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Open Access Article

1 - Window-Shopping as an Effective Element to Improve Viral Marketing
shahriyar mohammadi keyvan karimi

E-Commerce is going to be remembered as one of the most important concepts of the twentieth and twenty-first centuries, for a very unique reason: it is the combination of , business, marketing, and design. As well as the others, marketing has a long history. However, ra More

E-Commerce is going to be remembered as one of the most important concepts of the twentieth and twenty-first centuries, for a very unique reason: it is the combination of , business, marketing, and design. As well as the others, marketing has a long history. However, raising the Internet and e-commerce turns its utilization and practice into a modern way, as well as the others. One of the modern techniques of marketing is called viral marketing or so called Word-of-Mouth (WOM). This study is dedicated to reviewing the several strategies and key point of viral marketing. As the contribution, this study is focused on getting the window-shopping phenomenon to work, as a promoting factor on viral marketing strategies. In this paper, a case study on influence of window-shopping on a multi-stage customer decision process compared to that of WOM is been done as the empirical study deducing the applicability and competency of window-shopping. Manuscript profile

Open Access Article

2 - Training MLP Neural Network in Images Compression by GSA Method
maryam dehbashian hamid zahiri

Image compression is one of the important research fields in image processing. Up to now, different methods are presented for image compression. Neural network is one of these methods that has represented its good performance in many applications. The usual method in tr More

Image compression is one of the important research fields in image processing. Up to now, different methods are presented for image compression. Neural network is one of these methods that has represented its good performance in many applications. The usual method in training of neural networks is error back propagation method that its drawbacks are late convergence and stopping in points of local optimum. Lately, researchers apply heuristic algorithms in training of neural networks. This paper introduces a new training method based on the Gravitational Search Algorithm. Gravitational Search Algorithm is the latest and newest version of swarm intelligence optimization approaches. In this algorithm, the candidate answers in search space are masses that interact with each other by gravitational force and change their positions. Gently, the masses with better fitness obtain more mass and effect on other masses more. In this research, an MLP neural network by GSA method is trained for images compression. In order to efficiency evaluation of the presented compressor, we have compared its performance toward PSO and error back propagation methods in compression of four standard images. The final results show salient capability of the proposed method in training of MLP neural networks. Manuscript profile

Open Access Article

3 - A Novel Model for detecting intrusion with Mobile Agent and Game theory
Amin Nezarat mehdi raja Gholamhossein Dastghaibyfard

The proposed framework applies two game theoretic models for economic deployment of intrusion detection system (IDS). The first scheme models and analyzes the interaction behaviors of between an attacker and intrusion detection agent within a non-cooperative game, and t More

The proposed framework applies two game theoretic models for economic deployment of intrusion detection system (IDS). The first scheme models and analyzes the interaction behaviors of between an attacker and intrusion detection agent within a non-cooperative game, and then the security risk value is derived from the mixed strategy Nash equilibrium. The second scheme uses the security risk value to compute the Shapley value of intrusion detection agent under the various threat levels. Therefore, the fair agent allocation creates a minimum set of IDS deployment costs. Numerical examples show that the network administrator can quantitatively evaluate the security risk of each intrusion detection agent and easily select the most effective IDS agent deployment to meet the various threat levels. Manuscript profile

Open Access Article

4 - A New Method to computational intelligence to improve network lifetime in wireless sensor networks
faezeh talebian hassan khotanloo mansour esmaeilpour

Recent advances in wireless electronic and communications provide us the ability to build small, economical sensors with low power consumption and many diverse applications. Limited energy capacity of sensors is a huge challenge that will affect these networks. Clusteri More

Recent advances in wireless electronic and communications provide us the ability to build small, economical sensors with low power consumption and many diverse applications. Limited energy capacity of sensors is a huge challenge that will affect these networks. Clustering has been used as a well-known method to handle this challenge. To find appropriate location of clusters' heads, imperialist competitive algorithm as an emerging topic in computational intelligence has been used. Clusters' heads are connected in a three-level model so that cluster heads with low energy capacity and far from station are considered as level three indirectly communicating with base station. This eventually increases lifetime of wireless sensor networks. Manuscript profile

Open Access Article

5 - Optimized Modeling for satisfaction in the relationship between a physician and patient based on machine learnin methods
Fatemeh Saghafi mojtaba shadmehr Zainabolhoda Heshmati Hadi Veisi

Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the More

Health has always been one of the most important concerns of human. The goal in this research is to know what factors cause and affect patient satisfaction in the relationship between a physician and patient. Since this relationship is a form of healthcare service, the SERVQUAL service quality assessment method has been used as a framework. However the questions have been reviewed based on the previous literature and the experts’ views, leading to a questionnaire designed for the healthcare domain. Data collection has been performed using the questionnaires on subjects selected amongst clients of Rhinoplasty Centers in Tehran. To analyze the data, three machine-learning approaches have been implemented namely Decision Tree, Support Vector Machine and Artificial Neural Networks. A number of possible factors affecting the patient-physician relationship have been used as input and patient satisfaction has been taken as output. Comparing the results of these three methods, Artificial Neural Networks method is shown to have better performance, which has therefore been used for prioritizing the effective factors in this relationship. The results indicate that reaching the information which the patient expects their physician to give is the most effective characteristic in patient satisfaction. The rank of gained features were compared with similar researches. The outcome was very similar and approved the results. Manuscript profile

Open Access Article

6 - An optimised hybrid method for ambulance dispatch based on complex networks and Artificial Intelligence
Zainabolhoda Heshmati Mehdi Teimouri mehdi zarkeshzade Hadi Zare

Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuri More

Nowadays, monitoring people’s health and helping those in need of medical attention is one of the most important responsibilities of governments. Emergency Medical Services (EMS) are therefore setup to provide timely care to victims of sudden and life-threatening injuries or emergencies in order to prevent needless mortality or long-term morbidity. Providing rapid EMS with minimum response time will help improve survival rates. However, factors such as limited ambulance resources, large coverage area and high call-rates have caused delays in EMS, which have lead to lower survival rates. In this research the problem of ambulance dispatching with regards to lowering the response time is discussed. Response time is the most important factor in evaluating the performance of various EMS, which is directly proportional to mortality and survival rates. The most common method to reduce response time in ambulance dispatching is to choose the nearest available ambulance unit, i.e. the nearest neighbor (NN). Other methods use call-priority, first-in first-out (FIFO), preparedness and hybrid algorithms. One of the latest methods used in ambulance dispatching is based on complex networks. This method will apply a higher priority to the call that is more centrally located with respect to other calls. A hybrid algorithm has been proposed in this research which exceed the previous methods in terms of response time reduction.The proposed hybrid approach is based on complex network analysis and a search algorithm, which relies on the important parameters of dispatch, yet presents fewer constraints and overcomes some of the difficulties of the previous methods. In addition, prioritizing emergency calls is also considered. To evaluate these proposed algorithms, a combination of various parameters and operating environments has been simulated. The results of the simulations show improvements in response time reduction compared to previous methods. Manuscript profile

Open Access Article

7 - 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

8 - 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

9 - 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

10 - 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

20.1001.1.27170411.1397.10.35.1.7

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

11 - A greedy new method based on the cascade model to calculate maximizing penetration in social networks
Asgarali Bouyer Hamid Ahmadi

20.1001.1.27170414.1397.10.37.3.8

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

12 - Content and structural analysis of online forums in order to extract users' social relationships and use them in grouping mechanisms.
Fatemeh Orojie fataneh taghiyareh

Today, thanks to the growth and development of communication and information technologies, online learning systems have been able to provide group learning facilities and space for interaction and exchange of ideas between learners. This requires the formation of effect More

Today, thanks to the growth and development of communication and information technologies, online learning systems have been able to provide group learning facilities and space for interaction and exchange of ideas between learners. This requires the formation of effective learning groups and the provision of learner participation tools in online learning environments, which is rarely seen in existing systems that use virtual learning centers. In this article, the content and structure of the discussion forums have been examined. Content analysis has been done in order to match the content of the discussions with the objectives of the forum and to extract the areas of interest of the participants. While expressing the achievements of the social network analysis of an academic learning environment, the researchers have presented a solution for extracting the social relationships of people through the structural analysis of discussion forums in an online learning environment. Also, they have presented a method to use the extracted relationships in the mechanisms of grouping learners and evaluated its efficiency. Different parts of this research have been conducted in different courses in consecutive semesters and its achievements can be used to improve collaborative learning activities in online and blended learning environments. Manuscript profile

Open Access Article

13 - Routing of Multipartite Computer Networks Using Ant Genetic Algorithm
Mohammad Pourmahmood Aghababa amin bahadorani baghbaderani

20.1001.1.27170411.1396.9.31.1.2

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

14 - Presenting a model for using mobile agents in distributed intrusion detection systems based on game theory
امین نظارات مهدی رجا Gholamhossein Dastghaibyfard

20.1001.1.27170411.1395.8.27.6.2

Network intrusion detection systems are tools used to protect network resources from attacks. Due to the spread of attacks in the Internet space and the change in the form and type of attacks from centralized to distributed, the architecture of such systems is also movi More

Network intrusion detection systems are tools used to protect network resources from attacks. Due to the spread of attacks in the Internet space and the change in the form and type of attacks from centralized to distributed, the architecture of such systems is also moving towards distribution. In this article, a method based on mobile agents that act as sensors for detecting invalid movements is proposed. Mobile attack detection agents are scattered in the network moving from one node to another and at any time they build a security upper network and use a kind of cooperative game and communicate with each other, after reaching the Shipley value. They can detect and report the extent and origin of the attack. In this article, a method is proposed that WGA in a non-cooperative game with the attacking element tries to establish a revelation communication in order to calculate the value of Nash and reach the maximum utility, so that it can separate the attacks or real requests, the amount and intensity of the attack with Get help from other WGA Manuscript profile

Open Access Article

15 - Optimum modeling of patient satisfaction with the doctor based on machine learning methods
- شادمهر Zainabolhoda Heshmati Fatemeh saghafi Hadi Veisi

20.1001.1.27170411.1394.7.24.5.8

The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to co More

The patient-centered approach in the field of health has recently been proposed in the field of the medical system of our country, but until now there is no published scientific research on the factors of patient satisfaction with doctors. The present article aims to cover the stated gap with a scientific evaluation based on the real information obtained from the field study. A questionnaire was designed for the health sector and was approved by the opinion of experts. In order to get the opinions of patients, a questionnaire was distributed among 500 people who underwent rhinoplasty in Tehran, and 395 questionnaires were collected. Three methods of decision tree, support vector machine and neural networks were used for data analysis. The analysis of the results according to the accuracy criteria showed that the most efficient method, in priority, the importance of the factors affecting the patient's satisfaction; Neural network method. The results of the analysis with this method indicate that the most effective feature in the patient's satisfaction with the doctor is the information that the patient expects the doctor to provide. The results of ranking factors in comparison with other studies that only used statistical methods for analysis showed that the results were relatively similar and confirmed each other. But the strengths of the neural network method in modeling is the strength of this method compared to the mentioned studies. Manuscript profile

Open Access Article

16 - Presenting a method based on computational intelligence to improve energy consumption in smart wireless sensor networks
faezeh talebian حسن ختن¬لو منصور اسماعیل¬پور

20.1001.1.27170411.1394.7.24.1.4

Recent advances in the field of electronics and wireless communication have given the ability to design and manufacture sensors with low power consumption, small size, reasonable price and various uses. The limited energy capacity of sensors is a major challenge that af More

Recent advances in the field of electronics and wireless communication have given the ability to design and manufacture sensors with low power consumption, small size, reasonable price and various uses. The limited energy capacity of sensors is a major challenge that affects these networks. Clustering is used as one of the well-known methods to manage this challenge. To find the suitable location of the cluster heads, the colonial competition algorithm, which is one of the branches of computational intelligence, has been used. Cluster heads are connected by a three-level model, so that cluster heads with low energy capacity and far from the station are known as the third level and exchange information indirectly with the base station. This increases the lifespan of wireless sensor networks Manuscript profile

Open Access Article

17 - Providing an optimal method for dispatching an ambulance based on complex networks and artificial intelligence
مهدی زرکش زاده Zainabolhoda Heshmati Hadi Zare Mehdi Teimouri

20.1001.1.27170411.1394.7.24.4.7

The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring th More

The goal of emergency medical services is to reduce deaths and complications caused by diseases and injuries. Rapid dispatch of emergency services and reduced response time lead to increased survival rates. Response time is one of the important criteria for measuring the efficiency of emergency medical services. The usual method of sending ambulances is to send the nearest available unit, which pays attention to efficiency in the short term. One of the methods that has been recently mentioned in the field of ambulance dispatch is based on the analysis of complex networks. The purpose of this method is to send an ambulance to a call that is more central than other calls, which leads to better efficiency in the long run. Other methods in dispatching an ambulance are based on finding the best suitable route for service cars, and the time complexity of these methods is very high. In this article, using a hybrid approach and applying centrality criteria from complex network analysis and search methods based on artificial intelligence, an optimal and innovative method is presented to reduce the response time of emergency services. In addition, in the proposed method, the emergency priority of calls is also considered, which is an important variable in decisions. The proposed method has less limitations than the previous methods and the extensive simulation results confirm the significant improvement of this method compared to the previous methods such as the centrality method and the nearest neighbor method. Manuscript profile

Open Access Article

18 - Training of MLP neural network in image compression using GSA method
maryam dehbashian hamid zahiri

One of the important research areas in image processing is image compression. Until now, various methods for image compression have been presented, among which neural networks have attracted many audiences. The most common training method of neural networks is the error More

One of the important research areas in image processing is image compression. Until now, various methods for image compression have been presented, among which neural networks have attracted many audiences. The most common training method of neural networks is the error backpropagation method, which converges and stops at local optima are considered one of its most important weaknesses. The researchers' new approach is to use innovative algorithms in the process of training neural networks. In this article, a new educational method based on gravity search method (GSA) is introduced. The gravity search method is the latest and newest version of all types of collective intelligence search and optimization methods. In this method, the candidate answers in the search space are objects that are affected by the force of gravity and their positions change. Gradually, objects with better fit have more mass and have a greater effect on other objects. In this research, an MLP neural network is trained for image compression using the GSA algorithm. Manuscript profile

Open Access Article

19 - 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

20.1001.1.27170414.1398.11.39.5.1

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

20 - Comparing A Hybridization of Fuzzy Inference System and Particle Swarm Optimization Algorithm with Deep Learning to Predict Stock Prices
Majid Abdolrazzagh-Nezhad mahdi kherad

20.1001.1.27170411.1398.11.41.5.0

Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and compl More

Predicting stock prices by data analysts have created a great business opportunity for a wide range of investors in the stock markets. But the fact is difficulte, because there are many affective economic factors in the stock markets that they are too dynamic and complex. In this paper, two models are designed and implemented to identify the complex relationship between 10 economic factors on the stock prices of companies operating in the Tehran stock market. First, a Mamdani Fuzzy Inference System (MFIS) is designed that the fuzzy rules set of its inference engine is found by the Particle Swarm Optimization Algorithm (PSO). Then a Deep Learning model consisting of 26 neurons is designed wiht 5 hidden layers. The designed models are implemented to predict the stock prices of nine companies operating on the Tehran Stock Exchange. The experimental results show that the designed deep learning model can obtain better results than the hybridization of MFIS-PSO, the neural network and SVM, although the interperative ability of the obtained patterns, more consistent behavior with much less variance, as well as higher convergence speed than other models can be mentioned as significant competitive advantages of the MFIS-PSO model Manuscript profile

Open Access Article

21 - An Improved Method Based on Label Propagation and Greedy Approaches for Community Detection in Dynamic Social Networks
Mohammad ستاری kamran zamanifar

20.1001.1.27170414.1399.12.43.1.2

Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is More

Community detection in temporal social networks is one of the most important topics of research which attract many researchers around the world. There are variety of approaches in detecting communities in dynamic social network among which label propagation approach is simple and fast approach. This approach consists of many methods such as LabelRankT is one with high speed and less complexity. Similar to most methods for detecting communities in dynamic social networks, this one is not trouble free. That is, it is not considered the internal connection of communities, when it expands communities of the previous snapshots in the current snapshot. This drawback decreases the accuracy of community detection in dynamic social networks. For solving the drawback, a greedy approach based on local modularity optimization is added to LabelRankT method. Here, the newly proposed GreedyLabelRankT, LabelRankT and non-overlapping version of Dominant Label Propagation Algorithm Evolutionary (DLPAE-Non Overlapping) on real and synthetic datasets are implemented. Experimental results on both real and synthetic network show that the proposed method detect communities more accurately compared to the benchmark methods. Moreover, the finding here show that running time of the proposed method is close to LabelRankT. Therefore, the proposed method increase the accuracy of community detection in dynamic social networks with no noticeable change in the running time of that. Manuscript profile

Open Access Article

22 - Increasing the value of collected data and reducing energy consumption by using network coding and mobile sinks in wireless sensor networks
ehsan kharati

20.1001.1.27170414.1399.12.43.3.4

The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile More

The wireless sensor network includes a number of fixed sensor nodes that move sink nodes to collect data between nodes. To reduce energy consumption and increase the value of collected data, it is necessary to determine the optimum route and residence location of mobile sinks, which increases the life of wireless sensor networks. Using network coding, this paper presents a Mixed Integer Linear Programming Model to determine the optimal multicast routing of source sensor nodes to mobile sinks in wireless sensor networks, which determines the time and location of sinks to collect maximum coded data and reduces the delay in sink movement and energy consumption. Solving this problem in polynomial time is not possible due to the involvement of various parameters and the constrained resources of wireless sensor networks. Therefore, several exploratory and greedy and fully distributed algorithms are proposed to determine the movement of sinks and their residence location based on maximizing the value of coded data and the type of data dead time. By simulating, the optimal method and the use of coding and proposed algorithms, reduce the runtime and energy consumption and increase the value of collected data and network lifetime than non-coding methods. Manuscript profile

Open Access Article

23 - Improving performance of probe-based rate control mechanisms using classification: evaluation on an experimental testbed for High Throughput WLANs
ghalibaf ali Mohammad Nassiri mohammadhassan daei mahdi sakhaei

20.1001.1.27170414.1399.12.43.9.0

MIMO technology offers a wide range of transmission rates for modern wireless LANs. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help More

MIMO technology offers a wide range of transmission rates for modern wireless LANs. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help determine the rate at which future packets are sent. However, the great diversity of transmission rates in the 802.11n and 802.11ac standards imposes an overhead for updating this information. In this article, to reduce the state space of transmission rates while keeping statistics approximately up to date for each rate, a method for clustering rates is presented so that when sending a packet over a transmission rate, statistical information relating to all the rates belonging to the same cluster is updated. As a result, statistics for a greater number of rates can be updated even when sending a fewer number of packets. We implemented our proposed mechanism in the Linux kernel environment and evaluated its performance under different conditions on an experimental testbed deployed in our research laboratory. The results show that the proposed method outperforms the de-facto Minstrel-HT rate control mechanism in terms of throughput and number of successful transmissions. Manuscript profile

Open Access Article

24 - Converting protein sequence to image for classification with convolutional neural network
reza ahsan mansour ebrahimi dianat dianat

20.1001.1.27170414.1399.12.43.11.2

Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study di More

Since methods for sequencing machine learning sequences were not successful in classifying healthy and cancerous proteins, it is imperative to find a way to represent these sequences to classify healthy and ill individuals with deep learning approaches. In this study different methods of protein sequence representation for classification of protein sequence of healthy individuals and leukemia have been studied. Results showed that conversion of amino acid letters to one-dimensional feature vectors in classification of 2 classes was not successful and only one disease class was detected. By changing the feature vector to colored numbers, the accuracy of the healthy class recognition was slightly improved. The binary protein sequence representation method was more efficient than the previous methods with the initiative of sequencing the sequences in both one-dimensional and two-dimensional (image by Gabor filtering). Protein sequence representation as binary image was classified by applying Gabor filter with 100% accuracy of the protein sequence of healthy individuals and 98.6% protein sequence of those with leukemia. The findings of this study showed that the representation of protein sequence as binary image by applying Gabor filter can be used as a new effective method for representation of protein sequences for classification Manuscript profile

Open Access Article

25 - An access control model for online social networks using user-to-user relationships
Mohamad Javad Piran mahmud deypir

20.1001.1.27170411.1399.12.45.1.1

With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the manag More

With the pervasiveness of social networks and the growing information shared on them, users of these networks are exposed to potential threats to data security and privacy. The privacy settings included in these networks do not give users complete control over the management and privatization of access to their shared information by other users. In this article, using the concept of social graph, a new model of user access control was proposed to the user, which allows the expression of privacy policies and more accurate and professional access control in terms of pattern and depth of relationships between users in social networks. In this article, by using the regular index method, indirect relationships among users are examined and analyzed, and more precise policies than previous models are presented. The evaluation of the results showed that for 10 neighbors for each user, the probability accumulation of a qualified path for the first three counter loops is 1, 10.5 and 67.3%, respectively, and finally for the fourth counter it reaches 100%. As the defined counting characteristic increases, the average execution time of the proposed algorithm and previously proposed algorithms increases. However, for the higher limits of the counting characteristic, the proposed algorithm performs better than the previous ones. Manuscript profile

Open Access Article

26 - A Novel Method based on the Cocomo model to increase the accuracy of software projects effort estimates
mahdieh salari vahid khatibi amid khatibi

20.1001.1.27170411.1399.12.45.3.3

It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead t More

It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead the project to failure. It is therefore a major task in efficient development of software projects to estimate software costs accurately. Therefore, two methods were presented in this research for effort estimation in software projects, where attempts were made to provide a way to increase accuracy through analysis of stimuli and application of metaheuristic algorithms in combination with neural networks. The first method examined the effect of the cuckoo search algorithm in optimization of the estimation coefficients in the COCOMO model, and the second method was presented as a combination of neural networks and the cuckoo search optimization algorithm to increase the accuracy of effort estimation in software development. The results obtained on two real-world datasets demonstrated the proper efficiency of the proposed methods as compared to that of similar methods. Manuscript profile

Open Access Article

27 - Improving imperialist competitive algorithm for solving the nodes placement problem in three-dimensional grid wireless sensor networks
Sayed Wafa Barkhoda Hemmat Sheikhi sudabeh mohammadi

20.1001.1.27170411.1399.12.45.5.5

One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imp More

One of the basic and important research fields in wireless sensor networks is how to place sensor nodes where by using minimum number of sensor nodes all target points are covered and all sensor nodes are connected to the sink. In this paper, a novel method based on imperialist competitive algorithm is used for solving the mentioned problem. In the proposed method, a colony can immigrate from a weak empire to more powerful empire. The idea of immigration is inspired from human society in which a human can emigrate from a country to another country. The network is supposed to be a three-dimensional grid network and the sensor nodes can be only placed at cross-points of the grids while the target points can be deployed at each point of three-dimensional space. The simulation results show that the proposed method uses fewer number of sensor nodes than other similar algorithms and has the less running time. Manuscript profile

Open Access Article

28 - A Combination Method of DEA, DEMATEL and ANP for Evaluation of ERP Systems
amir amini alireza alinezhad

20.1001.1.27170411.1399.12.45.6.6

In this study Data envelopment analysis is used for the assessment of enterprise resources planning systems of 18 manufacturing companies, to determine whether the defined goals for ERP systems have been able to affect the performance after the implementation of the sys More

In this study Data envelopment analysis is used for the assessment of enterprise resources planning systems of 18 manufacturing companies, to determine whether the defined goals for ERP systems have been able to affect the performance after the implementation of the system. Considering the identification of effective factors in implementing ERP systems and using the previous researches, the performance evaluation criteria of this system were identified. Then, using experts’ views the most important input indicators were ranked by fuzzy DEMATEL and output indicators by ANP. In this ranking, time spent on implementation, the implementation infrastructure, training and user support were identified as top input indicators, and three indicators of productivity increase, proper resource management and user satisfaction were selected as top output indicators. Using selected indicators, the performance of ERP systems of selected companies was evaluated. The results of this research will be useful in identifying the strengths and weaknesses of companies compared to top ones and making their ERP system even better. Manuscript profile

Open Access Article

29 - Increasing the lifetime of underwater acoustic sensor networks by optimal relay node placement
zahra mihamadi mohadeseh soleimanpour daryush avasimoghaddam Siamak Talebi

20.1001.1.27170411.1399.12.45.7.7

Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasi More

Underwater acoustic sensor networks (UASNs) have gained growing importance due to their desirable features and wide spread practical applications in many communication fields. Due to the high cost of underwater sensor nodes as well as implementation complexity, increasing the lifetime of UASNs is an important issue. Although relay nodes have an important role in reducing the transmission distance and energy consumption. But the efficient RNP (Relay Node Placement) to avoid the critical sensor nodes' elimination is the main problem, i.e., to preserve the connected network. For this aim this paper presents an innovative solution called an Efficient Relay node Setting (ERS) algorithm, which involves formulating the Relay Node Placement (RNP) as a non-convex optimization problem. Actually, due to the Difference Convex (DC) constraints the proposed RNP problem is a non-convex problem and finding an optimal solution is complicated. However, a novel transformation can be applied to DC constraints which converts the problem into its convex programming equivalent. Application of the convex programming offers the advantage of readily computing a global optimal solution. Simulation results confirm the superiority of the proposed scheme over the competing RA method in terms of network lifetime and efficiency. Manuscript profile

Open Access Article

30 - An Intelligent Model for Multidimensional Personality Recognition of Users using Deep Learning Methods
Hossein Sadr fatemeh mohades deilami morteza tarkhan

20.1001.1.27170414.1400.13.47.6.2

Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opini More

Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets. Manuscript profile

Open Access Article

31 - Sentiment analysis for stock market predection with deep neural network: A case study for international corporate stock database
hakimeh mansour Saeedeh Momtazi Kamran Layeghi

20.1001.1.27170414.1400.13.47.7.3

Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting impo More

Emotional analysis is used as one of the main pillars in various fields such as financial management, marketing and economic changes forecasting in different countries. In order to build an emotion analyzer based on users' opinions on social media, after extracting important features between words by convolutional layers, we use LSTM layers to establish the relationship behind the sequence of words and extract the important features of the text. With discovery of new features extracted by LSTM, the ability of the proposed model to classify the stock values of companies increases. This article is based on the data of Nguyen et al. (2015) and uses only the emotional information of people in social networks to predict stocks. Given that we categorize each user's message into one of the emotional classes "Strong Buy", "Buy", "Hold", "Sell", "Strong Sell", this model can predict the stock value of the next day, whether it will be high or low. The proposed structure consisted of 21 layers of neural networks consisting of convolutional neural networks and long short-term memory network. These networks were implemented to predict the stock markets of 18 companies. Although some of the previously presented models have used for emotion analysis to predict the capital markets, the advanced hybrid methods have not been performed in deep networks with a good forecasting accuracy. The results were compared with 8 baseline methods and indicate that the performance of the proposed method is significantly better than other baselines. For daily forecasts of stocks changes, it resulted in 19.80% improvement in the prediction accuracy, compared with the deep CNN, and 24.50% and 23.94% improvement compared with the models developed by Nguyen et al. (2015) and Derakhshan et al. (2019), respectively. Manuscript profile

Open Access Article

32 - Agile Enterprise Architecture Modeling: Evaluating the Applicability of Six Modeling Standards based on Iran’s National EA Framework
Ali Razi reza rezaei ahmadali یزدان پناه

20.1001.1.27170414.1400.13.47.8.4

The Iran’s national enterprise architecture framework (INEAF) has been adapted from the TOGAF framework and its architectural development method. In this framework, the use of agility paradigm is emphasized, but there is no basis for using agile methods and techniques. More

The Iran’s national enterprise architecture framework (INEAF) has been adapted from the TOGAF framework and its architectural development method. In this framework, the use of agility paradigm is emphasized, but there is no basis for using agile methods and techniques. Based on the results obtained for the researcher, the studied sources did not indicate all the necessary solutions and features to develop an agile methodology based on the Iran’s national EA framework. According to Mr. Gill research, each modeling standard is different in scope and function, and since a modeling standard alone cannot support all the requirements of agile enterprise architecture, combining modeling standards is a suitable solution. In this paper, an agile enterprise architecture modeling methodology including ten solutions with a combination of six modeling standards ArchiMate, UML, BPMN, FAML, SoaML and BMM based on the Iran’s national EA framework is presented. The evaluation of the applicability of the proposed methodology is performed by the combined method (qualitative + quantitative). Qualitative evaluation is performed through a case study and quantitative evaluation is performed using multi-criteria decision making methods including ANP and DEMATEL. Data collection and information gathering and determining options and criteria, is performed through library studies and field methods, and using questionnaire, interview and observation tools. Based on the case study, combination of six standards by agile enterprise architecture modeling method based on the Iran’s national enterprise architecture framework is applicable. For quantitative evaluation of applicability in this paper, according to experts, four options have been proposed, which are based on the final weight: support by known tools, the ability to cover the artifacts of the Iran’s national enterprise architecture framework, efficiency or effectiveness, ease of learning or teachability. Manuscript profile

Open Access Article

33 - 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

20.1001.1.27170414.1400.13.47.9.5

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has bec More

For many years, fake news and messages have been spread in human societies, and today, with the spread of social networks among the people, the possibility of spreading false information has increased more than before. Therefore, detecting fake news and messages has become a prominent issue in the research community. It is also important to detect the users who generate this false information and publish it on the network. This paper detects users who publish incorrect information on the Twitter social network in Persian. In this regard, a system has been established based on combining context-user and context-network features with the help of a conditional generative adversarial network (CGAN) for balancing the data set. The system also detects users who publish fake news by modeling the twitter social network into a graph of user interactions and embedding a node to feature vector by Node2vec. Also, by conducting several tests, the proposed system has improved evaluation metrics up to 11%, 13%, 12%, and 12% in precision, recall, F-measure and accuracy respectively, compared to its competitors and has been able to create about 99% precision, in detecting users who publish fake news. Manuscript profile

Open Access Article

34 - A RPL-based Routing Algorithm for Multimedia Traffic for the Internet of Things
Mohammad Khansari Farzaneh Mortazavi

20.1001.1.27170414.1400.13.47.10.6

According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new More

According to enormous growths in communication networks, multimedia data will play a significant role on the Internet of Things in the near future. High volume of multimedia data leads to challenges such as reducing network lifetime and congestion. In this paper, a new objective function for the RPL routing protocol is proposed which addresses the characteristics of multimedia data in the routing process. In the objective function, node’s remaining energy and the remaining buffer capacity of nodes measures are combined using a weighted pair. In order to evaluate this method, input data is generated based on a video trace. Packet delivery ratio, network lifetime, nodes availability over the lifetime of the network, node energy distribution, and end-to-end delay are used to evaluate the proposed method. The evaluation results show that the proposed method increases the package delivery ratio compared to the standard RPL. This method also improves the lifetime of the nodes by distributing energy between the nodes in comparison with standard RPL and extends the node's availability over the lifetime of the network. Finally, it reduces the network congestion which led to a lower end-to-end delay. Manuscript profile

Open Access Article

35 - A Neighbor-based Link Prediction Method for Bipartite Networks
Golshan Sondossi alireza saebi S. Alireza hashemi G.

20.1001.1.27170414.1400.13.47.11.7

Social network analysis’ link prediction has a diverse range of applications in different areas of science. Bipartite networks are a kind of complex network, which can be used to describe various real-world phenomena. In this article, a link prediction method for bipart More

Social network analysis’ link prediction has a diverse range of applications in different areas of science. Bipartite networks are a kind of complex network, which can be used to describe various real-world phenomena. In this article, a link prediction method for bipartite network is presented. Uni-partite link prediction methods are not effective and efficient enough to be applied to bipartite networks. Thus, to solve this problem, distinct methods specifically designed for bipartite networks are required. The proposed method is neighbor based and consisted of measures of such. Classic uni-partite link prediction measures are redefined to be compatible with bipartite network. Subsequently, these modified measures are used as the basis of the presented method, which in addition to simplicity, has high performance rates and is superior to other neighbor-based methods by 15% in average. Manuscript profile

Open Access Article

36 - Stability Analysis of Networked Control Systems under Denial of Service Attacks using Switching System Theory
Mohammad SayadHaghighi Faezeh Farivar

20.1001.1.27170411.1400.13.49.5.0

With the development of computer networks, packet-based data transmission has found its way to Cyber-Physical Systems (CPS) and especially, networked control systems (NCS). NCSs are distributed industrial processes in which sensors and actuators exchange information bet More

With the development of computer networks, packet-based data transmission has found its way to Cyber-Physical Systems (CPS) and especially, networked control systems (NCS). NCSs are distributed industrial processes in which sensors and actuators exchange information between the physical plant and the controller via a network. Any loss of data or packet in the network links affects the performance of the physical system and its stability. This loss could be due to natural congestions in network or a result of intentional Denial of Service (DoS) attacks. In this paper, we analytically study the stability of NCSs with the possibility of data loss in the feed-forward link by modelling the system as a switching one. When data are lost (or replaced with a jammed or bogus invalid signal/packet) in the forward link, the physical system will not receive the control input sent from the controller. In this study, NCS is regarded as a stochastic switching system by using a two-position Markov jump model. In State 1, the control signal/packet passes through and gets to the system, while in State 2, the signal or packet is lost. We analyze the stability of system in State 2 by considering the situation as an open-loop control scenario with zero input. The proposed stochastic switching system is studied in both continuous and discrete-time spaces to see under what conditions it satisfies Lyapunov stability. The stability conditions are obtained according to random dwell times of the system in each state. Finally, the model is simulated on a DC motor as the plant. The results confirm the correctness of the obtained stability conditions. Manuscript profile

Open Access Article

37 - An efficient Two Pathways Deep Architecture for Soccer Goal Recognition towards Soccer Highlight Summarization
Amirhosein Zangane Mehdi Jampour Kamran Layeghi

20.1001.1.27170414.1400.13.49.5.5

In this paper, an automated method has been presented using a dual-path deep learning architecture model for the problem of soccer video analysis and it emphasizes the gate recognition as one of the most important elements of the goal event that is the most important so More

In this paper, an automated method has been presented using a dual-path deep learning architecture model for the problem of soccer video analysis and it emphasizes the gate recognition as one of the most important elements of the goal event that is the most important soccer game event. The proposed architecture is considered as an extended form of the VGG 13-layer model in which a dual-path architectural model has been defined. For recognizing the gate in the first path using the proposed architectural model, the model is trained by the training dataset. But in the second path, the training dataset is first examined by a screening system and the best images containing features different from the features of the first path are selected. In another word, features of a network similar to the first path, but after passing through the screening system are generated in the second path. Afterwards, the feature vectors generated in two paths are combined to create a global feature vector, thus covering different spaces of the gate recognition problem. Different evaluations have been performed on the presented method. The evaluation results represent the improved accuracy of gate recognition using the proposed dual-path architectural model in comparison to the basic model. A comparison of proposed method with other existing outcomes also represents the improved accuracy of the proposed method in comparison to the published results. Manuscript profile

Open Access Article

38 - Using Sentiment Analysis and Combining Classifiers for Spam Detection in Twitter
mehdi salkhordeh haghighi Aminolah Kermani

20.1001.1.27170411.1401.14.51.10.6

The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one More

The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output will detect spam text or normal text. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods to them, we separately calculate the Tweeter spam detection. Eventually, we enter the output of both approaches into a Meta Classifier so that its output specifies the final spam detection or the normality of the tweet text. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets. Manuscript profile

Open Access Article

39 - Explaining the adoption process of software-oriented networks (SDN) using the foundational data method and systems approach
Elham Ziaeipour ali rajabzadeh ghotri Alireza Taghizadeh

20.1001.1.27170411.1401.14.51.12.8

Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agi More

Software Defined Networking (SDN) is one of the technologies with most promising role in digital transformation. Dynamic structure of SDN can adapt to ever changing nature of future networks and their users. The important impact of this technology on intelligence, agility, management and control of current network devices as well as upcoming communication technologies reduces expenses and creates innovative businesses. Although, service providers are very interested in deploying SDN to transform their static infrastructures to a dynamic and programmable platform, they do not consider it as one of their priorities and still depend on traditional methods to manage their network. Therefore, this study highlights the factors affecting the acceptance of SDN architecture and its application by the national telecom operators, and proposes a comprehensive and new paradigm model using a systems approach and Grounded theory (Strauss and Corbin model). This innovative model is provided by systematically reviewing the theoretical foundations and conducting in-depth interviews with managers and experts in telecom industry. During the modeling process, more than a thousand initial codes were determined. Finally, based on the opinion of experts on these codes, a total of 73 open codes, 12 axial codes and 6 main categories have been extracted. Manuscript profile

Open Access Article

40 - Reduction of network load by mapping the application in the network on a chip using the discrete Harris hawk algorithm
Elham Hajebi Vahid Sattari-Naeini

20.1001.1.27170411.1401.14.51.14.0

Reducing load and power consumption in on-chip network systems is very important and one of the most important issues to increase the efficiency of on-chip network is the issue of mapping an application on the chip network. Solving the application mapping problem to fin More

Reducing load and power consumption in on-chip network systems is very important and one of the most important issues to increase the efficiency of on-chip network is the issue of mapping an application on the chip network. Solving the application mapping problem to find the best mapping is a complex and time consuming issue and has a huge impact on network latency and power consumption. In this paper, using the Harris hawk algorithm, we have been able to provide a method for mapping processing cores to the network on chip to reduce the load on the network and thus congestion in the links and improve network performance. The simulation results show that this algorithm performs better than the basic algorithms. Manuscript profile

Open Access Article

41 - Generalizing The Concept of Business Processes Structural Soundness from Classic Petri-nets to BPMN2.0 Process Models
Yahya Poursoltani Mohammad Hassan Shirali-Shahreza S. Alireza Hashemi Golpayegani

BPMN2.0 Standard is a modeling language, which can be understood and used by a wide range of users. However, because of its non-formal nature, models (designed using it) can be containing structural errors such as Deadlock (impossibility of executing some of process tas More

BPMN2.0 Standard is a modeling language, which can be understood and used by a wide range of users. However, because of its non-formal nature, models (designed using it) can be containing structural errors such as Deadlock (impossibility of executing some of process tasks) and Livelock (infinite repetition of tasks) may be produced by using them. These semantic errors can create anomalies in the workflow of the organization. So far, some researches has been conducted on the validation of these process models and various solutions have been provided to discover some of these structural errors. The question that may be raised about these methods is whether it is possible to definitely guarantee the structural accuracy of a BPMN method model by using any of them? To answer this question, we need a comprehensive definition of a correct BPMN2.0 process model, based on which we can evaluate the comprehensiveness of validation methods and strongly make sure that the considered method can discover all of the structural errors of the process model. In this paper, based on concept of general process models and the concept of soundness (based on process models created using Petri nets) and the generalization of its properties, i.e. Liveness and Boundness to BPMN2.0 process models, a comprehensive definition for a correct (sound) BPMN2 process model provided. Then, the comprehensiveness of the suggested methods of some of the most important researches conducted has been evaluated based on it. This definition can be used as a measure for efficiency of BPMN validation methods. Manuscript profile

Open Access Article

42 - A Novel Multi-Step Ahead Demand Forecasting Model Based on Deep Learning Techniques and Time Series Augmentation
Hossein Abbasimehr Reza Paki

20.1001.1.27170411.1401.14.53.1.1

In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formula More

In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formulated as a time series forecasting task. In the context of time series forecasting, deep learning methods have demonstrated good accuracy in predicting complex time series. However, the excellent performance of these methods is dependent on the amount of data available. For this purpose, in this study, we propose to use time series augmentation techniques to improve the performance of deep learning methods. In this study, three new methods have been used to test the effectiveness of the proposed approach, which are: 1) Long short-term memory, 2) Convolutional network 3) Multihead self-attention mechanism. This study also uses a multi-step forecasting approach that makes it possible to predict several future points in a forecasting operation. The proposed method is applied to the actual demand data of a furniture company. The experimental results show that the proposed approach improves the forecasting accuracy of the methods used in most different prediction scenarios Manuscript profile

Open Access Article

43 - Three Dimensional Beamforming in Multi User Multi Antenna Cellular Networks with Randomly Distributed Users
S. Mohammad Razavizadeh Nasim Mohammadi

20.1001.1.27170411.1401.14.53.7.7

In this paper, problem of using the 3D beamforming method (3DBF) in a multi-input-multi-output cellular communication network (MIMO) is discussed. The network consists of a cell with multiple users, in which users are distributed based on the Poisson point (PPP) process More

In this paper, problem of using the 3D beamforming method (3DBF) in a multi-input-multi-output cellular communication network (MIMO) is discussed. The network consists of a cell with multiple users, in which users are distributed based on the Poisson point (PPP) process at the cell area, which is closer to the conditions in a real mobile network. In this case, the number of users inside the cell and their location will be random. Depending on the distribution of users in the space and the difference in their distance from the base station, their elevation angles will also be different. Considering the downlink transmission and the zero-forcing (ZF) precoder in the base station, with the aim of eliminating intra cell interference, we evaluate and analyze the probability of coverage in the cell and then we obtain the best antenna tilt angle to achieve maximum probability of coverage. Using the analysis of numerical results, the accuracy of the calculations and the value of the optimal tilt angle of the antenna array are confirmed. Manuscript profile

Open Access Article

44 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
Vahid Safari dehnavi masoud shafiee

20.1001.1.27170411.1401.14.53.8.8

Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the vo More

Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model. Manuscript profile

Open Access Article

45 - Design and fabrication of the E-field probe for the measurement of the electromagnetic fields in 5G frequency band
Reza Bahri Mahdi Fasanghari Ahmadreza Eskandari Vahid Yazdanian

20.1001.1.27170411.1401.14.53.13.3

In this paper, a device for measuring the electric fields intensity in the environment is designed and presented in the 5G frequency band, including the frequency range of 3400 ~ 3600 MHz. This device, called the 5G electric probe, is realized by three orthogonal antenn More

In this paper, a device for measuring the electric fields intensity in the environment is designed and presented in the 5G frequency band, including the frequency range of 3400 ~ 3600 MHz. This device, called the 5G electric probe, is realized by three orthogonal antennas, in connection to filter circuits and power detectors. The proposed antenna is a strip monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotropically. The proposed filter is a coupled-line microstrip filter that has the ability to remove out-of-band signals. The proposed power detector is able to operate linearly over a wide dynamic range and convert the fields received from the antenna and filter sections to suitable DC voltages for digital processing. Finally, the designed 5G electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields. Manuscript profile

Open Access Article

46 - A comprehensive survey on the influence maximization problem in social networks
mohsen taherinia mahdi Esmaeili Behrooz Minaei

20.1001.1.27170411.1401.14.53.16.6

With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency a More

With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches Manuscript profile

Open Access Article

47 - Presenting the ICT Policies Implementation Model of the 6th Development Using the Neural Network Method
Nazila Mohammadi Gholamreza Memarzadeh Tehran Sedigheh Tootian Isfahani

It is inevitable to properly manage the implementation of information and communication technology policies in a planned way in order to improve the country's position in the fields of science and technology. The purpose of this research is to provide a model of the eff More

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

Open Access Article

48 - A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
Mohammad Javad shayegan mohadeseh valizadeh

<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of perso More

<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.</span></p> Manuscript profile

Open Access Article

49 - 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

20.1001.1.27170411.1402.15.55.8.9

Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of inform More

Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process. Manuscript profile

Open Access Article

50 - 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

20.1001.1.27170411.1402.15.55.9.0

Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted usi More

Cloud computing service models are growing rapidly, and inefficient use of resources in cloud data centers leads to high energy consumption and increased costs. Plans of resource allocation aiming to reduce energy consumption in cloud data centers has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into Delay-sensitive (Interactive) or Delay-Insensitive classes, and thereafter suitable VMs can be selected for migration. This is possible by virtual machine workload prediction .In fact, workload predicting and predicting analysis is a pre-migration process of a virtual machine. In this paper, In order to classification of VMs in the Microsoft Azure cloud service, a hybrid model based on Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Microsoft Azure Dataset is a labeled dataset and the workload of virtual machines in this dataset are in two labeled Delay-sensitive (Interactive) or Delay-Insensitive. But the distribution of samples in this dataset is unbalanced. In fact, many samples are in the Delay-Insensitive class. Therefore, Random Over-Sampling (ROS) method is used in this paper to overcome this challenge. Based on the empirical results, the proposed model obtained an accuracy of 94.42 which clearly demonstrates the superiority of our proposed model compared to other existing models. Manuscript profile

Open Access Article

51 - Design and implementation of a survival model for patients with melanoma based on data mining algorithms
farinaz sanaei Seyed Abdollah Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri

10.61186/jict.44093.15.57.1

20.1001.1.27170411.1402.15.57.1.6

Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of More

Background/Purpose: Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival. Methodology: This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms. Findings: A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics. Conclusion: The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model. Manuscript profile

Open Access Article

52 - An Intrusion Detection System based on Deep Learning for CAN Bus
Fatemeh Asghariyan Mohsen Raji

20.1001.1.27170411.1402.15.57.2.7

In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bu More

In recent years, with the advancement of automotive electronics and the development of modern vehicles with the help of embedded systems and portable equipment, in-vehicle networks such as the controller area network (CAN) have faced new security risks. Since the CAN bus lacks security systems such as authentication and encryption to deal with cyber-attacks, the need for an intrusion detection system to detect attacks on the CAN bus seem to be very necessary. In this paper, a deep adversarial neural network (DACNN) is proposed to detect various types of security intrusions in CAN buses. For this purpose, the DACNN method, which is an extension of the CNN method using adversarial learning, detects intrusion in three stages; In the first stage, CNN acts as a feature descriptor and the main features are extracted, and in the second stage, the discriminating classifier classifies these features and finally, the intrusion is detected using the adversarial learning. In order to show the efficiency of the proposed method, a real open source dataset was used in which the CAN network traffic on a real vehicle during message injection attacks is recorded on a real vehicle. The obtained results show that the proposed method performs better than other machine learning methods in terms of false negative rate and error rate, which is less than 0.1% for DoS and drive gear forgery attack and RPM forgery attack while this rate is less than 0.5% for fuzzy attack. Manuscript profile

Open Access Article

53 - Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
mohammadreza zeraatkarmoghaddam majid ghayori

20.1001.1.27170411.1402.15.57.12.7

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

54 - 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

55 - Generalizing The Concept of Business Processes Structural Soundness from Classic Petri-nets to BPMN2.0 Process Models
Yahya Poursoltani Mohammad Hassan Shirali-Shahreza S. Alireza hashemi G.

BPMN2.0 Standard is a modeling language, which can be understood and used by a wide range of users. However, because of its non-formal nature, models (designed using it) can be containing structural errors such as Deadlock (impossibility of executing some of process tas More

BPMN2.0 Standard is a modeling language, which can be understood and used by a wide range of users. However, because of its non-formal nature, models (designed using it) can be containing structural errors such as Deadlock (impossibility of executing some of process tasks) and Livelock (infinite repetition of tasks) may be produced by using them. These semantic errors can create anomalies in the workflow of the organization. So far, some researches has been conducted on the validation of these process models and various solutions have been provided to discover some of these structural errors. The question that may be raised about these methods is whether it is possible to definitely guarantee the structural accuracy of a BPMN method model by using any of them? To answer this question, we need a comprehensive definition of a correct BPMN2.0 process model, based on which we can evaluate the comprehensiveness of validation methods and strongly make sure that the considered method can discover all of the structural errors of the process model. In this paper, based on concept of general process models and the concept of soundness (based on process models created using Petri nets) and the generalization of its properties, i.e. Liveness and Boundness to BPMN2.0 process models, a comprehensive definition for a correct (sound) BPMN2 process model provided. Then, the comprehensiveness of the suggested methods of some of the most important researches conducted has been evaluated based on it. This definition can be used as a measure for efficiency of BPMN validation methods. Manuscript profile

Open Access Article

56 - 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

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