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      • Open Access Article

        1 - Evaluation of SIP signaling implementation using QoS parameters
        mojtaba jahanbakhsh azharivs azharivs maryam homayooni Ahmad akbari
        Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 More
        Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 !mso]> st1:*{behavior:url(#ieooui) } /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal" mso-tstyle-rowband-size:0 mso-tstyle-colband-size:0 mso-style-noshow:yes mso-style-priority:99 mso-style-qformat:yes mso-style-parent:"" mso-padding-alt:0cm 5.4pt 0cm 5.4pt mso-para-margin:0cm mso-para-margin-bottom:.0001pt mso-pagination:widow-orphan font-size:11.0pt font-family:"Calibri","sans-serif" mso-ascii-font-family:Calibri mso-ascii-theme-font:minor-latin mso-fareast-font-family:"Times New Roman" mso-fareast-theme-font:minor-fareast mso-hansi-font-family:Calibri mso-hansi-theme-font:minor-latin mso-bidi-font-family:Arial mso-bidi-theme-font:minor-bidi} Abstract The variety of services on IP networks and the need for network technology convergence have resulted in many access networks to adopt the IP technology. The Session Initiation Protocol (SIP) is an end to end application level protocol for establishing, terminating and modifying sessions and has experienced widespread use in IP networks due to its distinguished features such as being text based, independence from the underlying network, and more importantly supporting various types of mobility. In fact these features have lead SIP to be used as the core signaling protocol in the IP Multimedia Subsystem, which is the control plane proposed for next generation networks by the 3GPP community. Nevertheless, the performance of SIP servers when used by the millions of users of the next generation networks is not well established. In this paper we evaluate the performance of SIP servers using a test bed developed at the Iran University of Science & Technology. We consider eight different configurations for SIP server and also study the effect of using TCP and UDP as the transport protocol for SIP packets. We measure several parameters including call setup delay, call failure rate and SIP server throughput. Our results suggest that using SIP in large networks require using special techniques for balancing the load of SIP servers as well as mitigating temporary overloads.  Manuscript profile
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        2 - Cross-layer Design for Congestion Control, Routing and Scheduling in Ad-hoc Wireless Networks with considering the Electrical Power of nodes
        Hooman Tahayori
        Abstract Ad hoc Wireless Networks, are networks formed by a collection of nodes through radio. In wireless networking environment, formidable challenges are presented. One important challenge is connection maintenance mechanism for power consumption. In this paper, a mu More
        Abstract Ad hoc Wireless Networks, are networks formed by a collection of nodes through radio. In wireless networking environment, formidable challenges are presented. One important challenge is connection maintenance mechanism for power consumption. In this paper, a multi-objective optimal design is considered for ad-hoc networks which address the electrical power of nodes effects on cross-layer congestion control, routing and scheduling. We first formulate the rate and scheduling constraints. In this way, the multi-commodity flow variables are used. Then, resource allocation in networks with fixed wireless channel and single-rate devices is formulated. Since the electrical power of nodes effects are included in the design problem, we formulate resource allocation as utility and cost function, together in a maximization problem with those constraints. By dual decomposition, the resource allocation problem vertically decomposes into three sub-problems: congestion control, routing and scheduling. These three sub-problems interact through congestion and link price. Simulation results are included to verify the effectiveness of the proposed approach. Manuscript profile
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        3 - A New Approach to Extract and Utilize Learners Social Relationships through Analyzing Forums Structure and Contents
        Somayeh Ahari
        Collaborative learning tools play important roles in communications and knowledge building, among learners in a virtual learning environment. They demand appropriate grouping algorithms as well as facilitating learners’ participations mechanisms. This paper has utilized More
        Collaborative learning tools play important roles in communications and knowledge building, among learners in a virtual learning environment. They demand appropriate grouping algorithms as well as facilitating learners’ participations mechanisms. This paper has utilized some information retrieval techniquesto investigate the relevance of discussion posts to their containing forums, and extract learners’ most frequent topics. Trying to explore students online interactions, researchers have applied social network analysis, which has led to a new representation of social networking. They have proposed a new grouping algorithm based on the provided representation of social relationships. The mentioned approaches have been evaluated in some academic courses in Department of Electrical and Computer Engineering, and ELearning Center, University of Tehran. The results have revealed some considerable improvements in comparison to the traditional approaches. Research outcomes may help tutors to create and guide groups of learners more effectively. Manuscript profile
      • Open Access Article

        4 - using clustering in AODV routing protocol for vehicular ad-hoc networks on highway scenario
        amin feyzi
        Vehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network More
        Vehicular Ad hoc networks are a subset of mobile Ad hoc networks in which vehicles are considered as network nodes. Their major difference is rapid mobility of nodes which causes the quick change of topology in this network. Quick changes in the topology of the network are considered as a big challenge For routing in these networks, routing protocols must be robust and reliable. AODV Routing protocol is one of the known routing protocols in vehicular ad hoc networks. There are also some problems in applying this routing protocol on the vehicular ad hoc networks. The number of control massages increases with increasing the scale of the network and the number of nodes . One way to reduce the overhead in AODV routing protocol is clustering the nodes of the network. In this paper , the modified K-means algorithm has been used for clustering the nodes and particle swarm optimization has been used for selecting cluster head. The results of the proposed method improved normalized routing load and the increase of the packet delivery rate compared to AODV routing protocol. Manuscript profile
      • Open Access Article

        5 - Automatic Sepration of Learnrs in Learning Groups Based on Identifying Learning Style from Their Behavior in Learning Environment
        mohammad sadegh rezaei gholamali montazer
        Automatic identification of learners groups based on similarity of learning style improves e-learning systems from the viewpoint of learning adaptation and collaboration among learners. In this paper, a new system is proposed for identifying groups of learners, who have More
        Automatic identification of learners groups based on similarity of learning style improves e-learning systems from the viewpoint of learning adaptation and collaboration among learners. In this paper, a new system is proposed for identifying groups of learners, who have similar learning style, by using learners’ behavior information in an e-learning environment. Proposed clustering method for separation of learners is developed based on ART neural network structure and Snap-Drift neural network learning process. This artificial network enables us to identify learners groups in uncertain group separation parameters, without knowing appropriate number of groups.  The results of an empirical evaluation of the proposed method, which are based on two criteria, “Davies-Bouldin” and “Purity and Gathering”, indicate that our proposed method outperforms other clustering methods in terms of accuracy. Manuscript profile
      • Open Access Article

        6 - Multicast computer network routing using genetic algorithm and ant colony
        Mohammad Pourmahmood Aghababa
        Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from More
        Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from a sender to several receivers. To support such programs, there is a need to make an optimal multicast tree to indicate the optimal routes from the sending source to the corresponding sinks.  Providing an optimal tree for routing is a complicated problem. In this paper, we are looking forward a method for routing of multicast networks with considering some parameters such as the cost and delay. Also, this paper has emphasized the issue that every parameter in routing problem has different value for different packets. And in accordance to these parameters optimal routing multicast trees are proposed. To gain this end, the genetic algorithm and ant colony optimization approaches are adopted. The simulation results show that the presented algorithms are able to produce optimal multicast trees subject to the packets. Manuscript profile
      • Open Access Article

        7 - Modified orthogonal chaotic colonial competition algorithm and its application in improving pattern recognition in multilayer perceptron neural network
        Payman Moallem mehrdad sadeghi hariri MAHDI hashemi
        Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthog More
        Despite the success of the Colonial Competition Algorithm (ICA) in solving optimization problems, this algorithm still suffers from repeated entrapment in the local minimum and low convergence speed. In this paper, a new version of this algorithm, called Modified Orthogonal Chaotic Colonial Competition (COICA), is proposed. In the policy of absorbing the proposed version, each colony seeks the space to move towards the colonizer through the definition of a new orthogonal vector. Also, the possibility of selecting powerful empires is defined through the boltzmann distribution function, and the selection operation is performed through the roulette wheel method. The proposed multilevel perceptron neural network (MLP) algorithm is used to classify standard datasets, including ionosphere and sonar. To evaluate the performance of this algorithm and to evaluate the generalizability of the trained neural network with the proposed version, the K-Fold cross-validation method has been used. The results obtained from the simulations confirm the reduction of network training error as well as the improved generalizability of the proposed algorithm. Manuscript profile
      • Open Access Article

        8 - A Hybrid Neural Network Ensemble Model for Credit Risk Assessment
        shaban elahi Ahmad ghodselahi hamidreza naji
        Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble l More
        Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk, is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble learning for credit granting decisions is designed. Combining clustering and classification techniques resulted in system improvement. The German bank real dataset was used for neural network training. The proposed model implemented as credit risk evaluation multi agent system and the results showed the proposed model has higher accuracy, better performance and lesser cost in applicant classification when compared with other credit risk evaluation methods Manuscript profile
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        9 - A greedy new method based on the cascade model to calculate maximizing penetration in social networks
        Asgarali Bouyer Hamid Ahmadi
        In the case of penetration maximization, the goal is to find the minimum number of nodes that have the most propagation and penetration in the network. Studies on maximizing penetration and dissemination are becoming more widespread. In recent years, many algorithms hav More
        In the case of penetration maximization, the goal is to find the minimum number of nodes that have the most propagation and penetration in the network. Studies on maximizing penetration and dissemination are becoming more widespread. In recent years, many algorithms have been proposed to maximize the penetration of social networks. These studies include viral marketing, spreading rumors, innovating and spreading epidemics, and so on. Each of the previous studies has shortcomings in finding suitable nodes or high time complexity. In this article, we present a new method called ICIM-GREEDY to solve the problem of maximizing penetration. In the ICIM-GREEDY algorithm, we consider two important criteria that have not been considered in the previous work, one is penetration power and the other is penetration sensitivity. These two criteria are always present in human social life. The proposed method is evaluated on standard datasets. The obtained results show that this method has a better quality in finding penetrating nodes in 30 seed nodes than other compared algorithms. This method also performs better in terms of time compared to the comparative algorithms in terms of relatively fast convergence. Manuscript profile
      • Open Access Article

        10 - Routing of Multipartite Computer Networks Using Ant Genetic Algorithm
        Mohammad Pourmahmood Aghababa amin bahadorani baghbaderani
        With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple des More
        With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple destinations over a communication network. To support such programs, you need to create an optimal multipart tree , Which indicates the optimal routes to reach from one sender source to several desired destinations. Manuscript profile
      • Open Access Article

        11 - Application of clustering in AODV routing protocol for intercity networks on the highway scenario
        amin feyzi Vahid Sattari-Naeini majid mohammadi
        Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network More
        Intercarous networks are a subset of mobile networks in which vehicles are considered as network nodes. The main difference with case mobile networks is the rapid mobility of nodes, which causes rapid topology change in this network It becomes. Rapid changes in network topology are a major challenge for routing, for routing in these networks, routing protocols must be robust and reliable. One of the well-known routing protocols in intercity networks is the AODV routing protocol. The application of this routing protocol on intercity networks also has problems that increase the number of control messages in the network by increasing the scale of the network and the number of nodes. One way to reduce overhead in the AODV protocol is to cluster network nodes. In this paper, the modified K-Means algorithm is used to cluster the nodes and the particle swarm algorithm is used to select the cluster head. The results of the proposed method improve the normal routing load and increase the packet delivery rate compared to the AODV routing protocol. Manuscript profile
      • Open Access Article

        12 - Routing improvement to control congestion in software defined networks by using distributed controllers
        saied bakhtiyari Ardeshir Azarnejad
        Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way th More
        Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way that the delay between controllers and switches in wide area networks can be reduced. In this regard, most of the proposed methods have focused on reducing latency. But latency is just one factor in network efficiency and overall cost reduction between controllers and related switches. This article examines more factors to reduce the cost between controllers and switches, such as communication link traffic. In this regard, a cluster-based algorithm is provided for network segmentation. Using this algorithm, it can be ensured that each part of the network can reduce the maximum cost (including delays and traffic on links) between the controller and its related switches. In this paper, using Topology Zoo, extensive simulations have been performed under real network topologies. The results of the simulations show that when the probability of congestion in the network increases, the proposed algorithm has been able to control the congestion in the network by identifying the bottleneck links in the communication paths of each node with other nodes. Therefore, considering the two criteria of delay and the degree of busyness of the links, the process of placing and distributing the controllers in the clustering operation has been done with higher accuracy. By doing so, the maximum end-to-end cost between each controller and its related switches, in the topologies Chinanet of China, Uunet of the United States, DFN of Germany, and Rediris of Spain, is decreased 41.2694%, 29.2853%, 21.3805% and 46.2829% respectively. Manuscript profile
      • Open Access Article

        13 - Investigating the effect of using various marketing strategies on social networks on gaining the trust of council customers Investigating ترجمه‌های investigate فعلفراوانی بررسی کردن investigate, check, peruse, survey, study رسیدگی کردن consider, attend, check, investigate, inspect, investigate استفسار کردن investigate وارسی کردن sift, investigate پژوهیدن investigate, inquire, research, search تحقیق کردن investigate, inquire, verify, assay, interrogate, question اطلاعات مقدماتی بدست اوردن investigate جستار کردن investigate تفتیش کردن inquire, inspect, investigate, revise باز جویی کردن examine, assay, inquire, interrogate, investigate, cross-examine تعریف‌های investigate فعل ۱ carry out a systematic or formal inquiry to discover and examine the facts of (an incident, allegation, etc.) so as to establish the truth. police are investigating the alleged beating مترادف‌ها: check outsuss outgive something the once-overscope outinquire intolook intogo intolook overprobeexplorescrutinizeconduct an investigation intoconduct an inquiry intomake inquiries abouttry to get to the bottom ofinspectanalyzestudyexamineconsiderresearchsearch/sift the evidence concerningpore overdelve intoauditevaluatefollow up مترادف investigate فعل check outsuss outgive something the once-overscope out inquire intolook intogo intolook overprobeexplorescrutinizeconduct an investigation intoconduct an inquiry intomake inquiries abouttry to get to the bottom ofinspectanalyzestudyexamineconsiderresearchsearch/sift the evidence concerningpore overdelve intoauditevaluatefollow up همچنین ببینید investigate
        farzaneh milani jafari zenouzi
        The aim of this study is to investigate the effect of using variety of marketing strategies in social networks to build customers’ trust. Marketing through social networks has made appropriate opportunities for companies to attract more customers. Building customers' tr More
        The aim of this study is to investigate the effect of using variety of marketing strategies in social networks to build customers’ trust. Marketing through social networks has made appropriate opportunities for companies to attract more customers. Building customers' trust and attracting the customers can be mentioned as marketing challenges on these networks. So, in order to create competitive advantages, companies need to use appropriate strategies of building trust. The population of this study consists of all Iranian users of social networking sites that affected by companies advertisements. Also the sample size by using snowball sampling method is 446. The research method is descriptive survey research and data collection tool is questionnaire. To test hypotheses the partial least squares (PLS) technique and SmartPLS 3 software has been used. The results show that all four variables include: transactional, relationship, database and knowledge-based marketing strategies in social networks have a significant impact to build customers’ trust. Indeed, transactional strategy has negative impact on trust so the relation between this variable and dependent variable is reverse. knowledge-based marketing strategy has the most positive impact on customers’ trust. Manuscript profile
      • Open Access Article

        14 - Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices
        Majid Abdolrazzagh-Nezhad mahdi kherad
        Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and compl More
        Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and complex. In this paper, two models are designed and implemented to identify the complex relationship between 10 economic factors on the stock prices of companies operating in the Tehran stock market. First, a Mamdani Fuzzy Inference System (MFIS) is designed that the fuzzy rules set of its inference engine is found by the Particle Swarm Optimization Algorithm (PSO). Then a Deep Learning model consisting of 26 neurons is designed wiht 5 hidden layers. The designed models are implemented to predict the stock prices of nine companies operating on the Tehran Stock Exchange. The experimental results show that the designed deep learning model can obtain better results than the hybridization of MFIS-PSO, the neural network and SVM, although the interperative ability of the obtained patterns, more consistent behavior with much less variance, as well as higher convergence speed than other models can be mentioned as significant competitive advantages of the MFIS-PSO model Manuscript profile
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        15 - Introducing a genetic algorithm based Method for Community person's stance Detection in social media and news
        mehdi salkhordeh haghighi Seyyed Mohammad  ebrahimi
        News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the r More
        News reports in social media are presented with large volumes of different kinds of documents. The presented topics in these documents focus on different communities and person stances and opinions. Knowing the relationships among persons in the documents can help the readers to obtain a basic knowledge about the subject and the purpose of various documents. In the present paper, we introduce a method for detecting communities that includes the persons with the same stances and ideas. To do this, the persons referenced in different documents are clustered into communities that have related positions and stances. In the presented method. Community-based personalities are identified based on a friendship network as a base method. Then by using a genetic algorithm, the way that these communities are identified is improved. The criterion in the tests is rand index of detection of these communities. The experiments are designed based on real databases that published in Google News on a particular topic. The results indicate the efficiency and desirability of the proposed method Manuscript profile
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        16 - An Improved Method Based on Label Propagation and Greedy Approaches for Community Detection in Dynamic Social Networks
        Mohammad ستاری kamran zamanifar
        Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is More
        Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is simple and fast approach. This approach consists of many methods such as LabelRankT is one with high speed and less complexity. Similar to most methods for detecting communities in dynamic social networks, this one is not trouble free. That is, it is not considered the internal connection of communities, when it expands communities of the previous snapshots in the current snapshot. This drawback decreases the accuracy of community detection in dynamic social networks. For solving the drawback, a greedy approach based on local modularity optimization is added to LabelRankT method. Here, the newly proposed GreedyLabelRankT, LabelRankT and non-overlapping version of Dominant Label Propagation Algorithm Evolutionary (DLPAE-Non Overlapping) on real and synthetic datasets are implemented. Experimental results on both real and synthetic network show that the proposed method detect communities more accurately compared to the benchmark methods. Moreover, the finding here show that running time of the proposed method is close to LabelRankT. Therefore, the proposed method increase the accuracy of community detection in dynamic social networks with no noticeable change in the running time of that. Manuscript profile
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        17 - Increasing the value of collected data and reducing energy consumption by using network coding and mobile sinks in wireless sensor networks
        ehsan kharati
        The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile More
        The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile sinks, which increases the life of wireless sensor networks. Using network coding, this paper presents a Mixed Integer Linear Programming Model to determine the optimal multicast routing of source sensor nodes to mobile sinks in wireless sensor networks, which determines the time and location of sinks to collect maximum coded data and reduces the delay in sink movement and energy consumption. Solving this problem in polynomial time is not possible due to the involvement of various parameters and the constrained resources of wireless sensor networks. Therefore, several exploratory and greedy and fully distributed algorithms are proposed to determine the movement of sinks and their residence location based on maximizing the value of coded data and the type of data dead time. By simulating, the optimal method and the use of coding and proposed algorithms, reduce the runtime and energy consumption and increase the value of collected data and network lifetime than non-coding methods. Manuscript profile
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        18 - Converting protein sequence to image for classification with convolutional neural network
        reza ahsan mansour ebrahimi dianat dianat
        Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study di More
        Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification Manuscript profile
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        19 - An access control model for online social networks using user-to-user relationships
        Mohamad Javad Piran mahmud deypir
        With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the manag More
        With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the management and privatization of access to their shared information by other users. In this article, using the concept of social graph, a new model of user access control was proposed to the user, which allows the expression of privacy policies and more accurate and professional access control in terms of pattern and depth of relationships between users in social networks. In this article, by using the regular index method, indirect relationships among users are examined and analyzed, and more precise policies than previous models are presented. The evaluation of the results showed that for 10 neighbors for each user, the probability accumulation of a qualified path for the first three counter loops is 1, 10.5 and 67.3%, respectively, and finally for the fourth counter it reaches 100%. As the defined counting characteristic increases, the average execution time of the proposed algorithm and previously proposed algorithms increases. However, for the higher limits of the counting characteristic, the proposed algorithm performs better than the previous ones. Manuscript profile
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        20 - The role of organizational factors on consumer buying behavior in social networks
        zohreh dehdashti shahrokh Mitra Daneshparvar vahid nasehifar vahid Khashei
        The clothing industry has seen great growth in social media in recent years. Many factors affect the purchase of clothing from social networks, and one of these important and influential factors is organizational factors. Therefore, the purpose of this study is to inves More
        The clothing industry has seen great growth in social media in recent years. Many factors affect the purchase of clothing from social networks, and one of these important and influential factors is organizational factors. Therefore, the purpose of this study is to investigate the organizational factors affecting the consumer in shopping through social networks. In this research, a combined method has been used. In the qualitative section, by reviewing the literature and interviewing vendors active in social networks, texts were prepared and coded. In the quantitative part, based on the initial model, a questionnaire was developed and distributed to 385 clothing buyers in networks. The structural equation method was used to analyze the data and the Sobel test was used to examine the mediating role. This study examines the independent and combined effect of organizational variables affecting people's trust in buying clothes through social networks. The results showed that information quality, transaction security, company reputation and company location have a significant effect on people's trust and consumer willingness to buy clothing through social networks.. Manuscript profile
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        21 - A Novel Method based on the Cocomo model to increase the accuracy of software projects effort estimates
        mahdieh salari vahid khatibi amid khatibi
        It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead t More
        It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead the project to failure. It is therefore a major task in efficient development of software projects to estimate software costs accurately. Therefore, two methods were presented in this research for effort estimation in software projects, where attempts were made to provide a way to increase accuracy through analysis of stimuli and application of metaheuristic algorithms in combination with neural networks. The first method examined the effect of the cuckoo search algorithm in optimization of the estimation coefficients in the COCOMO model, and the second method was presented as a combination of neural networks and the cuckoo search optimization algorithm to increase the accuracy of effort estimation in software development. The results obtained on two real-world datasets demonstrated the proper efficiency of the proposed methods as compared to that of similar methods. Manuscript profile
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        22 - Improving imperialist competitive algorithm for solving the nodes placement problem in three-dimensional grid wireless sensor networks
        Sayed Wafa Barkhoda Hemmat Sheikhi sudabeh mohammadi
        One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imp More
        One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imperialist competitive algorithm is used for solving the mentioned problem. In the proposed method, a colony can immigrate from a weak empire to more powerful empire. The idea of immigration is inspired from human society in which a human can emigrate from a country to another country. The network is supposed to be a three-dimensional grid network and the sensor nodes can be only placed at cross-points of the grids while the target points can be deployed at each point of three-dimensional space. The simulation results show that the proposed method uses fewer number of sensor nodes than other similar algorithms and has the less running time. Manuscript profile
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        23 - Increasing the lifetime of underwater acoustic sensor networks by optimal relay node placement
        zahra mihamadi mohadeseh soleimanpour daryush avasimoghaddam Siamak Talebi
        Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasi More
        Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasing the lifetime of UASNs is an important issue. Although relay nodes have an important role in reducing the transmission distance and energy consumption. But the efficient RNP (Relay Node Placement) to avoid the critical sensor nodes' elimination is the main problem, i.e., to preserve the connected network. For this aim this paper presents an innovative solution called an Efficient Relay node Setting (ERS) algorithm, which involves formulating the Relay Node Placement (RNP) as a non-convex optimization problem. Actually, due to the Difference Convex (DC) constraints the proposed RNP problem is a non-convex problem and finding an optimal solution is complicated. However, a novel transformation can be applied to DC constraints which converts the problem into its convex programming equivalent. Application of the convex programming offers the advantage of readily computing a global optimal solution. Simulation results confirm the superiority of the proposed scheme over the competing RA method in terms of network lifetime and efficiency. Manuscript profile
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        24 - An Intelligent Model for Multidimensional Personality Recognition of Users using Deep Learning Methods
        Hossein Sadr fatemeh mohades deilami morteza tarkhan
        Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opini More
        Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets. Manuscript profile
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        25 - Sentiment analysis for stock market predection with deep neural network: A case study for international corporate stock database
        hakimeh mansour Saeedeh Momtazi Kamran Layeghi
        Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting impo More
        Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting important features between words by convolutional layers, we use LSTM layers to establish the relationship behind the sequence of words and extract the important features of the text. With discovery of new features extracted by LSTM, the ability of the proposed model to classify the stock values of companies increases. This article is based on the data of Nguyen et al. (2015) and uses only the emotional information of people in social networks to predict stocks. Given that we categorize each user's message into one of the emotional classes "Strong Buy", "Buy", "Hold", "Sell", "Strong Sell", this model can predict the stock value of the next day, whether it will be high or low. The proposed structure consisted of 21 layers of neural networks consisting of convolutional neural networks and long short-term memory network. These networks were implemented to predict the stock markets of 18 companies. Although some of the previously presented models have used for emotion analysis to predict the capital markets, the advanced hybrid methods have not been performed in deep networks with a good forecasting accuracy. The results were compared with 8 baseline methods and indicate that the performance of the proposed method is significantly better than other baselines. For daily forecasts of stocks changes, it resulted in 19.80% improvement in the prediction accuracy, compared with the deep CNN, and 24.50% and 23.94% improvement compared with the models developed by Nguyen et al. (2015) and Derakhshan et al. (2019), respectively. Manuscript profile
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        26 - Use of conditional generative adversarial network to produce synthetic data with the aim of improving the classification of users who publish fake news
        arefeh esmaili Saeed Farzi
        For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has bec More
        For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news. Manuscript profile
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        27 - A RPL-based Routing Algorithm for Multimedia Traffic for the Internet of Things
        Mohammad Khansari Farzaneh Mortazavi
        According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new More
        According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new objective function for the RPL routing protocol is proposed which addresses the characteristics of multimedia data in the routing process. In the objective function, node’s remaining energy and the remaining buffer capacity of nodes measures are combined using a weighted pair. In order to evaluate this method, input data is generated based on a video trace. Packet delivery ratio, network lifetime, nodes availability over the lifetime of the network, node energy distribution, and end-to-end delay are used to evaluate the proposed method. The evaluation results show that the proposed method increases the package delivery ratio compared to the standard RPL. This method also improves the lifetime of the nodes by distributing energy between the nodes in comparison with standard RPL and extends the node's availability over the lifetime of the network. Finally, it reduces the network congestion which led to a lower end-to-end delay. Manuscript profile
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        28 - Presenting a novel solution to choose a proper database for storing big data in national network services
        Mohammad Reza Ahmadi davood maleki ehsan arianyan
        The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector an More
        The increasing development of tools producing data in different services and the need to store the results of large-scale processing results produced from various activities in the national information network services and also the data produced by the private sector and social networks, has made the migration to new databases solutions with appropriate features inevitable. With the expansion and change in the size and composition of data and the formation of big data, traditional practices and patterns do not meet new requirements. Therefore, the necessity of using information storage systems in new and scalable formats and models has become necessary. In this paper, the basic structural dimensions and different functions of both traditional databases and modern storage systems are reviewed and a new technical solution for migrating from traditional databases to modern databases is presented. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the comprehensive services of the national information network are presented and the parameters and capabilities of databases in the standard and Hadoop context are examined. In addition, as a practical example, a solution for combining traditional and modern databases has been presented, evaluated and compared using the BSC method. Moreover, it is shown that in different data sets with different data volumes, a combined use of both traditional and modern databases can be the most efficient solution. Manuscript profile
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        29 - Using Sentiment Analysis and Combining Classifiers for Spam Detection in Twitter
        mehdi salkhordeh haghighi Aminolah Kermani
        The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one More
        The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output will detect spam text or normal text. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods to them, we separately calculate the Tweeter spam detection. Eventually, we enter the output of both approaches into a Meta Classifier so that its output specifies the final spam detection or the normality of the tweet text. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets. Manuscript profile
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        30 - Outage and Throughput Analysis of Bidirectional Cognitive Amplify-and-Forward Relaying Networks with Wireless Power Transfer
        Ehsan Soleimani Nasab
        Cognitive radio is a promising technology which aims to achieve better frequency spectrum utilization. On the other hand, wireless energy harvesting can provide extra energy requirement at the nodes. Two scenarios in a two-way network are assumed where in the first scen More
        Cognitive radio is a promising technology which aims to achieve better frequency spectrum utilization. On the other hand, wireless energy harvesting can provide extra energy requirement at the nodes. Two scenarios in a two-way network are assumed where in the first scenario, relay harvests its required energy from end-sources of secondary network in presence of cognitive radio network and in the second scenario, both end-sources harvest energy from relay in secondary network. Both the Nakagami-m fading caused by signal propagation and the interference at relay caused by primary users in a cognitive radio network are considered. Closed-form expressions for outage probability and throughput of bidirectional cognitive radio amplify-and-forward relaying network using energy harvesting and wireless power transfer techniques over independent and non-identically distributed (i.n.i.d.) Nakagami-m fading channels are proposed. The analytical derivations are validated employing Monte Carlo simulations, where it is demonstrated that the first scenario always outperforms the second one, while both scenarios perform better than no energy harvesting case. Manuscript profile
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        31 - Explaining the adoption process of software-oriented networks (SDN) using the foundational data method and systems approach
        Elham Ziaeipour ali rajabzadeh ghotri Alireza Taghizadeh
        Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agi More
        Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agility, management and control of current network devices as well as upcoming communication technologies reduces expenses and creates innovative businesses. Although, service providers are very interested in deploying SDN to transform their static infrastructures to a dynamic and programmable platform, they do not consider it as one of their priorities and still depend on traditional methods to manage their network. Therefore, this study highlights the factors affecting the acceptance of SDN architecture and its application by the national telecom operators, and proposes a comprehensive and new paradigm model using a systems approach and Grounded theory (Strauss and Corbin model). This innovative model is provided by systematically reviewing the theoretical foundations and conducting in-depth interviews with managers and experts in telecom industry. During the modeling process, more than a thousand initial codes were determined. Finally, based on the opinion of experts on these codes, a total of 73 open codes, 12 axial codes and 6 main categories have been extracted. Manuscript profile
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        32 - A Horizon for Sentiment Analysis in Social Networks based on Interpreting Contents
        Maryam Tayefeh Mahmoudi َAmirmansour  Yadegari Parvin Ahmadi Kambiz Badie
        Interpreting contents in social networks with the aim of analyzing the sentiment of their narrators is of particular significance. In this paper, we present a framework for such a purpose, which is able to classify the messages hidden in contents based on using some rul More
        Interpreting contents in social networks with the aim of analyzing the sentiment of their narrators is of particular significance. In this paper, we present a framework for such a purpose, which is able to classify the messages hidden in contents based on using some rule-type protocols with high abstraction level. According to this framework, items such as prosodic of a content's narrator, context of disseminating a content and the key propositions in a content's text are regarded in the condition part of a protocol, while the possible classes for the message in a content are considered as its action part. It is to be noted that the proposed rule-type protocols can equally be used for other languages due to the generic-ness of the above-mentioned items. Results of computer simulations on a variety of different contents in the social networks show that the proposed framework is sufficiently capable of analyzing the sentiment of the contents' narrators in these networks. Manuscript profile
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        33 - Multi-level ternary quantization for improving sparsity and computation in embedded deep neural networks
        Hosna Manavi Mofrad Seyed Ali ansarmohammadi Mostafa Salehi
        Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and co More
        Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and computational resources. Network compression techniques can overcome these challenges. Quantization and pruning methods are the most important compression techniques among them. One of the famous quantization methods in DNNs is the multi-level binary quantization, which not only exploits simple bit-wise logical operations, but also reduces the accuracy gap between binary neural networks and full precision DNNs. Since, multi-level binary can’t represent the zero value, this quantization does’nt take advantage of sparsity. On the other hand, it has been shown that DNNs are sparse, and by pruning the parameters of the DNNs, the amount of data storage in memory is reduced while computation speedup is also achieved. In this paper, we propose a pruning and quantization-aware training method for multi-level ternary quantization that takes advantage of both multi-level quantization and data sparsity. In addition to increasing the accuracy of the network compared to the binary multi-level networks, it gives the network the ability to be sparse. To save memory size and computation complexity, we increase the sparsity in the quantized network by pruning until the accuracy loss is negligible. The results show that the potential speedup of computation for our model at the bit and word-level sparsity can be increased by 15x and 45x compared to the basic multi-level binary networks. Manuscript profile
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        34 - A Novel Multi-Step Ahead Demand Forecasting Model Based on Deep Learning Techniques and Time Series Augmentation
        Hossein Abbasimehr Reza Paki
        In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formula More
        In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formulated as a time series forecasting task. In the context of time series forecasting, deep learning methods have demonstrated good accuracy in predicting complex time series. However, the excellent performance of these methods is dependent on the amount of data available. For this purpose, in this study, we propose to use time series augmentation techniques to improve the performance of deep learning methods. In this study, three new methods have been used to test the effectiveness of the proposed approach, which are: 1) Long short-term memory, 2) Convolutional network 3) Multihead self-attention mechanism. This study also uses a multi-step forecasting approach that makes it possible to predict several future points in a forecasting operation. The proposed method is applied to the actual demand data of a furniture company. The experimental results show that the proposed approach improves the forecasting accuracy of the methods used in most different prediction scenarios Manuscript profile
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        35 - Increasing Total Throughput, Reducing Outage to Zero, and Reducing Power Consumption in a Cellular Network
        Mohsen Seyyedi Saravi Mohammadreza Binesh Marvasti Seyedeh Leili Mirtaheri Seyyed Amir Asghari
        Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by as More
        Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by assuming a limited transmission capacity for mobile nodes in a wireless cellular network as limitations in the transmission capacity often exist both in terms of hardware, battery limitations, and regulatory rules in the real world. In presenting this framework, a new idea was proposed once the existing methods were examined and their advantages and disadvantages were compared, respectively. After the formula was proved, the idea's simulation steps were performed via MATLAB. Present methods either increased the throughput by assuming unlimited transmission power or prevented some nodes from accessing the communication service. The simulation results showed that the proposed algorithm reduced the power consumption of mobile nodes in the network by a quarter in addition to increasing the throughput by 27%, and further operated in a way that no node would lose communication service Manuscript profile
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        36 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
        Vahid Safari dehnavi masoud shafiee
        Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the vo More
        Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model. Manuscript profile
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        37 - A comprehensive survey on the influence maximization problem in social networks
        mohsen taherinia mahdi Esmaeili Behrooz Minaei
        With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency a More
        With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches Manuscript profile
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        38 - Presenting the ICT Policies Implementation Model of the 6th Development Using the Neural Network Method
        Nazila Mohammadi Gholamreza   Memarzadeh Tehran Sedigheh Tootian Isfahani
        It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the eff More
        It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the effective factors on the implementation of Iran's ICT policies with the help of the neural network technique and based on Giddens' constructive theory. From the point of view of conducting it, this research is of a survey type and based on the purpose, it is of an applied type because it is trying to use the results of the research in the Ministry of Communication and Information Technology and the Iranian Telecommunications Company. Data collection is based on library and field method. The tool for collecting information is research researcher-made questionnaire. The statistical population of the research is information and communication technology experts at the headquarters of Iran Telecommunication Company (810 people), of which 260 people were randomly selected as a sample based on Cochran's formula. MATLAB software was used for data analysis. According to the findings, the best combination for development is when all input variables are considered at the same time, and the worst case is when the infrastructure development variable is ignored, and the most important based on network sensitivity analysis is related to infrastructure development and the least important is related to content supply. Manuscript profile
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        39 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
        Ali Khosrozadeh Ali Movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
        Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More
        Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
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        40 - A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
        Mohammad Javad shayegan mohadeseh valizadeh
        <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of perso More
        <p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.</span></p> Manuscript profile
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        41 - The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data
        Sogand Dehghan shahriyar mohammadi rojiar pirmohamadiani
        Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of inform More
        Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process. Manuscript profile
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        42 - Predicting the workload of virtual machines in order to reduce energy consumption in cloud data centers using the combination of deep learning models
        Zeinab Khodaverdian Hossein Sadr Mojdeh Nazari Soleimandarabi Seyed Ahmad Edalatpanah
        Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More
        Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile
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        43 - Improving energy consumption in the Internet of Things using the Krill Herd optimization algorithm and mobile sink
        Shayesteh Tabatabaei
        Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging info More
        Internet of Things (IoT) technology involves a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. Clustering sensor nodes into separate categories and exchanging information through headers is one way to improve energy consumption. This paper introduces a new clustering-based routing protocol called KHCMSBA. The proposed protocol biologically uses fast and efficient search features inspired by the Krill Herd optimization algorithm based on krill feeding behavior to cluster the sensor nodes. The proposed protocol also uses a mobile well to prevent the hot spot problem. The clustering process at the base station is performed by a centralized control algorithm that is aware of the energy levels and position of the sensor nodes. Unlike protocols in other research, KHCMSBA considers a realistic energy model in the grid that is tested in the Opnet simulator and the results are compared with AFSRP (Artifical Fish Swarm Routing ProtocolThe simulation results show better performance of the proposed method in terms of energy consumption by 12.71%, throughput rate by 14.22%, end-to-end delay by 76.07%, signal-to-noise ratio by 82.82%. 46% compared to the AFSRP protocol Manuscript profile
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        44 - Identify and analyze decision points and key players in procurement process in the EPC companies
        Seyedeh Motahareh  Hosseini Mohammad aghdasim
        Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in o More
        Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in organizations and using tools such as process mining, a platform is provided for the use of data analysis approaches and better analysis of decisions, and managers can act in agile decision making. Selecting a supplier in the process of purchasing in complex projects is one of the basic and key decisions that affect the quality, cost and performance of the project. In this article, with a process perspective, the decision points in the purchasing process in a complex construction project in an EPC company have been discovered and the key players in the implementation of the process have been identified and analyzed through social network analysis. The results of this research have led to the investigation of decision points in the process, the performance of decision points and the identification of key people in decision making, which can be used to improve the company's future performance. Manuscript profile
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        45 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
        farinaz sanaei Seyed Abdollah  Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri
        Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of More
        Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile
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        46 - An Intrusion Detection System based on Deep Learning for CAN Bus
        Fatemeh Asghariyan Mohsen Raji
        In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bu More
        In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bus lacks security systems such as authentication and encryption to deal with cyber-attacks, the need for an intrusion detection system to detect attacks on the CAN bus seem to be very necessary. In this paper, a deep adversarial neural network (DACNN) is proposed to detect various types of security intrusions in CAN buses. For this purpose, the DACNN method, which is an extension of the CNN method using adversarial learning, detects intrusion in three stages; In the first stage, CNN acts as a feature descriptor and the main features are extracted, and in the second stage, the discriminating classifier classifies these features and finally, the intrusion is detected using the adversarial learning. In order to show the efficiency of the proposed method, a real open source dataset was used in which the CAN network traffic on a real vehicle during message injection attacks is recorded on a real vehicle. The obtained results show that the proposed method performs better than other machine learning methods in terms of false negative rate and error rate, which is less than 0.1% for DoS and drive gear forgery attack and RPM forgery attack while this rate is less than 0.5% for fuzzy attack. Manuscript profile
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        47 - Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
        mohammadreza zeraatkarmoghaddam majid ghayori
        Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or i More
        Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or intrusions. In deep learning, the most important challenge for training neural networks is determining the hyperparameters in these networks. To overcome this challenge, we have presented a hybrid approach to automate hyperparameter tuning in deep learning architecture by eliminating the human factor. In this article, an IDS in IIoT based on convolutional neural networks (CNN) and recurrent neural network based on short-term memory (LSTM) using metaheuristic algorithms of particle swarm optimization (PSO) and Whale (WOA) is used. This system uses a hybrid method based on neural networks and metaheuristic algorithms to improve neural network performance and increase detection rate and reduce neural network training time. In our method, considering the PSO-WOA algorithm, the hyperparameters of the neural network are determined automatically without the intervention of human agent. In this paper, UNSW-NB15 dataset is used for training and testing. In this research, the PSO-WOA algorithm has use optimized the hyperparameters of the neural network by limiting the search space, and the CNN-LSTM neural network has been trained with this the determined hyperparameters. The results of the implementation indicate that in addition to automating the determination of hyperparameters of the neural network, the detection rate of are method improve 98.5, which is a good improvement compared to other methods. Manuscript profile
      • Open Access Article

        48 - A Framework for Sentiment Analysis in Social Networks based on Interpreting Contents
        Maryam Tayfeh-Mahmoudi َAmirmansour  Yadegari Parvin Ahmadi kambiz badie
        Interpreting contents with the aim of analyzing the sentiment of their narrators in social networks, holds a high significance due to the role of a content in disseminating information to the corresponding human groups. In this paper, we propose a framework for analyzin More
        Interpreting contents with the aim of analyzing the sentiment of their narrators in social networks, holds a high significance due to the role of a content in disseminating information to the corresponding human groups. In this paper, we propose a framework for analyzing sentiment on complex contents in a social network according to which a set of if-then type rules defined at high abstraction level, would be able to classify the messages behind these contents. According to this framework, items such as prosodic, context and key propositions are considered in the condition part of a rule and possible classes of message are taken into account in a rule’s action part. It is to be noted that the rules proposed for interpreting a content do not depend on the considered language due to the very inherent property of the items which are considered in interpretation. Results of experiments on a wide range of different contents in a social network support the fact that the proposed framework is sufficiently capable of analyzing the sentiments of contents’ narrators. Manuscript profile
      • Open Access Article

        49 - Multi-Level Ternary Quantization for Improving Sparsity and Computation in Embedded Deep Neural Networks
        Hosna Manavi Mofrad ali ansarmohammadi Mostafa Salehi
        Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and co More
        Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and computational resources. Network compression techniques can overcome these challenges. Quantization and pruning methods are the most important compression techniques among them. One of the famous quantization methods in DNNs is the multi-level binary quantization, which not only exploits simple bit-wise logical operations, but also reduces the accuracy gap between binary neural networks and full precision DNNs. Since, multi-level binary can’t represent the zero value, this quantization does not take advantage of sparsity. On the other hand, it has been shown that DNNs are sparse, and by pruning the parameters of the DNNs, the amount of data storage in memory is reduced while computation speedup is also achieved. In this paper, we propose a pruning and quantization-aware training method for multi-level ternary quantization that takes advantage of both multi-level quantization and data sparsity. In addition to increasing the accuracy of the network compared to the binary multi-level networks, it gives the network the ability to be sparse. To save memory size and computation complexity, we increase the sparsity in the quantized network by pruning until the accuracy loss is negligible. The results show that the potential speedup of computation for our model at the bit and word-level sparsity can be increased by 15x and 45x compared to the basic multi-level binary networks. Manuscript profile
      • Open Access Article

        50 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
        Ali Khosrozadeh movaghar movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
        Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More
        Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
      • Open Access Article

        51 - Presenting the ICT Policies Implementation Model of the 6th Development Using the Neural Network Method
        Nazila Mohammadi Gholamreza  Memarzadeh sedigheh tootian
        It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the eff More
        It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the effective factors on the implementation of Iran's ICT policies by the neural network technique and based on Giddens' constructive theory. From the point of view of conducting it, this research is of a survey type and based on the purpose, it is of an applied type because it is trying to use the results of the research in the Ministry of Communication and Information Technology and the Iranian Telecommunications Company. Data collection is based on library and field method. The tool for collecting information is researcher-made questionnaire. The statistical population of the research is ICT experts at the headquarters of Iran Telecommunication Company (810 people), of which 260 people were randomly selected as a sample based on Cochran's formula. MATLAB software was used for data analysis. According to the findings, the best combination for development is when all input variables are considered at the same time, and the worst case is when the infrastructure development variable is ignored, and the most important based on network sensitivity analysis is related to infrastructure development and the least important is related to content supply. Manuscript profile