechanism called multi-level clustering, the formation process of cluster fluctuations was investigated with regard to the fractal structure of financial markets. For this purpose, the daily information of the final price of 150 shares that were accepted in the Tehran Stock Exchange, after the final screening, was entered in 5 sections with 30 shares in each section, in the desired model, and they were aggregated in three stock levels., sector and market were measured. Due to the fact that some investors have a longer investment horizon in the stock market and due to the limitation of the investigated time period, the maximum investment horizon of 1000 days has been determined in the model.In addition, the data studied in the research model are from August 2012 to September 2018. The findings of the research showed that the intensity of the tendency of collec-tive behavior at the sector level is much stronger than at the market level. In addition, based on the findings of the research, it was determined that the distribution of simulation eigenvalues in three levels is significantly similar to the distribution of real data. Also, according to the investor's time horizon, the studied series always has a long-term memory for fluctuations. In addi-tion, it was found that long-term memory is directly related to fractal dimen-sions. The findings of this research, in addition to providing a new insight into the space-time correlations of financial markets, show that multi-level conglomeration is one of the mechanisms for creating the microscopic mi-crostructure of such markets. In other words, multi-level collective behavior is an important factor in the occurrence of cluster and fractal fluctuations in the market, and therefore, it should be considered from this point of view in the interpretation of the concept of risk and the definition of risk manage-ment strategies. Manuscript profile -
Open Access Article
69 - Typology of Iranian Consumers based on Values System and Lifestyles: A Clustering Method
Zahra Saneian seyed mohammad Tabatabai-Nasab -
Open Access Article
70 - PRFM Model Developed for the Separation of Enterprise Customers Based on the Distribution Companies of Various Goods and Services
Mohammad Mahdi Hajmohamad Narges Rahimi Behzad Sasanizadeh -
Open Access Article
71 - Innovation Capability Based on Clustering and Ranking Approach (Case Study: Food and Beverage Industries of Urmia Metropolis)
Khadijeh Bahrami Houshang Taghizadeh Morteza Honarmand Azimi -
Open Access Article
72 - Predicting Customer Churn Using CLV in Insurance Industry
Vahid Dust Mohammadi Amir Albadvi Babak Teymorpur -
Open Access Article
73 - Presenting a model for statistical process control in order to optimize efficiency and quality in manufacturing industries
abbas morovvati Seyed Jalaledin Hosseini Ghoncheh Hasan HalehIn this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and MoreIn this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and component industries are considered as the main body of the country's industries for case study and implementation. Clustering techniques are used to discover factors affecting efficiency. And then using decision tree algorithms to predict efficiency and quality in these industries, and in the final stage, control charts of dispersion and average variables are used to draw control charts. The comparison table of the parameters is prepared by the output of the Clementine software, and RapidMiner software is used in the neural network section. The results obtained from the identification of influencing and forecasting factors are close to the target values from a technical point of view, and the control charts are consistent with the technical control limits of the characteristics and are useful for optimizing the target value, which is efficiency and quality. Manuscript profile -
Open Access Article
74 - The Investigation and Analysis of Relationship Industrial Clustering and the improvement of Learning Capabilities in the Printing / Publishing and Furniture Industries in Qom Metropolis
Hashem Dadashpoor Mahdi Pourtaheri Abolfazl MoarrefiThe industrial cluster approach is one of the successful approaches for regional development, Causes the improvement of learning capabilities in the urban and regional environment, using physical, social-cultural, economic and institutional proximities of the surroundin MoreThe industrial cluster approach is one of the successful approaches for regional development, Causes the improvement of learning capabilities in the urban and regional environment, using physical, social-cultural, economic and institutional proximities of the surrounding environment. This paper aims to The Investigation and Analysis of Relationship Industrial Clustering And the improvement of learning capabilities in the Printing / Publishing and Furniture Industries in Qom’ Metropolis. This is a survey-based study (184 completed questionnaires) whit applied approach which required information gathered through the library study and fieldwork. The Study’s sample size is based on the “minimum sample size for population table” ( 94 firms in the furniture industry and 90 firms in the publishing industry) and “Stratified random sampling method”. Furthermore, apparent validity, content validity and reliability of test are verified by “using experts”, “KMO and Bartlett's sphericity test” and “Cronbach's alpha test” respectively. Research instruments include learning capability questionnaire with 10 items and institutional cluster proximities with 36 items. Using Pearson correlation, data obtained by questionnaires are analyzed. Findings indicated that: printing/ publishing industry cluster with social-cultural and economic proximities industrial cluster furniture with only social-cultural proximity and general characteristics of both industrial firm’s (education manager/owner firms, the number of workers, the age of establishing and average annual sales of the firm) indicate a significant relationship between improvement of learning capabilities. Moreover, it seems that institutional, physical and economical proximities in the furniture industry and institution, physical proximities in printing/publishing industry haven’t a significant role in the learning capabilities enhancement. This study recommends that proximities of institutional, physical, economic, social-cultural industries should be strengthened for the success of these clusters and improvement of learning capabilities and finally stimulates regional development. Manuscript profile -
Open Access Article
75 - Improvement of adaptive neuro-fuzzy controller using by fuzzy clustering means algorithm for control of vehicle suspension system
Gholamreza Bamimohamadi Mehdi SalehiSuspension system is an important part of vehicle whose main role is to separate the vehicle body from road induced vibrations. Design and control of a suspension system that can adapt to different road conditions with high flexibility is essential. In this study, data MoreSuspension system is an important part of vehicle whose main role is to separate the vehicle body from road induced vibrations. Design and control of a suspension system that can adapt to different road conditions with high flexibility is essential. In this study, data were collected from three types of road conditions with different roughness coefficients in various forward speeds for training a suspension model. Primarily, dynamic equations were derived for a linear full model suspension system. Then, with the use of fuzzy system simulation data, two adaptive neuro-fuzzy controllers namely Grid Partitioning and Fuzzy Clustering were trained. Finally, four methods were evaluated and the results showed that decrease in linear deflection and acceleration of vehicle body is higher in adaptive neuro-fuzzy controller by Subtractive Clustering compared to other systems. Manuscript profile -
Open Access Article
76 - Generating Optimal Timetabling for Lecturers using Hybrid Fuzzy and Clustering Algorithms
hamed babaei amin hadidi -
Open Access Article
77 - Geochemical pattern recognition for Cu-Au Deposit Based on Self-Organizing Map (SOM) and Fuzzy K-means Clustering (FKMC) in Meshginshahr, NW of Iran
Aynur Nasseri -
Open Access Article
78 - Using Cluster and Feature Weighted FCM for reducing ANFIS Rules
Solmaz Abdollahizad -
Open Access Article
79 - Increasing Lifetime Using Whale Optimization Routing Algorithm in Wireless Sensor Networks
Hassan Nouri Esmaeil Zeinali -
Open Access Article
80 - Selecting Optimal k in the k-means Clustering Algorithm
Mojtaba Jahanian Abbas Karimi Faraneh Zarafshan -
Open Access Article
81 - Fuzzy Clustering Algorithm to Identify Sybil Attacks in Vehicular ad Hoc Networks
Mahdi Maleknasab Ardekani Mohammad Tabarzad Mohammad Amin Shayegan -
Open Access Article
82 - Cluster-Based Image Segmentation Using Fuzzy Markov Random Field
Peyman Rasouli Mohammad Reza Meybodi -
Open Access Article
83 - Detection of Breast Cancer Progress Using Adaptive Nero Fuzzy Inference System and Data Mining Techniques
Hengameh Mahdavi -
Open Access Article
84 - Modified Convex Data Clustering Algorithm Based on Alternating Direction Method of Multipliers
Tahereh Esmaeili Abharian Mohammad Bagher Menhaj -
Open Access Article
85 - Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems
Leily Sheugh Sasan H. Alizadeh -
Open Access Article
86 - A Hybrid Geospatial Data Clustering Method for Hotspot Analysis
Mohammad Reza Keyvanpour Mostafa Javideh Mohammad Reza Ebrahimi -
Open Access Article
87 - Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Rasool Azimi Hedieh Sajedi -
Open Access Article
88 - Drawing and analyzing the synonym map of digital entrepreneurship in art universities
Soheila panahzadeh Soheila Khishtandar Farhad Nejhad Haji Ali IraniDigital entrepreneurship is an entrepreneurial approach that is suitable for the digital age and has become a dominant discourse in the field of entrepreneurship due to its increasing speed. Given the richness of studies and research background on digital entrepreneursh MoreDigital entrepreneurship is an entrepreneurial approach that is suitable for the digital age and has become a dominant discourse in the field of entrepreneurship due to its increasing speed. Given the richness of studies and research background on digital entrepreneurship, the use of scientific methods and co-occurrence analysis can provide a comprehensive and pervasive picture of this field. Therefore, this study aims to draw a co-occurrence map of digital entrepreneurship. This applied research uses a scientific approach and analyzes data using co-occurrence analysis. The data was collected from the Science Direct database from 2015 to the end of 2022, and VOSviewer software was used to draw the co-occurrence map. In total, 19 keywords, 4 clusters, and 68 links with a strength of 152 were identified. The concepts of digital entrepreneurship, knowledge management, big data, business intelligence, strategic management, organizational learning, social networks, competitive advantage, and optimization are the most common words in this field. Therefore, art universities can provide a platform for digital entrepreneurship through a strategic approach using big data analysis and activities on social networks. Knowledge management plays a fundamental role in this regard and can guarantee the competitive advantage of universities through digital entrepreneurship Manuscript profile -
Open Access Article
89 - Assessing Credit Risk in the Banking System Using Data Mining Techniques
Nima Hamta Mohammad Ehsanifar Bahareh MohammadiA credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments. The objective of this paper is recognition of the factors that effect on credit risk and presenting a model for prediction of credit risk and legal customer MoreA credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments. The objective of this paper is recognition of the factors that effect on credit risk and presenting a model for prediction of credit risk and legal customer credit ranking that are applicant of Sepah bank facilities in Dezfool city and the method of Clustering, Neural Network and Supporter Vector Machine has been used in the current study. Accordingly necessary investigations have been done on financial and nonfinancial data by means of a simple random sample of 200 legal customers that were applicant of bank facilities. In the this paper, 27 descriptive variable that include financial and nonfinancial variables were investigated and finally available variables 8 effective variables on credit risk were selected by means of bank experts judges that were separated by data collection Clustering method in to some groups (Clusters) in the someway that data in one Cluster were considering other points in other Clusters had more similarity. Also selected variables with 3 layers perceptron Neural Network input vector entered the model and finally by means of Support Vector Machine was presented in order to bank legal customers’ financial operation prediction. The obtained results of Neural Network model and Supporter Machine indicate that Neural Network model has mire efficiency in legal customers’ credit risk prediction and credit ranking. Manuscript profile -
Open Access Article
90 - A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection
farshad faezy razi Naeimeh Shadloo -
Open Access Article
91 - Integrating AHP and data mining for effective retailer segmentation based on retailer lifetime value
Amin Parvaneh Hossein Abbasimehr Mohammad Jafar Tarokh -
Open Access Article
92 - Using the Hybrid Model for Credit Scoring (Case Study: Credit Clients of microloans, Bank Refah-Kargeran of Zanjan, Iran)
Abdollah Nazari Mohammadreza Mehregan Reza Tehrani -
Open Access Article
93 - A Data Mining approach for forecasting failure root causes: A case study in an Automated Teller Machine (ATM) manufacturing company
Seyedehpardis Bagherighadikolaei Rouzbeh Ghousi Abdolrahman Haeri -
Open Access Article
94 - A bi-objective mathematical model for the patient appointment scheduling problem in outpatient chemotherapy clinics using Fuzzy C-means clustering: A case study
Masoud Rabbani Alireza Khani amirreza Zare niloofar Akbarian-Saravi -
Open Access Article
95 - Residential appliance clustering based on their inherent characteristics for optimal use based K-means and hierarchical clustering method
Shima Simsar Mahmood Alborzi Ali Rajabzadeh Ghatari Ali Yazdian Varjani -
Open Access Article
96 - Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem
esmaeil Mehdizadeh reza Tavakkoli Moghaddam -
Open Access Article
97 - A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
Jhila Nasiri Farzin Modarres Khiyabani NIma Azorbaarmir ShotorbaniMethods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whal MoreMethods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions. Manuscript profile -
Open Access Article
98 - A New Approach to Define the Number of Clusters for Partitional Clustering Algorithms
Huliane Silva Benjamın Ren Callejas Bedregal Anne Canuto Thiago Batista Ronildo MouraData clustering consists of grouping similar objects according to some characteristic. In the literature, there are several clustering algorithms, among which stands out the Fuzzy C-Means (FCM), one of the most discussed algorithms, being used in different ap MoreData clustering consists of grouping similar objects according to some characteristic. In the literature, there are several clustering algorithms, among which stands out the Fuzzy C-Means (FCM), one of the most discussed algorithms, being used in different applications. Although it is a simple and easy to manipulate clustering method, the FCM requires as its initial parameter the number of clusters. Usually, this information is unknown, beforehand and this becomes a relevant problem in the data cluster analysis process. In this context, this work proposes a new methodology to determine the number of clusters of partitional algorithms, using subsets of the original data in order to define the number of clusters. This new methodology, is intended to reduce the side effects of the cluster definition phase, possibly making the processing time faster and decreasing the computational cost. To evaluate the proposed methodology, different cluster validation indices will be used to evaluate the quality of the clusters obtained by the FCM algorithms and some of its variants, when applied to different databases. Through the empirical analysis, we can conclude that the results obtained in this article are promising, both from an experimental point of view and from a statistical point of view. Manuscript profile -
Open Access Article
99 - Improving Lifetime of Strategic Information Network in Oil Supply Chain
mohammad ali afshar kazemi mohammad hossein darvish motevally Mahmood Darvish Motevalli -
Open Access Article
100 - Identification and Management of the Main Challenges in Saffron Industry in Iran
Samuel Yousefi Jamileh Hayati Sargiz Yousefi -
Open Access Article
101 - Studying the effect of evapotranspiration on the temperature near the surface of the earth using the LCZ algorithm in Tehran and Alborz provinces.
Ali teymoori Saeed Jahanbakhsh Ali mohammad Khorshid DostEarth surface temperature changes can affect various environmental factors and elements, and by affecting bioclimatic comfort, energy consumption, plant and animal phenology, air pollutant concentration, soil and air moisture density, and other environmental factors can MoreEarth surface temperature changes can affect various environmental factors and elements, and by affecting bioclimatic comfort, energy consumption, plant and animal phenology, air pollutant concentration, soil and air moisture density, and other environmental factors can also be affected by these factors.In this research, in order to investigate the role of actual evaporation-transpiration on the temperature of the earth's surface at ten meters, the variables of carbon monoxide, water vapor density, nitrogen dioxide, sulfur dioxide, ozone, water vapor pressure and wind speed in twelve uses With the help of LCZ algorithm, determined for Tehran and Alborz provinces, have been studied and investigated.The studied area has a high potential for the occurrence of thermal islands due to special geographical conditions such as location, variety of man-made structures and special ecological conditions. In this research, according to the impact of the studied variables and the need to examine the relationships between them, hierarchical clustering in a combined manner, path analysis method and remote sensing techniques have been used. According to the obtained results, among the studied variables, actual evaporation-transpiration in none of the uses had a significant effect on increasing or decreasing the temperature of the ground surface at 10 meters. Water vapor pressure in class 14 (areas with grassy vegetation) shows the greatest effect in increasing the temperature of the earth's surface.Based on the obtained results, the type of use is highly dependent on the impact of the studied variables on the surface temperature of the earth. Manuscript profile -
Open Access Article
102 - Assessment of Different Methods of The Estimation of Reference Evapotranspiration By FAO’s Evaporation Pan Method in Catchment Basin of East And South Eastern of The Country
Javad Khoshhal Hamid Zare Abyaneh Alireza JoshaniAssessment of reference evapotranspiration to estimate aqueous Plants’ needs, to manage aquatic and drainage plans and irrigation timing of plants are some necessities of agriculture section. So in this research to find the best model for estimation of refere MoreAssessment of reference evapotranspiration to estimate aqueous Plants’ needs, to manage aquatic and drainage plans and irrigation timing of plants are some necessities of agriculture section. So in this research to find the best model for estimation of reference evaporation and transpiration for catchment basin of East and South Eastern of the Country, relying on clustering method and considering data made by 66 weather stations, reference evaporation and transpiration made by evaporation basin was assessed and compared to evaporation and transpiration measures of 8 equations based on statistical parameters: r, t, d, MAE, MBE, NRMSE. Based on the results, considering the different time dimensions, the methods Hargreaves - Samani, Blaney – Criddle – FAO 24, Turk and Priestley – Taylor, have the best conformity with reference evapotranspiration values resulted by evaporation pan. Also the results of research show that in seasonal scale, summer had the minimum and winter had the maximum estimation error of ETO .On the other hand, monthly scale had the minimum error compared to seasonal scale. This result show decrease in ETO error in small time scales Manuscript profile -
Open Access Article
103 - Synoptic Analysis of Dust From The Warm Half of The Year in Southern Khorasan Province
Zohra Ahmadi Reza Doostan Abbas MofidiDust is the first Natural hazard in desert and semi-desert world and Iran. In order to identify the days of the dust of South KHorasan, the daily amount of horizontal visibility, wind speed and direction in the spring and summer of 1991 -2008 were received from Meteorol MoreDust is the first Natural hazard in desert and semi-desert world and Iran. In order to identify the days of the dust of South KHorasan, the daily amount of horizontal visibility, wind speed and direction in the spring and summer of 1991 -2008 were received from Meteorological Organization. Then, based on Shao and Dong index, the days were extracted with dust. In order to determine atmospheric pattern led to the dust, the daily Geopotential height 500 HP from the National Center for Environmental Prediction America/ National Center for Atmospheric Research (NCEP / NCAR), was prepared. In this study, to determine the pressure patterns were used the principle component analysis approach in the state S and hierarchical clustering (ward). Then the composite maps of vorticity, geopotential height, and sea pressure and flow pattern for each pattern produced and were analyzed. The results showed that the tow dominant synoptic patterns respectively are the summer pattern with a subtropical high on Iran and high low on Pakistan in the high levels of the atmosphere and the pressure difference between the South-East of Iran (low pressure) and the Caspian Sea (high pressure) on the land surface. In this pattern, the wind from the East of the Caspian Sea and Turkmenistan desert in the dry lands and deserts to eastern Iran flows and causing dust. This atmospheric conditions is this Connection with the 120-day winds of Sistan and Baluchestan in Iran's East. In the spring pattern, the trough of westerly winds in the center and east of Iran in the high level of atmosphere and low pressure on the ground in this area has led to unstable weather conditions in the central deserts of Iran and the West the province that these flows transfer dust particles and reduce visibility. As well as intense pressure difference between the centers lead to dust storms in the South Khorasan province Manuscript profile -
Open Access Article
104 - The Study of some Citrus varieties in Northern of Iran by molecular ISSR markers
Babak Babakhani yalda NaghashiRecognition of genetic diversity and kinship relationships in Citrus is necessary for planning and applying breeding programs, preserving biodiversity, recording new cultivars, and performing molecular studies. In this study, the genetic diversity of 29 varieties of Cit MoreRecognition of genetic diversity and kinship relationships in Citrus is necessary for planning and applying breeding programs, preserving biodiversity, recording new cultivars, and performing molecular studies. In this study, the genetic diversity of 29 varieties of Citrus including: oranges, mandarins, sour orange, pummel, and natural types were investigated by using ISSR marker. In total, 97 bands were obtained using eight primers in which 78 bands were polymorph. The highest and the lowest polymorphism were in ISSR-8 and ISSR-5 with 90% and 73%, respectively. The average polymorphism information content (PIC) was 0.18, which the highest belonged to ISSR-6 and ISSR-8 (0.27) and the lowest belonged to ISSR-1 (0.12). Dendrogram resulting from cluster analysis of UPGMA method with simple matching similarity coefficient classified varieties into five distinct groups. Pummelo was distinguished from the other genotypes in a single cluster. Unshiu mandarin (Sugiyama) was classified into a group and separated from Clemantine mandarin (Nules). All genotypes including Siavaraz 1, Siavaraz 2, Siavaraz 3, Siavaraz 4, natural types, Parson brown orange and Washington navel orange were clustered into the same group and showed high similarity to gather. The studey of molecular marker can provide useful information about the level of polymorphism and variation in citrus fruits which indicating it’s apply in detection of citrus germplasm. Manuscript profile -
Open Access Article
105 - A Cutting-edge Metaheuristic Approach Based on The Manifold Distance for Energy-efficient Clustering in WSN
Faraein Aeini -
Open Access Article
106 - Identification and Clustering Outsourcing Risks of Aviation Part- Manufacturing Projects in Aviation Industries Organization Using Kmeans Method
Alireza Abbasi Mehrdad Nikbakht -
Open Access Article
107 - Implementation of Agglomerative Hierarchical Clustering Algorithm Applying the Map-Reduce Parallel Approach
Fahimeh Tavakoli Faramarz Safi-EsfahaniThe map-reduce model is a method for executing large data applications. It is also a parallel programming model for writing applications that can be executed on the cloud. Organizations are increasingly producing data that is generated by business processes, user activi MoreThe map-reduce model is a method for executing large data applications. It is also a parallel programming model for writing applications that can be executed on the cloud. Organizations are increasingly producing data that is generated by business processes, user activities, website tracking, sensors, finance, accounting, and more. Data clustering algorithms are used as tools for analyzing large volumes of data. The main purpose of these algorithms is to categorize data into clusters so that the data objects in each cluster are more similar. In this paper, a dense hierarchical clustering algorithm, one of the data mining techniques, is implemented using map-reduce design and then the results of this algorithm are compared with the usual one. Experiments show that runtime decreases with increasing input data size. The runtime of the algorithm improved by 16.80% for the 200 data-point dataset, and 29.26% for the dataset with 1000 data points. The percentage of CPU usage in the parallel system also increased from 22% to 94%. Manuscript profile -
Open Access Article
108 - Voiced-Unvoiced-Silence Detection of Speech Signal using Combined Spectro-Temporal Features
Nafiseh Esfandian -
Open Access Article
109 - A Hybrid Algorithm for Fault Diagnosis using Fuzzy Clustering Tools
Adrián Rodríguez Ramos Pedro Juan Rivera-Torres Antônio José da Silva Neto Orestes Llanes-Santiago -
Open Access Article
110 - بهبود خوشه بندی خودکار با بکارگیری الگوریتمهای فراابتکاری چند هدفه با ارائه معیار ارزیابی جدید و کاربرد آن در ریسک اعتباری
مجید محمدی راد مهدی افضلی -
Open Access Article
111 - An Ant-Colony Optimization Clustering Model for Cellular Automata Routing in Wireless Sensor Networks
عارف صفری -
Open Access Article
112 - Genetic Structure and Marker-Trait Associations in Parental Lines of Sunflower (Helianthus annuus L.)
Hossein Zeinalzadeh-Tabrizi Arash Hosseinpour Mehdi Ghaffari Kamil Haliloglu -
Open Access Article
113 - An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks
Hasan Rabani Farhad Soleimanian Gharehchopogh -
Open Access Article
114 - An Improved W-LEACH Routing Protocol in Wireless Sensor Network
Farhad Rad Zahra Moghtaderinasab Hamid Parvin -
Open Access Article
115 - Providing a Method to Identify Malicious Users in Electronic Banking System Using Fuzzy Clustering Techniques
Leila Pourabdi Ali Harounabadi -
Open Access Article
116 - Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization
Farhad Ramezani -
Open Access Article
117 - High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
Farnaz Hoseini Ghader Mortezaie Dekahi -
Open Access Article
118 - An Improved SSPCO Optimization Algorithm for Solve of the Clustering Problem
Rohollah Omidvar Amin Eskandari Narjes Heydari Fatemeh Hemmat Mohammad Feyli -
Open Access Article
119 - A New Dynamic Clustering Control Method in Wireless Sensor Networks
Rohollah Omidvar Hamid Parvin Farhad Rad Amin Eskandari Ali Chamkoori -
Open Access Article
120 - A Balanced Distribution Method Of Cluster Head Selection For Clustering In Wireless Sensor Networks
Marzieh Gholami Mehdi Golsorkhtabaramiri -
Open Access Article
121 - Feature Selection And Clustering By Multi-objective Optimization
Seyedeh Mohtaram Daryabari Farhad Ramezani -
Open Access Article
122 - Presenting a Fast Classifier Based on Unsupervised Learning for Diagnosis Diseases
Najmeh Hosseinpour Afzal Ghaseimi -
Open Access Article
123 - FFS: A F-DBSCAN Clustering- Based Feature Selection For Classification Data
Nasim Eshaghi Ali Aghagolzadeh -
Open Access Article
124 - Stock Trading Signal Prediction Using a Combination of K-Means Clustering and Colored Petri Nets (Case Study: Tehran Stock Exchange)
Ali Ghorbani Mahmood Yahyazadehfar Seyyed Ali Nabavi Chashmi -
Open Access Article
125 - An improved opposition-based Crow Search Algorithm for Data Clustering
Rogayyeh Jafari Jabal Kandi Farhad Soleimanian Gharehchopogh -
Open Access Article
126 - Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms
Ali Pazahr -
Open Access Article
127 - NMFA: Novel Modified FA algorithm Based On Firefly Recent Behaviors
Fatemeh Jafarnejad Rezaiyeh Kambiz Majidzadeh -
Open Access Article
128 - An Improved K-Means with Artificial Bee Colony Algorithm for Clustering Crimes
Mohammad Karimi Farhad Soleimanian Gharehchopogh -
Open Access Article
129 - Improving Accuracy in Intrusion Detection Systems Using Classifier Ensemble and Clustering
Ensieh Nejati Hassan Shakeri Hassan Raei -
Open Access Article
130 - IKM-SARAVOA: A New Hybrid-based Search and Rescue Algorithm with African Vulture Optimization Algorithm for Data Clustering
Ehsan Soleimani Dehkordi Mohammadreza Mollahoseini Ardakani -
Open Access Article
131 - An Efficient Protocol for Data Aggregation In Wireless Sensor Networks
Mohammad Karim Sohrabi Elahe Khorramian -
Open Access Article
132 - Proposing a Novel Algorithm for Fault-Tolerant Relay Node Placement in Wireless Sensor Networks
Hamid Barati Mohsen Sedighi Ali Movaghar Iman Attarzadeh -
Open Access Article
133 - Double Clustering Method in Hiding Association Rules
Zahra Kiani Abari Mohammad Naderi Dehkordi -
Open Access Article
134 - Intrusion Detection System in Computer Networks Using Decision Tree and SVM Algorithms
Zeinab Kermansaravi Hamid Jazayeriy Soheil Fateri -
Open Access Article
135 - Solving the Capacitated Clustering Problem by a Combined Meta-Heuristic Algorithm
Narges Mahmoodi Darani Vahid Ahmadi Zahra Saadati Eskandari Majid Yousefikhoshbakht -
Open Access Article
136 - Intelligent Diabetic Retinopathy Diagnosis in Retinal Images
Marzie Zahmatkesh Ali Rafiee Majid Mazinani -
Open Access Article
137 - A Routing Algorithm based on Fuzzy Clustering and Minimum Cost Tree (FCMCT) in Wireless Sensor Network
Maryam Javaherian Abolfazl T.Haghighat -
Open Access Article
138 - Presenting a Real Time Method for Automatic Detection of Diabetes Based on Fuzzy Reward-Penalty System
Najmeh Hosseinpour Mohammad Mosleh Saeed Setayeshi -
Open Access Article
139 - Robust Cluster-Based method for monitoring generalized linear profiles in phase I
Davood Saremian Rassoul Noorossana Sadigh Raissi Paria Soleimani -
Open Access Article
140 - A New Multi-Stage Feature Selection and Classification Approach: Bank Customer Credit Risk Scoring
Farshid Abdi -
Open Access Article
141 - Exact algorithms for solving a bi-level location–allocation problem considering customer preferences
Ehsan Mirzaei Mahdi Bashiri Hossein Shams Shemirani -
Open Access Article
142 - New spatial clustering-based models for optimal urban facility location considering geographical obstacles
Maryam Javadi Jamal Shahrabi -
Open Access Article
143 - Market segmentation of customers of public librariesbased on the loyalty to cluster analysis of k-mean (studying Lurestan, Fars and Khuzestan provinces)
mariam keshvari ندا پورخلیل مرجان خجسته فرObjective: Identification of the target audience of public libraries is the most important step in marketing programs, and one of the most important strategies to achieve this goal, is market segmentation of the customers. In this regard, this study intends to investiga MoreObjective: Identification of the target audience of public libraries is the most important step in marketing programs, and one of the most important strategies to achieve this goal, is market segmentation of the customers. In this regard, this study intends to investigate market segmentation of public libraries’ customers in Lurestan, Fars, and Khuzestan provinces based on the loyalty concept with k- mean method. Methodology: Participants of the study were 300 public library members in the three mentioned provinces and a questionnaire was used as the data collection tool. SPSS was used for the analysis of the statistical data. Data analysis was conducted in three stages. In the first stage based on factor analysis, variables were classified as five factors; in the second stage based on clustering, k-mean was used and market segments were identified and in the third stage, the clusters were analyzed. Results:At the end of the study and according to the observed characteristics, two clusters were recognized as the “behavioral and attributed loyals” and “neutral customers”. the cluster of customers loyalty are loyal both attitudinal and behavioral, but neutral customers are “no comment”. In demographic characteristics, only there were significant difference between books and serials are borrowed. The results of the present research about public libraries can provide useful hints to the actual activities of the public libraries and can be regarded as the basis of many marketing programs. Manuscript profile -
Open Access Article
144 - Ranking and determining the core websites of Islamic Azad University's comprehensive and state branches using webometrics method
alireza isfandyari moghadam farshid danesh faramarz sohiliThis research mainly aims to investigate visibility, Web Impact Factor (WIF), and the collaboration rate of the websites of 36 Iranian Islamic Azad Universities which are comprehensive or located in centers of provinces. The current method applied in this type of resear MoreThis research mainly aims to investigate visibility, Web Impact Factor (WIF), and the collaboration rate of the websites of 36 Iranian Islamic Azad Universities which are comprehensive or located in centers of provinces. The current method applied in this type of research is the process of link analysis, which is a webometrics method. In this process, "in-links", "self-links", and "co-links" of the websites under study were summed and then cluster and multiple dimensional scaling were applied. To collect needed data Yahoo subject directory was utilized. The research findings indicated that websites of South Tehran with 2710 in-links, Karaj with 2560 in-links, and Tabriz with 1350 in-links were the most visited sites; the websites belonging to Khorram-Abad (10.34381) and Khorasgan (0.188561) made the most and the least revisited WIF, respectively; the websites belonging to Khorram-Abad (10.57303) and Khorasgan (0.212915) made the most and the least total WIF, respectively. It is notable that 10 websites were chosen as core websites. According to findings, it is necessary that website managers and designers outline plans for the improvement of the quality and content of their websites, recognizing the factors required by the website in order to attract links. Finally, some recommendations for the improvement of the websites, and some further research have been indicated. Manuscript profile -
Open Access Article
145 - Citation analysis and mapping Library & Information Science in WOS citation database 1993-2011
Maryam okhovati halimeh Sadeghi Ali Talebian Mohammadreza BaneshiPurpose: Mapping is a method to represent scientific output in a field of science. This study aims at mapping LIS publications in Web of Science during 1993-2011. Methdology: The type of survey is scientometric. To do this, web of science (WOS) database was searc MorePurpose: Mapping is a method to represent scientific output in a field of science. This study aims at mapping LIS publications in Web of Science during 1993-2011. Methdology: The type of survey is scientometric. To do this, web of science (WOS) database was searched and total records were downloaded and imported into histcite software and analyzed. This software can analyze the records in the files of author, journal, institution and ect . Some of them were entered to excel for further analysis. The map of science constructed based on global citation scores (gcs). Findings:5097 records were retrieved. HistCite was used to make maps. The average growth rate was % 0.013. The papers were published in ninety-nine journal. The electronic library journal published more than other journals. 2466 universities and colleges contributed and Illinois university published the most papers. Bawden, Budd and Nicholas were the most active authors. While 82 countries contributed in publications U.S. published the most papers. Contribution coefficient was relatively low (0.24). Three clusters were identified. The dominant subjects in three clusters were “information seeking behavior”, “information retrieval” (1st cluster), “children information seeking behavior” (2nd), “information retrieval”, “visualization” and “scientometrics” (3rd cluster). Conclusion: In this study, major magazines, active authors and major institutions, leading countries, of library and information science were determined, to be a good model for our country researchers to improve the qualitative and quantitative of their researches. Manuscript profile -
Open Access Article
146 - Automakers Clustering based on Economic Production Function using Data Envelopment Analysis
S. Rezaei Gh.R. Amin M.Gh. AriyanezhadDEA-based clustering approach reveals the input–output relationships hidden in the data items of input and output. DEA-based clustering approach employs the piece-wise production functions derived from the DEA method to cluster the car companies. Estimate produ MoreDEA-based clustering approach reveals the input–output relationships hidden in the data items of input and output. DEA-based clustering approach employs the piece-wise production functions derived from the DEA method to cluster the car companies. Estimate production function for each car company by input-output data is the benefit of this method. Thus, each car company (like Saipa) not only knows the cluster that it belongs to, but also checks the production function type that it confronts. It is important for managerial decision-making in different fields where decision-makers are interested in knowing the changes required in combining input resources. Manuscript profile -
Open Access Article
147 - Mapping Knowledge Structure in Mystical Researches: A Co-Word Analysis
Soheilā Farhangi Ali Akbar Khāsseh Ārezoo Ebrāhimi DināniBy using co-word analysis, knowledge structure can be determined in a research field and its topical clusters can be identified. This research tries to study knowledge structure in mystical researches via this method and by using network analysis approaches and science MoreBy using co-word analysis, knowledge structure can be determined in a research field and its topical clusters can be identified. This research tries to study knowledge structure in mystical researches via this method and by using network analysis approaches and science visualization. The population of the current study is comprised of 1931 published articles in the field of mysticism that have been indexed in Islamic World Science Citation Center (ISC). By using scientometric and network analysis techniques, the records were retrieved and integrated. It has been used a combination of softwares, including UCINet, VosViewer and SPSS, for data analysis and mapping. Analyzing all keywords show that the most important keywords, based on frequency distribution, are top figures of mysticism: Rumi (Mulavi) and Ibn-Arabi, also Love. As in the co-word analysis, keywords’ pairs such as “Rumi-Masnavi”, “manifestation-unity of existence”, “Ibn-Arabi-unity of existence” have the most frequent co-occurrences. Analyzing lexical clusters show the most important topics related to mystical research in eleven clusters with diverse titles. Based on multidimensional scaling map, these eleven clusters are decreased into eight ones with more general topics, more conjunction and similarity. Studying the centrality and density of these clusters- which indicate maturity and development of each topic based on its keywords- in strategic diagram show that Hekmat (theosophy) or theoretical mysticism, especially from Ibn-Arabi’s point of view, not only have development ability, but also most central role among other topics. Manuscript profile -
Open Access Article
148 - An Energy Efficient improving the Leach protocol Scheme in Wireless Sensor Networks
Farzaneh Abdolahi Maryam Khademi -
Open Access Article
149 - Entropy-based Kernel Graph Cut with Weighted K-Means for Textural Image Region Segmentation
Mehrnaz Niazi Kambiz Rahbar Mansour Sheikhan Maryam Khademi -
Open Access Article
150 - Customer Clustering by Combining the Particle Swarm And K-Means Algorithms and Analyzing Their Behavior on Commercial Websites
MohammadReza Mehrazma Behrad Mahboobi -
Open Access Article
151 - Recognizing Ecological Species Groups and their Relationships with Environmental Factors at Chamanbid-Jozak Protected Area, North Khorasan Province, Iran
Mohabat Nadaf hamid Ejtehadi Mansour Mesdaghi Mohamad Farzam -
Open Access Article
152 - Presenting a customer classification Pattern with a combined data mining approach (case study :Hygienic and Cosmetic products Industry )
omid Bashardoust Ezzatollah Asgharizadeh moHammadAli AfsharKazemiDue to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with curren MoreDue to the accumulated volume of customer purchasing information and the complexity of competition in the present era, the importance of creating a platform for analyzing up-to-date and accurate customer data, with the aim of creating effective relationships with current and loyal customers, more than ever for organizations as It has become a competitive advantage. The purpose of this study was to investigate the behavioral patterns of customers buying Hygienic Products in order to classify them based on the WRFM using data mining methods. 65534 samples were collected from the company databases in the period of 1396-1397 among the customers of Tehran province by the available purposeful sampling method, also with the help of SPSS, the amount of WRFM determined according to the opinion of industry experts and then this field had been to other fields in the research and using Clementine software, customers clustering has been done according to 70% of the data; also, in order to evaluate the quality of clustering, the criteria of Gini Score, error percentage, and normalized mutual information were used. The results indicate the high efficiency of the K-Means clustering method with the number of four clusters with purity percentage (0.761) for customer segmentation. Manuscript profile -
Open Access Article
153 - Diagnosis of Liver Cancer by Fuzzy Kmeans Clustering Based on Evidence Theory
Babak Fouladi Nia Abbas Karimi Faraneh Zarafshan Manochehr Kazemi -
Open Access Article
154 - A Novel Clustering Algorithm Based upon Learning Automata for Collaborative Filtering
Sara Taghipour Javad Akbari Torkestani Sara Nazari -
Open Access Article
155 - Presenting a Practical Way to Preprocess the Raw Data of Smart Meters and Calculate the Load Duration Curve
Hassan Majidi Mahdi Emadaleslami -
Open Access Article
156 - A New Clustering Approach for Efficient Placement of Controllers in SDN using Firefly Algorithm
Azam Amin Mohsen Jahanshahi Mohammadreza Meybodi -
Open Access Article
157 - Intelligent Hybrid Heuristic-Metaheuristic Algorithm for Lifetime Extension in Wireless Body Area Networks
Pouya Aryai Ahmad Khademzadeh Somayyeh Jafarali Jassbi Mehdi Hosseinzadeh -
Open Access Article
158 - Online Aggregation of Coherent Generators Based on Electrical Parameters of Synchronous Generators
Farkhondeh Jabari Heresh Seyedi Sajad Najafi Ravadanegh -
Open Access Article
159 - An Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks Using Fuzzy Inference Systems
Mohsen Jahanshahi Shaban Rahmani Shaghayegh Ghaderi -
Open Access Article
160 - Finding Community Base on Web Graph Clustering
Alireza Rezaee Fariba Jahandideh Shekalgourabi2 -
Open Access Article
161 - Analysis of Financial networks in Tehran Stock Exchange using the application of centrality measures
Majid Montasheri Hojjatollah SadeqiThe purpose of this study is to create a Financial network to identify stock market leaders using centrality measures.This study finally provides a clustering of superior stocks that can be used as an optimal stock portfolio by investors.The statistical population of al MoreThe purpose of this study is to create a Financial network to identify stock market leaders using centrality measures.This study finally provides a clustering of superior stocks that can be used as an optimal stock portfolio by investors.The statistical population of all stock exchanges is that the 100 companies with the most capital were selected as a statistical sample over a period of 11 years.Due to the nature of the research ranking, the Kendall correlation coefficient was used to calculate the correlation.The Prime algorithm was used to identify relationships and construct the minimum spanning tree, and the fast-greedy algorithm was used to cluster stocks.The results show that in terms of degree centrality measure, stocks of Sepahan Cement companies,Omid Investment Management and Bank Melli Investment, based on closeness centrality measure, stocks of Sepahan Cement companies,International Building Development, and Khuzestan Steel, based on Betweenness centrality measure, the stocks of Sepahan Cement, Ghadir Investment and Bank Melli Investments, and finally based on the bottleneck centrality measure,the shares of Sepahan Cement, Khuzestan Steel and International Building Development have the greatest impact on the stock exchange network. Also, the top stocks were classified into 11 clusters,each of which shows a strong relationship between its components. Manuscript profile -
Open Access Article
162 - Online Portfolio Selection Using Spectral Pattern Matching
Matin Abdi amirabbas najafiNowadays, due to the rise of turnover and pace of trading in financial markets, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases. Hence, financial markets tend to apply algorithmic tradi MoreNowadays, due to the rise of turnover and pace of trading in financial markets, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. Online Portfolio Selection (OLPS) is one of the most modern techniques in algorithmic trading. OLPS allocates capital to a number of stocks and updates portfolio at the beginning of each period by some techniques. Actually, individual has no role in portfolio selection and the algorithm determines the way of investing in each period. In this article, an algorithm which follows pattern matching principle has been introduced. In pattern matching principle, the portfolio is selected based on identical historical patterns and in this article these patterns are found by spectral clustering in data mining. At the end of article, there is a numerical example which uses the most 20 active stocks in New York Stock Exchange (NYSE) data and its results has been compared with other algorithms in this topic. Manuscript profile -
Open Access Article
163 - Volatility Clustering in financial markets based on the agent based model
zahra shirazianThe purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation.Time series of financial asset returns show the clustering of volatility, which shows that large changes MoreThe purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation.Time series of financial asset returns show the clustering of volatility, which shows that large changes in prices tend to form clusters together And these clusters will last for a long time. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss several economic mechanisms which have been proposed to explain the origin of this volatility clustering in terms of behavior of market participants and the news arrival process. A common feature of these models seems to be a switching between low and high activity regimes with heavy-tailed durations of regimes. Finally, we discuss a simple agent-based model which links such variations in market activity to threshold behavior of market participants and suggests a link between volatility clustering and investor inertia. Manuscript profile -
Open Access Article
164 - Volatility clustering in financial markets based on the agent based model
zahra shirazian Hashem Nikoomaram Fereydoon Rahnamay Roodposhti Taghi TORABIThe purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation. Time series of financial asset returns show the clustering of volatility, which shows that large changes MoreThe purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation. Time series of financial asset returns show the clustering of volatility, which shows that large changes in prices tend to form clusters together And these clusters will last for a long time. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss several economic mechanisms which have been proposed to explain the origin of this volatility clustering in terms of behavior of market participants and the news arrival process. A common feature of these models seems to be a switching between low and high activity regimes with heavy-tailed durations of regimes. Finally, we discuss a simple agent-based model which links such variations in market activity to threshold behavior of market participants and suggests a link between volatility clustering and investor inertia. Manuscript profile -
Open Access Article
165 - Clustering with K-Means Hybridization Ant Colony Optimization (K-ACO)
Dewi Ratnaningsih -
Open Access Article
166 - A Novel Method for Improving Cold Start Challenge in Recommender Systems through Users Demographics Information
Taravat Abedini Alireza Hedayati Ali Harounabadi -
Open Access Article
167 - FUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA
Saba Sajadi Majid Amirfakhrian -
Open Access Article
168 - Morphometric relationships of some species of the genus Rosa (Rosaceae) in Iran
Leila Mirzaei Leila JodiMultivariate analysis on morphological species Rosa L. in Iran. In total, 12 species were poured on quantitative and qualitative characteristics. Statistical analysis was performed using SPSS version 18 different methods of multivariate statistical analysis, including c MoreMultivariate analysis on morphological species Rosa L. in Iran. In total, 12 species were poured on quantitative and qualitative characteristics. Statistical analysis was performed using SPSS version 18 different methods of multivariate statistical analysis, including cluster analysis (Ward metod) and ordination (PCA) was used. Using the results of studies and morphological diagnostic characters obtained in the principal component analysis, two components were introduced in the first component, plant height and size and type of spine, has a high rate and positive traits. The classification of cluster analysis and principal component analysis was confirmed by the good traits and diversity of ROSA species based on similarities and differences have separated and morphological traits can identify and classify Rosa species of this genus have systematic application. MMMM MMM MMMM MM MKA SAK SP QO Q[PQ][PLKWQSKN WD SDL;ASl; KM JA KD NWJ DA LDL adS[ QPdk iw jeo ifhe wjdp ';lw p okw eidj ewjk dlw qwqkklk Manuscript profile -
Open Access Article
169 - Assessing Populations Diversity of Small Panel Oak (Quercus brantii) in Western Forests of Iran: a Major Effort in Reforestation Programs
Adele Rafezi Mohammad Reza Azimi Mehrshad Zeinalabedini Mohammad Reza ghaffari -
Open Access Article
170 - A Review of Research on Financial Time Series Clustering: A Bibliometrics Approach
Marziyeh Nourahmadi Fatemeh Rasti Hojjatollah SadeqiThe amount of information and data we retrieve and use is growing rapidly. Data mining is the process of extracting relevant data from large volumes of data and the method of discovering and finding the appropriate pattern from large volumes of data sets. Clustering is MoreThe amount of information and data we retrieve and use is growing rapidly. Data mining is the process of extracting relevant data from large volumes of data and the method of discovering and finding the appropriate pattern from large volumes of data sets. Clustering is one of the most common methods of statistical data analysis, and also one of the best data mining approaches. This approach, as a method of unsupervised learning, uses algorithms to classify time series data according to different criteria. The purpose of this study is to investigate the types of applications of clustering and networking in various financial fields, including risk, algorithmic trading, banking and other widely used topics in this field. In this research, using the bibliometrix package in the software, all the researches on clustering is reviewed. While extracting various criteria and clustering approaches, its applications have been studied. This study with a comprehensive review of all research in this field can help researchers as a toolbox to provide a variety of clustering methods in ideation and selection of appropriate methods in classifying and analyzing financial data. Manuscript profile -
Open Access Article
171 - Taxonomy of Promotion Strategies of the Prosperous Pharmaceutical Products in the Growth Stage
Mahdi Ebrahimi ali asgarhalvaeiIran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and min MoreIran's pharmaceutical industry has long been confronted with various marketing and advertising constraints Most of these constraints has arised from governmental terms and conditions, and has led to the overwhelming majority of these companies pursuing a passive and minimalistic approach to exploiting promotional strategies. However,today, with the arrival of newly stablished pharmaceutical companies and with private sector support, we are observing a remarkable change in the past approaches of these companies toward their promotion strategies. In this research, presenting the latest findings of promotion strategies of human pharmaceutical companies, we aimed to identify the common types of these strategies using the taxonomic method. For this purpose, we first created a comprehensive framework for the dimensions and components of the promotion strategy of pharmaceutical companies by conducting semi-structured interviews in a qualitative research and using a content analysis method ,then, through a quantitative survey and completion of questionnaires, the promotion strategy of each of the forty pharmaceutical companies in the statistical sample of this study was identified. Finally, by performing fuzzy clustering, four clusters or distinct types of promotion strategies of pharmaceutical companies were identified, each with significant differences in some of the key characteristics of other types. A key result of study shows that pharmaceutical companies have adopted different approaches to using promotion strategies. Manuscript profile -
Open Access Article
172 - Optimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
Farhad Rad Ali Asghar Nadri Hamid Parvin -
Open Access Article
173 - The Application of Combined Fuzzy Clustering Model and Neural Networks to Measure Valuably of Bank Customers
Raheleh Nasiri Sharifi Maryam Rastgarpour