Network

Open Access Article

1 - An Intelligent Algorithm for the Process Section of Radar Surveillance Systems
Habib Rasi

10.7508/jist.2016.01.002

In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intel More

In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intelligence system cannot recognize the different devices of the same type or class. A very important role is played by Measurement and Signature Intelligence. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. The information such as Pulse Repetition Interval (PRI), Angle of Arrival (AoA), Pulse Width (PW), Radio Frequencies (RF), and Doppler shifts are not usable. In the proposed algorithm, for clustering of overlapping pulses received from self-organization neural network SOFM (due to its high accuracy in comparison with other neural networks, such as CLNN and neural networks (Fuzzy ART), and for detecting intra-pulse modulation type, matrix method, and for identifying the radar type, RBF neural network have been used. The simulation results of the proposed algorithm shows that in the presence 5% noise and 5% missing pulse, the accuracy of the clustering part of the proposed algorithm is equivalent to 91/8%, intra-pulse modulation recognition accuracy is 98%, the detection accuracy is 2/99%, and the total output of the algorithm precision is 89/244%, respectively. Manuscript profile

Open Access Article

2 - A Unicast Tree-Based Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks
Zeynab Mottaginia Ali Ghaffari

10.7508/jist.2016.01.008

The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing proto More

The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing protocols cannot be applied usefully in such challenging network conditions because of intermittent connections and/or long delays. Hence, this research is intended to propose a Unicast Tree-based Data Gathering protocol (UTDG) to resolve this problem. A UTDG includes 3 phases: tree formation phase, data collection and data transmission phase, and finally the updating phase. The proposed protocol constructs a tree in each community on the basis of transmission ranking, contact probability and the link expiration time. The selection of the next-hop node is based on the tree structure rather than forwarding the message to the neighbor node directly. Each node unicasts the data to its parent in the related community, and the root of the tree successively sends the data to the sink node. The authors contend, based on the simulation results of the study, that the proposed protocol can gain significantly higher message delivery rates with lower transmission overhead and also lower delay in data delivery than the other existing DTMSNs routing protocols in some applications. Manuscript profile

Open Access Article

3 - High I/Q Imbalance Receiver Compensation and Decision Directed Frequency Selective Channel Estimation in an OFDM Receiver Employing Neural Network
afalahati afalahati Sajjad Nasirpour

10.7508/jist.2014.03.002

The disparity introduced between In-phase and Quadrature components in a digital communication system receiver known as I/Q imbalance is a prime objective within the employment of direct conversion architectures. It reduces the performance of channel estimation and caus More

The disparity introduced between In-phase and Quadrature components in a digital communication system receiver known as I/Q imbalance is a prime objective within the employment of direct conversion architectures. It reduces the performance of channel estimation and causes to receive the data symbol with errors. This imbalance phenomenon, at its lowest still can result very serious signal distortions at the reception of an OFDM multi-carrier system. In this manuscript, an algorithm based on neural network scenario, is proposed that deploys both Long Training Symbols (LTS) as well as data symbols, to jointly estimate the channel and to compensate parameters that are damaged by I/Q imbalanced receiver. In this algorithm, we have a tradeoff between these parameters. I.e. when the minimum CG mean value is required, the minimum CG mean value could be chosen without others noticing it, but in usual case we have to take into account other parameters too, the limited values for the aimed parameters must be known. It uses the first iterations to train the system to reach the suitable value of GC without error floor. In this present article, it is assumed that the correlation between subcarriers is low and a few numbers of training and data symbols are used. The simulation results show that the proposed algorithm can compensate the high I/Q imbalance values and estimate channel frequency response more accurately compared with to date existing methods. Manuscript profile

Open Access Article

4 - A New Cooperative Approach for Cognitive Radio Networks with Correlated Wireless Channels
Mehdi Ghamari Adian Hassan Aghaeenia

10.7508/jist.2013.03.003

An effective cooperative cognitive radio system is proposed, when the wireless channels are highly correlated. The system model consists of two multi-antenna secondary users (SU TX and SU RX), constituting the desired link and some single-antenna primary and secondary u More

An effective cooperative cognitive radio system is proposed, when the wireless channels are highly correlated. The system model consists of two multi-antenna secondary users (SU TX and SU RX), constituting the desired link and some single-antenna primary and secondary users. The objective is the maximization of the data rates of the desired SU link subject to the interference constraints on the primary users. An effective system, exploiting Transmit Beamforming (TB) at SU TX, cooperation of some single-antenna SUs and Cooperative Beamforming (CB) at them and the antenna selection at SU RX to reduce the costs associated with RF-chains at the radio front end at SU RX, is proposed. Due to the issue of MIMO channels with correlated fading, some problems arise such as inapplicability of the well-known Grassmanian Beamforming as TB scheme at SU TX. We then propose a method to overcome this problem. After formulating the problem, a novel iterative scheme is proposed to find the best TB weight vector in SU TX and best subset of antennas at SU RX, considering the correlated channel. Manuscript profile

Open Access Article

5 - Multiple Antenna Relay Beamforming for Wireless Peer to Peer Communications
Mohammad Hossein Golbon Haghighi Behrad Mahboobi Mehrdad Ardebilipour

10.7508/jist.2013.04.001

This paper deals with optimal beamforming in wireless multiple-input-multiple-output (MIMO) relay networks that involves multiple concurrent source-destination pairs with imperfect channel state information (CSI) at the relays. Our aim is the optimization of the MIMO re More

This paper deals with optimal beamforming in wireless multiple-input-multiple-output (MIMO) relay networks that involves multiple concurrent source-destination pairs with imperfect channel state information (CSI) at the relays. Our aim is the optimization of the MIMO relay weights that minimize the total relay transmit power subject to signal-to-interference-plus-noise ratio (SINR) of all destinations to be kept above a certain threshold. Since power minimization is a non-convex quadratically constrained quadratic programming (QCQP), we use semi-definite programming (SDP) relaxation of above mentioned problem by using a randomization technique. Numerical Monte Carlo simulations verify the performance gain of our proposed multiple antenna relay system in terms of transmit power and symbol error probability. Manuscript profile

Open Access Article

6 - Load Balanced Spanning Tree in Metro Ethernet Networks
Ghasem Mirjalily Samira Samadi

10.7508/jist.2014.02.007

Spanning Tree Protocol (STP) is a link management standard that provides loop free paths in Ethernet networks. Deploying STP in metro area networks is inadequate because it does not meet the requirements of these networks. STP blocks redundant links, causing the risk of More

Spanning Tree Protocol (STP) is a link management standard that provides loop free paths in Ethernet networks. Deploying STP in metro area networks is inadequate because it does not meet the requirements of these networks. STP blocks redundant links, causing the risk of congestion close to the root. As a result, STP provides poor support for load balancing in metro Ethernet networks. A solution for this problem is using multi-criteria spanning tree by considering criterions related to load balancing over links and switches. In our previous work, an algorithm named Best Spanning Tree (BST) is proposed to find the best spanning tree in a metro Ethernet network. BST is based on the computation of total cost for each possible spanning tree; therefore, it is very time consuming especially when the network is large. In this paper, two heuristic algorithms named Load Balanced Spanning Tree (LBST) and Modified LBST (MLBST) will be proposed to find the near-optimal balanced spanning tree in metro Ethernet networks. The computational complexity of the proposed algorithms is much less than BST algorithm. Furthermore, simulation results show that the spanning tree obtained by proposed algorithms is the same or similar to the spanning tree obtained by BST algorithm. Manuscript profile

Open Access Article

7 - Node to Node Watermarking in Wireless Sensor Networks for Authentication of Self Nodes
Hassan Farsi Seyed Morteza Nourian

10.7508/jist.2014.02.008

In order to solve some security issues in Wireless Sensor Networks (WSNs), node to node authentication method based on digital watermarking technique for verification of relative nodes is proposed. In the proposed method, some algorithms with low computational for gener More

In order to solve some security issues in Wireless Sensor Networks (WSNs), node to node authentication method based on digital watermarking technique for verification of relative nodes is proposed. In the proposed method, some algorithms with low computational for generation, embedding and detection of security ID are designed. The collected data packets by the nodes are marked using security ID. In the proposed method, header is used to mark the packets. Since the nature of the sensor networks is cooperative, using the head of the packets is proposed for authentication. Also using the marked head can prevent from sending and receiving fake data in the other nodes. Simulations have been performed in environments with imposing unrealistic data and having a probability from 1% to 10%. Comparing the proposed method with other methods shows that the proposed method in term of security, reducing traffic and increasing network lifetime is more effective. Manuscript profile

Open Access Article

8 - Joint Relay Selection and Power Allocation in MIMO Cooperative Cognitive Radio Networks
Mehdi Ghamari Adian Hassan Aghaeenia

10.7508/jist.2015.01.005

In this work, the issue of joint relay selection and power allocation in Underlay MIMO Cooperative Cognitive Radio Networks (U-MIMO-CCRN) is addressed. The system consists of a number of secondary users (SUs) in the secondary network and a primary user (PU) in the prima More

In this work, the issue of joint relay selection and power allocation in Underlay MIMO Cooperative Cognitive Radio Networks (U-MIMO-CCRN) is addressed. The system consists of a number of secondary users (SUs) in the secondary network and a primary user (PU) in the primary network. We consider the communications in the link between two selected SUs, referred to as the desired link which is enhanced using the cooperation of one of the existing SUs. The core aim of this work is to maximize the achievable data rate in the desired link, using the cooperation of one of the SUs which is chosen opportunistically out of existing SUs. Meanwhile, the interference due to the secondary transmission on the PU should not exceed the tolerable amount. The approach to determine the optimal power allocation, i.e. the optimal transmits covariance and amplification matrices of the SUs, and also the optimal cooperating SU is proposed. Since the proposed optimal approach is a highly complex method, a low complexity approach is further proposed and its performance is evaluated using simulations. The simulation results reveal that the performance loss due to the low complexity approach is only about 14%, while the complexity of the algorithm is greatly reduced. Manuscript profile

Open Access Article

9 - Ant Colony Scheduling for Network On Chip
Neda Dousttalab Mohammad Ali Jabraeil Jamali Ali Ghaffari

10.7508/jist.2015.02.004

The operation scheduling problem in network on chip is NP-hard; therefore effective heuristic methods are needful to provide modal solutions. This paper introduces ant colony scheduling, a simple and effective method to increase allocator matching efficiency and hence n More

The operation scheduling problem in network on chip is NP-hard; therefore effective heuristic methods are needful to provide modal solutions. This paper introduces ant colony scheduling, a simple and effective method to increase allocator matching efficiency and hence network performance, particularly suited to networks with complex topology and asymmetric traffic patterns. Proposed algorithm has been studied in torus and flattened-butterfly topologies with multiple types of traffic pattern. Evaluation results show that this algorithm in many causes has showed positive effects on reducing network delays and increased chip performance in comparison with other algorithms. Manuscript profile

Open Access Article

10 - Trust evaluation in unsupervised network: A fuzzy logic approach
Golnar Assadat Afzali Monireh Hosseini

10.7508/jist.2014.04.005

Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and secu More

Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and security of users. In this research, by using social networks as supervised networks, trust level of users is evaluated and by identifying these users in unsupervised networks, appropriate trust level is assigned to them. Manuscript profile

Open Access Article

11 - Scalable Community Detection through Content and Link Analysis in Social Networks
Zahra Arefian Mohammad Reza Khayyam Bashi

10.7508/jist.2015.04.003

Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. More

Social network analysis is an important problem that has been attracting a great deal of attention in recent years. Such networks provide users many different applications and features; as a result, they have been mentioned as the most important event of recent decades. Using features that are available in the social networks, first discovering a complete and comprehensive communication should be done. Many methods have been proposed to explore the community, which are community detections through link analysis and nodes content. Most of the research exploring the social communication network only focuses on the one method, while attention to only one of the methods would be a confusion and incomplete exploration. Community detections is generally associated with graph clustering, most clustering methods rely on analyzing links, and no attention to regarding the content that improves the clustering quality. In this paper, to scalable community detections, an integral algorithm is proposed to cluster graphs according to link structure and nodes content, and it aims finding clusters in the groups with similar features. To implement the Integral Algorithm, first a graph is weighted by the algorithm according to the node content, and then network graph is analyzed using Markov Clustering Algorithm, in other word, strong relationships are distinguished from weak ones. Markov Clustering Algorithm is proposed as a Multi-Level one to be scalable. The proposed Integral Algorithm was tested on real datasets, and the effectiveness of the proposed method is evaluated. Manuscript profile

Open Access Article

12 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
Hamideh Sadat Cheraghchi Ali Zakerolhossieni

10.7508/jist.2016.02.002

The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social More

The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript profile

Open Access Article

13 - Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM
hojatallah hamidi Atefeh Daraei

10.7508/jist.2016.02.003

Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effe More

Although decreasing rate of death in developed countries because of Myocardial Infarction, it is turned to the leading cause of death in developing countries. Data mining approaches can be utilized to predict occurrence of Myocardial Infarction. Because of the side effects of using Angioplasty as main method for diagnosing Myocardial Infarction, presenting a method for diagnosing MI before occurrence seems really important. This study aim to investigate prediction models for Myocardial Infarction, by applying a feature selection model based on Wight by SVM and genetic algorithm. In our proposed method, for improving the performance of classification algorithm, a hybrid feature selection method is applied. At first stage of this method, the features are selected based on their weights, using weight by Support Vector Machine. At second stage, the selected features, are given to genetic algorithm for final selection. After selecting appropriate features, eight classification methods, include Sequential Minimal Optimization, REPTree, Multi-layer Perceptron, Random Forest, K-Nearest Neighbors and Bayesian Network, are applied to predict occurrence of Myocardial Infarction. Finally, the best accuracy of applied classification algorithms, have achieved by Multi-layer Perceptron and Sequential Minimal Optimization. Manuscript profile

Open Access Article

14 - Coverage Improving with Energy Efficient in Wireless Sensor Networks
Amir Pakmehr Ali Ghaffari

10.7508/jist.2017.17.008

Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service qua More

Wireless sensor networks (WSNs) are formed by numerous sensors nodes that are able to sense different environmental phenomena and to transfer the collected data to the sink. The coverage of a network is one of the main discussion and one of the parameters of service quality in WSNs. In most of the applications, the sensor nodes are scattered in the environment randomly that causes the density of the nodes to be high in some regions and low in some other regions. In this case, some regions are not covered with any nodes of the network that are called covering holes. Moreover, creating some regions with high density causes extra overlapping and consequently the consumption of energy increases in the network and life of the network decreases. The proposed approach causes an increase in life of the network and an increase in it through careful selection of the most appropriate approach as cluster head node and form clusters with a maximum length of two steps and selecting some nodes as redundancy nodes in order to cover the created holes in the network. The proposed scheme is simulated using MATLAB software. The function of the suggested approach will be compared with Learning Automata based Energy Efficient Coverage protocol (LAEEC) approach either. Simulation results shows that the function of the suggested approach is better than LAEEC considering the parameters such as average of the active nodes, average remaining energy in nodes, percent of network coverage and number of control packets. Manuscript profile

Open Access Article

15 - Analysis of expert finding algorithms in social network in order to rank the top algorithms
AhmadAgha kardan Behnam Bozorgi

10.7508/jist.2017.20.002

The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed f More

The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to other users’ questions. These communities designed for knowledge-sharing aim to improve user knowledge, making it imperative to have a mechanism that can evaluate users’ knowledge level or in other words “to find experts”. There is a need for expert-finding algorithms in social networks or any other knowledge sharing environment like question and answer communities. There are various content analysis and link analysis methods for expert-finding in social networks. This paper aims to challenge four algorithms by applying them to our dataset and analyze the results in order to compare the algorithms. The algorithms suitable for expert finding has been found and ranked. Based on the results and tests it is concluded that the Z-score algorithm has a better performance than others. Manuscript profile

Open Access Article

16 - Node Classification in Social Network by Distributed Learning Automata
Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda

10.7508/jist.2017.18.005

The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitio More

The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved. Manuscript profile

Open Access Article

17 - A Novel Resource Allocation Algorithm for Heterogeneous Cooperative Cognitive Radio Networks
Mehdi Ghamari Adian

10.7508/jist.2017.18.008

In cognitive radio networks (CRN), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary us More

In cognitive radio networks (CRN), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users (PUs). Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity is a current research focus and in this work, cooperative diversity is investigated as a promising solution for this problem. Cooperative diversity has the capability to achieve diversity gain for wireless networks. Thus, in this work, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied carefully. To accomplish this, a resource allocation (RA) model is developed and analyzed for the heterogeneous, cooperative CRN. In the considered model, during cooperation, a best relay is selected to assist the secondary users (SUs) that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the SUs to improve their transmission rates while still causing minimal harm to the PUs. The results show a remarkable improvement in the RA performance of the CRN when cooperation is employed in contrast to when the CRN operates only by direct communication. Manuscript profile

Open Access Article

18 - Data Aggregation Tree Structure in Wireless Sensor Networks Using Cuckoo Optimization Algorithm
Elham Mohsenifard Behnam Talebi

10.7508/jist.2016.03.006

Wireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be rech More

Wireless sensor networks (WSNs) consist of numerous tiny sensors which can be regarded as a robust tool for collecting and aggregating data in different data environments. The energy of these small sensors is supplied by a battery with limited power which cannot be recharged. Certain approaches are needed so that the power of the sensors can be efficiently and optimally utilized. One of the notable approaches for reducing energy consumption in WSNs is to decrease the number of packets to be transmitted in the network. Using data aggregation method, the mass of data which should be transmitted can be remarkably reduced. One of the related methods in this approach is the data aggregation tree. However, it should be noted that finding the optimization tree for data aggregation in networks with one working-station is an NP-Hard problem. In this paper, using cuckoo optimization algorithm (COA), a data aggregation tree was proposed which can optimize energy consumption in the network. The proposed method in this study was compared with genetic algorithm (GA), Power Efficient Data gathering and Aggregation Protocol- Power Aware (PEDAPPA) and energy efficient spanning tree (EESR). The results of simulations which were conducted in matlab indicated that the proposed method had better performance than GA, PEDAPPA and EESR algorithm in terms of energy consumption. Consequently, the proposed method was able to enhance network lifetime. Manuscript profile

Open Access Article

19 - A Hybrid Cuckoo Search for Direct Blockmodeling
Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour

10.7508/jist.2017.18.001

As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit More

As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript profile

Open Access Article

20 - Crisis management using spatial query processing in wireless sensor networks
mohammad shakeri seyyed majid mazinani

10.7508/jist.2017.18.004

Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real chal More

Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real challenge in crisis management is the inability to timely receive the information from the stricken areas. Technology has come to the aid of crisis management programs to help find an answer to the problem. One of these technologies is wireless sensor network. With recent advances in this field, sensor nodes can independently respond to the queries from the users. This has transformed the processing of the queries into one of the most useful chapters in sensor networks. Without requiring any infrastructure, the sensor network can easily be deployed in the stricken area. And with the help of spatial query processing, it can easily provide managers with the latest information. The main problem, however, is the irregular shape of the area. Since these areas require many points to present them, the transmission of the coordinates by sensor nodes necessitates an increase in the number of data packet transmissions in the sensor network. The high number of packets considerably increases energy consumption. In related previous works, to solve this problem, line simplification algorithm s, such as Ramer-Douglas-Peucker (RDP), were used. These algorithms could lessen energy consumption by reducing the number of points in the shape of the area. In this article, we present a new algorithm to simplify packet shapes which can reduce more points with more accuracy. This results in decreasing the number of transmitted packets in the network, the concomitant reduction of energy consumption, and, finally, increasing network lifetime. Our proposed method was implemented in different scenarios and could on average reduce network’s energy consumption by 72.3%, while it caused only 4.5% carelessness which, when compared to previous methods, showed a far better performance. Manuscript profile

Open Access Article

21 - A Model for Mobile Code Computing Paradigms in Computer Networks
Hojatallah Hamidi Maryam Parvini

10.7508/jist.2017.17.002

This paper presents a reliable model for mobile codes in distributed networks, which represents reliable mobile agent execution. The model ensures non-blocking mobile agent execution and forces the once property without relying on correct fault detection. A mobile agent More

This paper presents a reliable model for mobile codes in distributed networks, which represents reliable mobile agent execution. The model ensures non-blocking mobile agent execution and forces the once property without relying on correct fault detection. A mobile agent execution is blocking if a fault of agent prevents the agent from continuing in its execution. The once problem is related to non-blocking in the sense that solutions to the latter may lead to multiple executions of the mobile agent. A solution to reliable mobile agent execution needs to ensure both the non-blocking and once properties. The analytical results show new theoretical perceptions into the statistical behaviors of mobile agents and provide useful tools for executing mobile agents in networks. The results show that agents' behavior is influenced by places' characteristics and the agents' behavior can be managed to network. In this paper, we analyzed the average time consuming of mobile agents between two places. The approach, Fault-Tolerant approach for mobile codes offers a user-transparent fault tolerance which can be selected by the user for every single application given to the environment. Thereby, the user can decide for every application weather it has to be treated fault-tolerant or not. We proposed a reliable execution model of mobile codes and analyzed the life expectancy, including the average time consuming of mobile agents between two places, the average number of places agents will visit, and the agents' life expectancy. Manuscript profile

Open Access Article

22 - The Separation of Radar Clutters using Multi-Layer Perceptron
Mohammad Akhondi Darzikolaei Ataollah Ebrahimzadeh Elahe Gholami

10.7508/jist.2017.17.006

Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complica More

Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complicated issues. Therefore, in this paper aims to classify radar clutters. The novel proposed MLP-based classifier for separating radar clutters is introduced. This classifier is designed with different hidden layers and five training algorithms. These training algorithms consist of Levenberg-Marquardt, conjugate gradient, resilient back-propagation, BFGS and one step secant algorithms. Statistical distributions are established models which widely used in the performance calculations of radar clutters. Hence In this research, Rayleigh, Log normal, Weibull and K-distribution clutters are utilized as input data. Then Burg’s reflection coefficients, skewness and kurtosis are three features which applied to extract the best characteristics of input data. In the next step, the proposed classifier is tested in different conditions and the results represent that the proposed MLP-based classifier is very successful and can distinguish clutters with high accuracy. Comparing the results of proposed technique and RBF-based classifier show that proposed method is more efficient. The results of simulations prove that the validity of MLP-based method. Manuscript profile

Open Access Article

23 - Toward Energy-Aware Traffic Engineering in Intra-Domain IP Networks Using Heuristic and Meta-Heuristics Approaches
Muharram Mansoorizadeh

10.7508/jist.2018.02.005

Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization i More

Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization in internet service providers is about 30-40%. Energy-aware traffic engineering aims to change routing algorithms so that low utilized links would be deactivated and their load would be distributed over other routes. As a consequence, by turning off these links and their respective devices and ports, network energy consumption is significantly decreased. In this paper, we propose four algorithms for energy-aware traffic engineering in intra-domain networks. Sequential Link Elimination (SLE) removes links based on their role in maximum network utilization. As a heuristic method, Extended Minimum Spanning Tree (EMST) uses minimum spanning trees to eliminate redundant links and nodes. Energy-aware DAMOTE (EAD) is another heuristic method that turns off links with low utilization. The fourth approach is based on genetic algorithms that randomly search for feasible network architectures in a potentially huge solution space. Evaluation results on Abilene network with real traffic matrix indicate that about 35% saving can be obtained by turning off underutilized links and routers on off-peak hours with respect to QoS. Furthermore, experiments with GA confirm that a subset of links and core nodes with respect to QoS can be switched off when traffic is in its off-peak periods, and hence energy can be saved up to 37%. Manuscript profile

Open Access Article

24 - Representing a Content-based link Prediction Algorithm in Scientific Social Networks
Hosna Solaimannezhad omid fatemi

10.7508/jist.2017.19.001

Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. A More

Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, link prediction means predicting edges that will be created between nodes in the future. The output of link prediction algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on link prediction that use content published by nodes for predicting collaboration between them. In this study, a new link prediction algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this algorithm predicts their communication in future. The results of tests on SID dataset as coauthor dataset show that developed algorithm outperforms all the structure-based link prediction algorithms. Finally, the reasons of algorithm’s efficiency are analyzed and presented Manuscript profile

Open Access Article

25 - Analysis of Business Customers’ Value Network Using Data Mining Techniques
Forough Farazzmanesh (Isvand) Monireh Hosseini

10.7508/jist.2017.19.003

In today's competitive environment, customers are the most important asset to any company. Therefore companies should understand what the retention and value drivers are for each customer. An approach that can help consider customers‘ different value dimensions is the More

In today's competitive environment, customers are the most important asset to any company. Therefore companies should understand what the retention and value drivers are for each customer. An approach that can help consider customers‘ different value dimensions is the value network. This paper aims to introduce a new approach using data mining techniques for mapping and analyzing customers‘ value network. Besides, this approach is applied in a real case study. This research contributes to develop and implement a methodology to identify and define network entities of a value network in the context of B2B relationships. To conduct this work, we use a combination of methods and techniques designed to analyze customer data-sets (e.g. RFM and customer migration) and to analyze value network. As a result, this paper develops a new strategic network view of customers and discusses how a company can add value to its customers. The proposed approach provides an opportunity for marketing managers to gain a deep understanding of their business customers, the characteristics and structure of their customers‘ value network. This paper is the first contribution of its kind to focus exclusively on large data-set analytics to analyze value network. This new approach indicates that future research of value network can further gain the data mining tools. In this case study, we identify the value entities of the network and its value flows in the telecommunication organization using the available data in order to show that it can improve the value in the network by continuous monitoring. Manuscript profile

Open Access Article

26 - Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
Ramin Safa Seyed Abolghassem Mirroshandel Soroush Javadi Mohammad Azizi

10.7508/jist.2018.21.005

Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying More

Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations. Manuscript profile

Open Access Article

27 - Lifetime Maximization by Dynamic Threshold and Sensor Selection in Multi-channel Cognitive Sensor Network
Asma Bagheri Ataollah Ebrahimzadeh maryam najimi

10.7508/jist.2017.20.003

The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor netw More

The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption. Manuscript profile

Open Access Article

28 - Clustering for Reduction of Energy Consumption in Wireless Sensor Networks by AHP Method
Mohammad Reza Taghva Robab Hamlbarani Haghi Aziz Hanifi Kamran feizi

10.7508/jist.2018.21.002

Due to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of t More

Due to the type of applications, wireless sensor nodes must always be energy efficient and small. Hence, some studies have been done in order to the reduction in energy consumption. Data collection in wireless sensor networks is one of the most important operations of these networks. Due to the energy limitation of nodes, energy efficiency is considered as a key objective in the design of sensor networks. In this paper, we present a method in which, in the first phase, nodes obtain their position by using the position of the base station and two other two nodes informed geographic position and are out of covered environment. In the second phase, the optimal location of the base station is determined. In the third phase, we determine the cluster heads based on the criteria such as the remaining energy, the distance (the distance from the cluster head and the distance from the base station), the number of neighbors (the one-step neighbors and the two-step neighbors) and the centrality. Using the multi-as criteria to select optimally cluster heads by decision making method. We implement the proposed method in the NS2 environment and evaluate its effect and compare it with the NEECP E-LEACH protocols. Simulation results show that by reducing energy consumption, the proposed method enhances the network life time expectancy. In addition it improves average packet delivery and the average delay. Manuscript profile

Open Access Article

29 - A New Set Covering Controller Placement Problem Model for Large Scale SDNs
احمد جلیلی رضا اکبری منیژه کشتگری

10.7508/jist.2018.21.004

Software Defined Network (SDN) is an emerging architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler and manageable network. New SDN paradigms are encouraged to deploy multiple (rather than centra More

Software Defined Network (SDN) is an emerging architecture that can overcome the challenges facing traditional networks. SDN enables administrator/operator to build a simpler and manageable network. New SDN paradigms are encouraged to deploy multiple (rather than centralized) controllers to monitor the entire system. The Controller Placement Problem (CPP) is one of the key issues in SDN that affects every aspect of it such as scalability, convergence time, fault tolerance and node to controller latency. This problem has been investigated in diverse papers with their major attention paid on optimizing the location of an arbitrary number of controllers. The related works in this area get less attention to two following important issues. i) Bidirectional end-to-end latency between switch and its controller instead of propagation latency, ii) finding the minimal number of controllers that even is a prerequisite for locating them. In this paper, a Set Covering Controller Placement Problem Model (SCCPPM) to find the least number of required controllers with regard to carrier grade latency requirement is proposed. The new model is carried out on a set of 124 graphs from the Internet Topology Zoo and solve them with IBM ILOG CPLEX Optimization package. As expected, our results indicate that the number of required controllers for high resiliency is dependent on topology and network size. As well, in order to achieve carrier grade requirement, 86 percent of topologies must have more than one controller. Manuscript profile

Open Access Article

30 - Security Enhancement of Wireless Sensor Networks: A Hybrid Efficient Encryption Algorithm Approach
Omid Mahdi Ebadati Farshad Eshghi Amin Zamani

10.7508/jist.2018.03.007

Wireless sensor networks are new technologies that are used for various purposes such as environmental monitoring, home security, industrial process monitoring, healthcare programs and etc. Wireless sensor networks are vulnerable to various attacks. Cryptography is one More

Wireless sensor networks are new technologies that are used for various purposes such as environmental monitoring, home security, industrial process monitoring, healthcare programs and etc. Wireless sensor networks are vulnerable to various attacks. Cryptography is one of the methods for secure transmission of information between sensors in wireless sensor networks. A complete and secure encryption system must establish three principles of confidentiality, authentication and integrity. An encryption algorithm alone cannot provide all the principles of encryption. A hybrid encryption algorithm, consisting of symmetric and asymmetric encryption algorithms, provides complete security for a cryptographic system. The papers presented in this area over the last few years, and a new secure algorithm present with regard to the limitations of wireless sensor networks, which establishes three principles of cryptography. The details of the algorithm and basic concepts are presented in such a way that the algorithm can be operational and showed a very high efficiency in compare to the current proposed methods. Manuscript profile

Open Access Article

31 - Lifetime Improvement Using Cluster Head Selection and Base Station Localization in Wireless Sensor Networks
maryam najimi Sajjad Nankhoshki

10.7508/jist.2018.02.006

The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most eff More

The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the cluster heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy- efficient algorithm, cluster heads distance from the base station and their residual energy of the sensors nodes are important parameters for cluster head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations. Manuscript profile

Open Access Article

32 - An SRN Based Approach for Performance Evaluation of Network Layer in Mobile Ad hoc Networks
meisam Yadollahzadeh tabari Ali A Pouyan

10.7508/jist.2018.04.006

The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing More

The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing high level performance metrics. Also there is no theoretical background for mentioned simulators, too. In this research, we propose a framework for performance evaluation of mobile ad hoc networks. The presented framework points to the network layer of MANETs using SRN (Stochastic Reward Nets) modeling tool as variation of generalized stochastic Petri net (GSPN). Based on decomposition technique it encompasses two separate models: one for analysis of data flowing process and the other for modeling routing process ; supposing AODV as a routing protocol that is worked out. To verify the presented model, an equivalence-based method is applied. The proposed SRN model has been quantified by deriving two performances metrics as Packet Delivery Ratio (PDR) and End-to-end Delay. Both metrics are also compared to the value obtained from NS-2 simulator versus different number of nodes and four packet generation rates. The results show the obtained values from presented SRN model well matched to the values generated from NS-2 simulator with a considerable lesser execution time. Manuscript profile

Open Access Article

33 - Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder
Hanieh Zamanian Hassan Farsi Sajad Mohammadzadeh

10.7508/jist.2018.03.002

Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predic More

Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method. Manuscript profile

Open Access Article

34 - A Multi-objective Multi-agent Optimization Algorithm for the Community Detection Problem
Amirhossein Hosseinian Vahid Baradaran

10.7508/jist.2018.03.006

This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, whil More

This paper addresses the community detection problem as one of the significant problems in the field of social network analysis. The goal of the community detection problem is to find sub-graphs of a network where they have high density of within-group connections, while they have a lower density of between-group connections. Due to high practical usage of community detection in scientific fields, many researchers developed different algorithms to meet various scientific requirements. However, single-objective optimization algorithms may fail to detect high quality communities of complex networks. In this paper, a novel multi-objective Multi-agent Optimization Algorithm, named the MAOA is proposed to detect communities of complex networks. The MAOA aims to optimize modularity and community score as objective functions, simultaneously. In the proposed algorithm, each feasible solution is considered as an agent and the MAOA organizes agents in multiple groups. The MAOA uses new search operators based on social, autonomous and self-learning behaviors of agents. Moreover, the MAOA uses the weighted sum method (WSM) in finding the global best agent and leader agent of each group. The Pareto solutions obtained by the MAOA is evaluated in terms of several performance measures. The results of the proposed method are compared with the outputs of three meta-heuristics. Experiments results based on five real-world networks show that the MAOA is more efficient in finding better communities than other methods. Manuscript profile

Open Access Article

35 - A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
Abbas Mirzaei Amir Rahimi

10.7508/jist.2019.01.005

One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided More

One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile

Open Access Article

36 - SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications
Morteza Jaderyan Hassan Khotanlou

10.7508/jist.2019.02.004

The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text analysis applications is introduced. The proposed functionalities of the system are achieved by int More

The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text analysis applications is introduced. The proposed functionalities of the system are achieved by integrating structured knowledge in the core components of the system. The semantic, lexical, syntactical and structural features are identified by the pre-processing module. The enrichment module is introduced to identify contextually similar concepts and concept maps for improving the representation. The information content of documents and the enriched contents are then fused (merged) into the graphical structure of a semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 datasets are used for evaluation. The evaluation results suggest that the proposed method exhibits a high level of accuracy, recall and precision. The results also indicate that even when a small portion of the information content is available, the proposed method performs well in standard text mining applications Manuscript profile

Open Access Article

37 - Investigate Network Simulation Tools in Designing and Managing Intelligent Systems
fatemeh fakhar

10.7508/jist.2019.04.004

Network simulation is a technique that models network behavior by performing transaction calculations between different network entities and using mathematical formulas and taking observations of network products. A network simulator is a software program have been appl More

Network simulation is a technique that models network behavior by performing transaction calculations between different network entities and using mathematical formulas and taking observations of network products. A network simulator is a software program have been applied to analyze the performance of a computer network without the presence of a real network. Hardware equipment, equipment configuration, communication, and routing protocols and network traffic modeled in simulation software and the behavior of the network and its components examined from different dimensions. The user can also customize the simulation software according to their needs. Simulation software has different uses, and the user can use these tools to model their network by recognizing this software. In terms of research, it is difficult to create a network, especially large networks, in a real-time scenario, and it is not easily possible to carry out it in the real world, and it is very costly. So, simulators help network developers to control whether the network can work in real-time or not, or whether it is efficient enough. This reduces the time and cost of network application testing.Today, simulation technology is successfully used to model, design and manage a variety of intelligent systems. Numerous tools have been created in this regard. In this article, we review and compare important network simulators such as CloudSim, GloMoSim, GNS3, NS-2, Opnet, OMNet ++, NetSim, NS-3, AVRORA, Packet Tracer, QualNet, J-Sim, REAL and OptSim and their results. These comparisons express from several perspectives in the tables. Manuscript profile

Open Access Article

38 - DeepSumm: A Novel Deep Learning-Based Multi-Lingual Multi-Documents Summarization System
Shima Mehrabi Seyed Abolghassem Mirroshandel Hamidreza Ahmadifar

10.7508/jist.2019.03.005

With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language pro More

With the increasing amount of accessible textual information via the internet, it seems necessary to have a summarization system that can generate a summary of information for user demands. Since a long time ago, summarization has been considered by natural language processing researchers. Today, with improvement in processing power and the development of computational tools, efforts to improve the performance of the summarization system is continued, especially with utilizing more powerful learning algorithms such as deep learning method. In this paper, a novel multi-lingual multi-document summarization system is proposed that works based on deep learning techniques, and it is amongst the first Persian summarization system by use of deep learning. The proposed system ranks the sentences based on some predefined features and by using a deep artificial neural network. A comprehensive study about the effect of different features was also done to achieve the best possible features combination. The performance of the proposed system is evaluated on the standard baseline datasets in Persian and English. The result of evaluations demonstrates the effectiveness and success of the proposed summarization system in both languages. It can be said that the proposed method has achieve the state of the art performance in Persian and English. Manuscript profile

Open Access Article

39 - BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers
Hasibeh Naseri Sadoon Azizi Alireza Abdollahpouri

10.7508/jist.2019.03.002

To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data cent More

To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data center design. Flow-based mechanisms usually suffer from collision between elephant flows; while, packet-based mechanisms encounter packet re-ordering phenomenon. Both of these challenges lead to severe performance degradation in a data center network. To address these problems, in this paper, we propose an efficient mechanism for the flow scheduling problem in cloud data center networks. The proposed mechanism, on one hand, makes decisions per flow, thus preventing the necessity for rearrangement of packets. On the other hand, thanks do SDN technology and utilizing bidirectional search algorithm, our proposed method is able to distribute elephant flows across the entire network smoothly and with a high speed. Simulation results confirm the outperformance of our proposed method with the comparison of state-of-the-art algorithms under different traffic patterns. In particular, compared to the second-best result, the proposed mechanism provides about 20% higher throughput for random traffic pattern. In addition, with regard to flow completion time, the percentage of improvement is 12% for random traffic pattern Manuscript profile

Open Access Article

40 - Using Residual Design for Key Management in Hierarchical Wireless Sensor Networks
Vahid Modiri Hamid Haj Seyyed Javadi Amir Masoud Rahmani Mohaddese Anzani

10.7508/jist.2020.01.006

Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial stru More

Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial structures in which key-rings, are pre-distributed to each sensor node before deployment in a real environment. Regarding the restricted resources, key distribution is a great engagement and challenging issue in providing sufficient security in wireless sensor networks. To provide secure communication, a unique key should be found from their stored key-rings. Most of the key pre-distribution protocols based on public-key mechanisms could not support highly scalable networks due to their key storage overhead and communication cost that linearly increasing. In this paper, we introduce a new key distribution approach for hierarchical clustered wireless sensor networks. Each cluster has a construction that contains new points or that reinforces and builds upon similar ideas of their head clusters. Based on Residual Design as a powerful algebraic combinatorial architecture and hierarchical network model, our approach guarantees good connectivity between sensor nodes and also cluster heads. Compared with similar existing schemes, our approach can provide sufficient security no matter if the cluster head or normal sensor node is compromised Manuscript profile

Open Access Article

41 - Energy Efficient Clustering Algorithm for Wireless Sensor Networks
Maryam Bavaghar Amin Mohajer Sarah Taghavi Motlagh

10.7508/jist.2019.04.001

In Wireless Sensor Networks (WSNs), sensor nodes are usually deployed with limited energy reserves in remote environments for a long period of time with less or no human intervention. It makes energy efficiency as a challenging issue both for the design and deployment o More

In Wireless Sensor Networks (WSNs), sensor nodes are usually deployed with limited energy reserves in remote environments for a long period of time with less or no human intervention. It makes energy efficiency as a challenging issue both for the design and deployment of sensor networks. This paper presents a novel approach named Energy Efficient Clustering Algorithm (EECA) for Wireless Sensor Networks which is based on two phases clustering model and provides maximum network coverage in an energy efficient way. In this framework, an effective resource-aware load balancing approach applied for autonomous methods of configuring the parameters in accordance with the signaling patterns in which approximately the same bit rate data is provided for each sensor. This resource-efficient clustering model can also form energy balanced clusters which results in increasing network life time and ensuring better network coverage. Simulation results prove that EECA is better than LEACH, LEA2C and EECS with respect to network lifetime and at the same time achieving more network coverage. In addition to obtained an optimal cluster size with minimum energy loss, the proposed approach also suggests new and better way for selecting cluster heads to reduce energy consumption of the distributed nodes resulting in increased operational reliability of sensor networks. Manuscript profile

Open Access Article

42 - Drone Detection by Neural Network Using GLCM and SURF Features
Tanzia Ahmed Tanvir Rahman Bir Ballav Roy Jia Uddin

10.52547/jist.9.33.15

20.1001.1.23221437.2021.9.33.3.8

This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extrac More

This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error. Manuscript profile

Open Access Article

43 - Recognizing Transliterated English Words in Persian Texts
Ali Hoseinmardy Saeedeh Momtazi

10.29252/jist.8.30.84

One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classific More

One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classification and clustering. In this case, if the text-processing engine does not use similar/related words in the same sense, it may not be able to guide you to the appropriate result. Various statistical techniques have been proposed to bridge the vocabulary gap problem; e.g., if two words are used in similar contexts frequently, they have similar/related meanings. Synonym and similar words, however, are only one of the categories of related words that are expected to be captured by statistical approaches. Another category of related words is the pair of an original word in one language and its transliteration from another language. This kind of related words is common in non-English languages. In non-English texts, instead of using the original word from the target language, the writer may borrow the English word and only transliterate it to the target language. Since this kind of writing style is used in limited texts, the frequency of transliterated words is not as high as original words. As a result, available corpus-based techniques are not able to capture their concept. In this article, we propose two different approaches to overcome this problem: (1) using neural network-based transliteration, (2) using available tools that are used for machine translation/transliteration, such as Google Translate and Behnevis. Our experiments on a dataset, which is provided for this purpose, shows that the combination of the two approaches can detect English words with 89.39% accuracy. Manuscript profile

Open Access Article

44 - Energy Efficient Cross Layer MAC Protocol for Wireless Sensor Networks in Remote Area Monitoring Applications
R Rathna L Mary Gladence J Sybi Cynthia V Maria Anu

10.52547/jist.9.35.207

20.1001.1.23221437.2021.9.35.6.5

Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless d More

Sensor nodes are typically less mobile, much limited in capabilities, and more densely deployed than the traditional wired networks as well as mobile ad-hoc networks. General Wireless Sensor Networks (WSNs) are designed with electro-mechanical sensors through wireless data communication. Nowadays the WSN has become ubiquitous. WSN is used in combination with Internet of Things and in many Big Data applications, it is used in the lower layer for data collection. It is deployed in combination with several high end networks. All the higher layer networks and application layer services depend on the low level WSN in the deployment site. So to achieve energy efficiency in the overall network some simplification strategies have to be carried out not only in the Medium Access Control (MAC) layer but also in the network and transport layers. An energy efficient algorithm for scheduling and clustering is proposed and described in detail. The proposed methodology clusters the nodes using a traditional yet simplified approach of hierarchically sorting the sensor nodes. Few important works on cross layer protocols for WSNs are reviewed and an attempt to modify their pattern has also been presented in this paper with results. Comparison with few prominent protocols in this domain has also been made. As a result of the comparison one would get a basic idea of using which type of scheduling algorithm for which type of monitoring applications. Manuscript profile

Open Access Article

45 - Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory
Nazbanoo Farzaneh Reyhaneh Hoseini

10.52547/jist.9.33.25

20.1001.1.23221437.2021.9.33.4.9

DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a secur More

DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a security unit, is placed on the control plane of the software-defined wireless sensor network aiming at detecting the suspicious traffic. The main purpose of this paper is detection of the DDoS attack using the central controller of the software-defined network and entropy approach as an effective light-weight and quick solution in the early stages of the detection and, also, Dempster-Shafer theory in order to do a more exact detection with longer time. Evaluation of the attacks including integration of data from the evidence obtained using Dempster-Shafer and entropy modules has been done with the purpose of increasing the rate of detection of the DDoS attack, maximizing the true positive, decreasing the false negative, and confronting the attack. The results of the paper show that providing a security unit on the control plane in a software-defined wireless sensor network is an efficient method for detecting and evaluating the probability of DDoS attacks and increasing the rate of detection of an attacker. Manuscript profile

Open Access Article

46 - Providing a Network for Measuring the Dynamics Volatility Connectedness of Oil and Financial Markets
Nasser Gholami Teymor Mohammadi Hamid Amadeh Morteza Khorsandi

10.29252/jist.8.31.149

20.1001.1.23221437.2020.8.31.6.0

Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure t More

Various studies have shown that markets are not separated and that fluctuations in different markets affect each other. Therefore, awareness of connectedness is needed for investors and policymakers for making appropriate decisions. The aim of this paper is to measure the dynamics connectedness of selected stock markets in the Middle East, oil markets, gold, the dollar index, and euro-dollar and pound-dollar exchange rates during the period February 2007 to August 2019 in networks with different weekly horizons. In this paper, we intend to evaluate the pairwise impact of crude oil and the Middle East stock markets, in particular on the Tehran Stock Exchange, and to analyze this variance using different time horizons. The results show that in all time horizons the variance of forecast error in most markets is due to the shocks themselves. The Saudi Arabian Stock Exchange has the most impact on other Middle Eastern stocks. The dynamics connectedness of the oil markets is remarkable, however, as the time horizon increases, dynamic connectedness between the two markets decreases and they are mostly affected by other markets, especially the Middle East stock exchanges except for Iran. Moreover, Iran stock market is an isolated market. About the gold market, there is a significant connectedness with the pound-dollar exchange rate and gold market; however, the dynamics connectedness of this market with other markets are not significant. Therefore, this market and Iran stock exchange can be used as a tool to hedge risk for investors. Manuscript profile

Open Access Article

47 - Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection
Mohammad Pouya Salvati Jamshid Bagherzadeh Mohasefi Sadegh Sulaimany

10.52547/jist.9.35.183

20.1001.1.23221437.2021.9.35.4.3

Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A maj More

Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms. Manuscript profile

Open Access Article

48 - Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification
Nidhi Chandra Laxmi Ahuja Sunil Kumar Khatri Himanshu Monga

10.52547/jist.9.34.89

20.1001.1.23221437.2021.9.34.2.9

The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time a More

The classification of text is one of the key areas of research for natural language processing. Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them. Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis. Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism. Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment. The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures. A few of the use cases that follow are – sentiment analysis for NLP. GRU with RNN is being used as it would need to retain long-term dependencies. This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network. This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text. It helps specifically inform the network of long-term dependencies. We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%. Manuscript profile

Open Access Article

49 - Sailor Localization in Oceans Beds using Genetic and Firefly Algorithm
Shruti Gupta Dr Ajay Rana Vineet Kansal

10.29252/jist.8.31.175

20.1001.1.23221437.2020.8.31.1.5

The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the More

The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the position of relative nodes in the network allowing a better and effective network for increasing the efficiency and thus increasing the lifeline of the network. Determining the current limitations in FA that are applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinity of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. This paper presents GEFIR (GenFire) algorithm to calculate position of unknown nodes for the fishermen in the ocean. The proposed approach calculates the position of unknown nodes, the proposed method effectively selects the anchor node in the cluster head to reduce the energy dissipation. Major benefits over other similar localization algorithms are a better positioning of nodes is provided and average localization error is reduced which eventually leads to better efficiency thus optimize the lifetime of the network for sailors. The obtained results depict that the proposed model surpasses the previous generation of localization algorithm in terms of energy dispersion and location estimation which is suitable for fishermen on the ocean bed. Manuscript profile

Open Access Article

50 - Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)
Mohammad Nejadeh Peyman Bayat Jalal Kheirkhah Hassan Moladoust

10.29252/jist.8.31.197

20.1001.1.23221437.2020.8.31.2.6

Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the progno More

Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables. Manuscript profile

Open Access Article

51 - Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
Elham Gholami Seyed Reza Kamel Tabbakh Maryam khairabadi

10.52547/jist.9.35.191

20.1001.1.23221437.2021.9.35.5.4

Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting nu More

Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods. Manuscript profile

Open Access Article

52 - A New Game Theory-Based Algorithm for Target Coverage in Directional Sensor Networks
Elham Golrasan marzieh varposhti

10.52547/jist.9.34.103

20.1001.1.23221437.2021.9.34.3.0

One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage q More

One of the challenging problems in directional sensor networks is maximizing target coverage while minimizing the amount of energy consumption. Considering the high redundancy in dense directional sensor networks, it is possible to preserve energy and enhance coverage quality by turning off redundant sensors and adjusting the direction of the active sensor nodes. In this paper, we address the problem of maximizing network lifetime with adjustable ranges (MNLAR) and propose a new game theory-based algorithm in which sensor nodes try to adjust their working direction and sensing range in a distributed manner to achieve the desired coverage. For this purpose, we formulate this problem as a multiplayer repeated game in which each sensor as a player tries to maximize its utility function which is designed to capture the tradeoff between target coverage and energy consumption. To achieve an efficient action profile, we present a distributed payoff-based learning algorithm. The performance of the proposed algorithm is evaluated via simulations and compared to some existing methods. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of network lifetime. Manuscript profile

Open Access Article

53 - Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks
Ali Sedighimanesh Hessam Zandhessami Mahmood Alborzi mohammadsadegh Khayyatian

10.52547/jist.15638.10.37.37

20.1001.1.23221437.2022.10.37.2.2

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sen More

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teaching-learning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion: Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime. Manuscript profile

Open Access Article

54 - Self-Organization Map (SOM) Algorithm for DDoS Attack Detection in Distributed Software Defined Network (D-SDN)
Mohsen Rafiee Alireza shirmarz

10.52547/jist.15644.10.38.120

20.1001.1.23221437.2022.10.38.2.4

The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the netw More

The extend of the internet across the world has increased cyber-attacks and threats. One of the most significant threats includes denial-of-service (DoS) which causes the server or network not to be able to serve. This attack can be done by distributed nodes in the network as if the nodes collaborated. This attack is called distributed denial-of-service (DDoS). There is offered a novel architecture for the future networks to make them more agile, programmable and flexible. This architecture is called software defined network (SDN) that the main idea is data and control network flows separation. This architecture allows the network administrator to resist DDoS attacks in the centralized controller. The main issue is to detect DDoS flows in the controller. In this paper, the Self-Organizing Map (SOM) method and Learning Vector Quantization (LVQ) are used for DDoS attack detection in SDN with distributed architecture in the control layer. To evaluate the proposed model, we use a labelled data set to prove the proposed model that has improved the DDoS attack flow detection by 99.56%. This research can be used by the researchers working on SDN-based DDoS attack detection improvement. Manuscript profile

Open Access Article

55 - Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata
Ali Ghaffari Seyyed Keyvan Mousavi

10.52547/jist.9.35.197

20.1001.1.23221437.2021.9.35.7.6

Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the d More

Network coverage is one of the most important challenges in wireless sensor networks (WSNs). In a WSN, each sensor node has a sensing area coverage based on its sensing range. In most applications, sensor nodes are randomly deployed in the environment which causes the density of nodes become high in some areas and low in some other. In this case, some areas are not covered by none of sensor nodes which these areas are called coverage holes. Also, creating areas with high density leads to redundant overlapping and as a result the network lifetime decreases. In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed. In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends them to the cluster head (CH). Afterward, each CH starts to reward or penalize the vectors and sends the results to the sender for updating purposes. Thereafter, among the sent vectors, the CH node selects the best action vector and broadcasts it in the form of a message inside the cluster. Finally, each member changes its status in accordance with the vector included in the received message from the corresponding CH and the active sensor nodes perform environment monitoring operations. The simulation results show that the proposed scheme improves the network coverage and the energy consumption. Manuscript profile

Open Access Article

56 - Providing a New Smart Camera Architecture for Intrusion Detection in Wireless Visual Sensor Network
Meisam Sharifi Sani Amid Khatibi

10.52547/jist.15672.11.41.31

20.1001.1.23221437.2023.11.41.1.6

The wireless Visual sensor network is a highly functional domain of high-potential network generations in unpredictable and dynamic environments that have been deployed from a large number of uniform or non-uniform groups within the desired area, cause the realization o More

The wireless Visual sensor network is a highly functional domain of high-potential network generations in unpredictable and dynamic environments that have been deployed from a large number of uniform or non-uniform groups within the desired area, cause the realization of large regulatory applications from the military and industrial domain to hospital and environment. Therefore, security is one of the most important challenges in these networks. In this research, a new method of routing smart cameras with the help of cloud computing technology has been provided. The framework in the cloud computing management layer increases security, routing, inter interaction, and other features required by wireless sensor networks. Systematic attacks are simulated by a series of standard data collected at the CTU University related to the Czech Republic with RapidMiner software. Finally, the accuracy of detection of attacks and error rates with the suggested NN-SVM algorithm, which is a combination of vector machines and neural networks, is provided in the smart cameras based on the visual wireless sensor networks in MATLAB software. The results show that different components of the proposed architecture meet the quality characteristics of visual wireless sensor networks. Detection of attacks in this method is in the range of 99.24% and 99.35% in the worst and best conditions, respectively. Manuscript profile

Open Access Article

57 - Secure Key Management Scheme for Hierarchical Network Using Combinatorial Design
Siddiq Iqbal B R Sujatha

10.52547/jist.15691.10.37.20

20.1001.1.23221437.2022.10.37.8.8

The wireless sensor network (WSN) signifies to a gathering of spatially spread and committed sensors for observing and logging the physical states of the environment and for organizing the information gathered at the central Base station. Many security threats may affec More

The wireless sensor network (WSN) signifies to a gathering of spatially spread and committed sensors for observing and logging the physical states of the environment and for organizing the information gathered at the central Base station. Many security threats may affect the functioning of these networks. Security of the data in the system depends on the cryptographic procedure and the methods where encryption and decryption keys are developed among the sensors. Symmetric key foundation is one of the best applicable ideal models for safe exchanges in WSNs. The main goal is to improve and evaluate certain issues, such as node attack, to provide better key strength, connectivity, security for node interaction, and throughput. Uniform Balanced Incomplete Block Design (UBIBD) is used to generate the keys allocated by the base station to the cluster head. The cluster head distributes keys to its members using Symmetric Balanced Incomplete Block Design (SBIBD), and the keys are refreshed on a regular basis to avoid out-of-date entries. In wireless sensor networks, compromised nodes can be used to inject false reports. The concept of interacting between sensor nodes using keys and establishing a secure connection aids in ensuring the network's security. Manuscript profile

Open Access Article

58 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
Alireza Mansouri Fattaneh Taghiyareh

10.52547/jist.9.33.1

20.1001.1.23221437.2021.9.33.2.7

People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke More

People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript profile

Open Access Article

59 - Deep Learning Approach for Cardiac MRI Images
Afshin Sandooghdar Farzin Yaghmaee

10.52547/jist.16121.10.37.61

20.1001.1.23221437.2022.10.37.5.5

Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an o More

Deep Learning (DL) is the most widely used image-analysis process, especially in medical image processing. Though DL has entered image processing to solve Machine Learning (ML) problems, identifying the most suitable model based on evaluation of the epochs is still an open question for scholars in the field. There are so many types of function approximators like Decision Tree, Gaussian Processes and Deep Learning, used in multi-layered Neural Networks (NNs), which should be evaluated to determine their effectiveness. Therefore, this study aimed to assess an approach based on DL techniques for modern medical imaging methods according to Magnetic Resonance Imaging (MRI) segmentation. To do so, an experiment with a random sampling approach was conducted. One hundred patient cases were used in this study for training, validation, and testing. The method used in this study was based on full automatic processing of segmentation and disease classification based on MRI images. U-Net structure was used for the segmentation process, with the use of cardiac Right Ventricular Cavity (RVC), Left Ventricular Cavity (LVC), Left Ventricular Myocardium (LVM), and information extracted from the segmentation step. With train and using random forest classifier, and Multilayer Perceptron (MLP), the task of predicting the pathologic target class was conducted. Segmentation extracted information was in the form of comprehensive features handcrafted to reflect demonstrative clinical strategies. Our study suggests 92% test accuracy for cardiac MRI image segmentation and classification. As for the MLP ensemble, and for the random forest, test accuracy was equal to 91% and 90%, respectively. This study has implications for scholars in the field of medical image processing. Manuscript profile

Open Access Article

60 - A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques
Abbas Mirzaei Shahram Zandian

10.52547/jist.16302.10.40.300

20.1001.1.23221437.2022.10.40.3.9

Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomen More

Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput. Manuscript profile

Open Access Article

61 - Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO
Mohammad Nazarpour navid nezafati Sajjad Shokouhyar

10.52547/jist.16307.10.40.270

20.1001.1.23221437.2022.10.40.4.0

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detect More

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB. Manuscript profile

Open Access Article

62 - Edge Detection and Identification using Deep Learning to Identify Vehicles
Zohreh Dorrani Hassan Farsi Sajad Mohammadzadeh

10.52547/jist.16385.10.39.201

20.1001.1.23221437.2022.10.39.2.6

A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract informati More

A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods. Manuscript profile

Open Access Article

63 - Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs)
Sara Motamed Elham Askari

10.52547/jist.16399.10.39.222

20.1001.1.23221437.2022.10.39.3.7

Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might More

Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might interfere with your own learning, work, and communication with others, it could be controlled by early diagnosis and treatment. Because the automatic recognition and classification of electroencephalography (EEG) signals is challenging due to the large variation in time features and signal frequency, the present study attempts to provide an efficient method for diagnosing hyperactive patients. The proposed method is that first, the recorded brain signals of hyperactive subjects are read from the input and in order to the signals to be converted from time range to frequency range, Fast Fourier Transform (FFT) is used. Also, to select an effective feature to check hyperactive subjects from healthy ones, the peak frequency (PF) is applied. Then, to select the features, principal component analysis and without principal component analysis will be used. In the final step, convolutional neural networks (CNNs) will be utilized to calculate the recognition rate of individuals with hyperactivity. For model efficiency, this model is compared to the models of K- nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the best method is to use feature selection by principal component analysis and classification of CNNs and the recognition rate of individuals with ADHD from healthy ones is equal to 91%. Manuscript profile

Open Access Article

64 - Hierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
Nagesh Jadhav

10.52547/jist.16499.10.38.89

20.1001.1.23221437.2022.10.38.8.0

Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distres More

Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distress in humans. Personalized affective cues provide a non-invasive approach considering visual, vocal, and verbal cues to recognize the affective state. In this paper, we are proposing a multimodal hierarchical weighted framework to recognize emotional distress. We are utilizing negative emotions to detect the unapparent behavior of the person. To capture facial cues, we have employed hybrid models consisting of a transfer learned residual network and CNN models. Extracted facial cue features are processed and fused at decision using a weighted approach. For audio cues, we employed two different models exploiting the LSTM and CNN capabilities fusing the results at the decision level. For textual cues, we used a BERT transformer to learn extracted features. We have proposed a novel decision level adaptive hierarchical weighted algorithm to fuse the results of the different modalities. The proposed algorithm has been used to detect the emotional distress of a person. Hence, we have proposed a novel algorithm for the detection of emotional distress based on visual, verbal, and vocal cues. Experiments on multiple datasets like FER2013, JAFFE, CK+, RAVDESS, TESS, ISEAR, Emotion Stimulus dataset, and Daily-Dialog dataset demonstrates the effectiveness and usability of the proposed architecture. Experiments on the enterface'05 dataset for distress detection has demonstrated significant results. Manuscript profile

Open Access Article

65 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
hadi shakibian Jalaledin Nasiri

10.52547/jist.19789.10.40.259

20.1001.1.23221437.2022.10.40.5.1

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile

Open Access Article

66 - Mathematical Modeling of Flow Control Mechanism in Wireless Network-on-Chip
Fardad Rad Marzieh Gerami

10.52547/jist.19923.11.41.48

20.1001.1.23221437.2023.11.41.4.9

Network-on-chip (NoC) is an effective interconnection solution of multicore chips. In recent years, wireless interfaces (WIs) are used in NoCs to reduce the delay and power consumption between long-distance cores. This new communication structure is called wireless netw More

Network-on-chip (NoC) is an effective interconnection solution of multicore chips. In recent years, wireless interfaces (WIs) are used in NoCs to reduce the delay and power consumption between long-distance cores. This new communication structure is called wireless network-on-chip (WiNoC). Compared to the wired links, demand to use the shared wireless links leads to congestion in WiNoCs. This problem increases the average packet latency as well as the network latency. However, using an efficient control mechanism will have a great impact on the efficiency and performance of the WiNoCs. In this paper, a mathematical modeling-based flow control mechanism in WiNoCs has been investigated. At first, the flow control problem has been modeled as a utility-based optimization problem with the wireless bandwidth capacity constraints and flow rate of processing cores. Next, the initial problem has been transformed into a dual problem without limitations and the best solution of the dual problem is obtained by the gradient projection method. Finally, an iterative algorithm is proposed in a WiNoC to control the flow rate of each core. The simulation results of synthetic traffic patterns show that the proposed algorithm can control and regulate the flow rate of each core with an acceptable convergence. Hence, the network throughput will be significantly improved. Manuscript profile

Open Access Article

67 - Dynamic Tree- Based Routing: Applied in Wireless Sensor Network and IOT
Mehdi Khazaei

10.52547/jist.22504.10.39.191

20.1001.1.23221437.2022.10.39.4.8

The Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some app More

The Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some applications, the IOT is converted into WSN with the same descriptions and limitations. Working with WSN is limited to energy, memory and computational ability of the sensor nodes. This makes the energy consumption to be wise if protection of network reliability is sought. The newly developed and effective hierarchical and clustering techniques are to overcome these limitations. The method proposed in this article, regarding energy consumption reduction is tree-based hierarchical technique, used clustering based on dynamic structure. In this method, the location-based and time-based properties of the sensor nodes are applied leading to provision of a greedy method as to form the subtree leaves. The rest of the tree structure up to the root, would be formed by applying the centrality concept in the network theory by the base station. The simulation reveals that the scalability and fairness parameter in energy consumption compare to the similar method has improved, thus, prolonged network lifetime and reliability. Manuscript profile

Open Access Article

68 - Cache Point Selection and Transmissions Reduction using LSTM Neural Network
Malihe Bahekmat Mohammad Hossein Yaghmaee Moghaddam

10.52547/jist.27279.11.42.123

20.1001.1.23221437.2023.11.42.2.9

Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance More

Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput. Manuscript profile

Open Access Article

69 - Inferring Diffusion Network from Information Cascades using Transitive Influence
Mehdi Emadi Maseud Rahgozar Farhad Oroumchian

10.61186/jist.33656.11.44.307

Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a so More

Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n3). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too. Manuscript profile

Open Access Article

70 - Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks
maryam Najimi

10.61186/jist.33968.11.44.320

Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor networ More

Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor network, in which the cognitive sensor transmitter has the capability of the energy harvesting. In the first slot, the cognitive sensor transmitter participates in spectrum sensing and in the existence of the primary user, it harvests the energy from the primary signal, otherwise the sensor transmitter sends its signal to the corresponding receiver while in the second slot, using the decode-and-forward (DF) protocol, a part of the bandwidth is used to forward the signal of the primary user and the remained bandwidth is used for transmission of the cognitive sensor. Therefore, our purposed algorithm is to maximize the cognitive network transmission rate by selection of the suitable cognitive sensor transmitters subject to the rate of the primary transmission and energy consumption of the cognitive sensors according to the mobility model of the cognitive sensors in the dynamic network. Simulation results illustrate the effectiveness of the proposed algorithm in performance improvement of the network as well as reducing the energy consumption. Manuscript profile

Open Access Article

71 - Convolutional Neural Networks for Medical Image Segmentation and Classification: A Review
Jenifer S Carmel Mary Belinda M J

10.61186/jist.37936.11.44.347

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep le More

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep Convolutional Neural Networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the works exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pre-trained models and General Adversarial Networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on covid-19 detection and child bone age prediction. Manuscript profile

Open Access Article

72 - A Recommender System for Scientific Resources Based on Recurrent Neural Networks
Hadis Ahmadian Seyed Javad Mahdavi Chabok Maryam Kheirabadi

10.61186/jist.39439.11.44.282

Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses direct More

Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses directly affect the number of active participants; on the other hand, it becomes difficult for teachers to guide students in choosing the appropriate learning resource due to the abundance of online learning resources. Students also find it challenging to decide which educational resources to choose according to their condition. The resource recommender system can be used as a Guide tool for educational resource recommendations to students so that these suggestions are tailored to the preferences and needs of each student. In this paper, it was presented a resource recommender system with the help of Bi-LSTM networks. Utilizing this type of structure involves both long-term and short-term interests of the user and, due to the gradual learning property of the system, supports the learners' behavioral changes. It has more appropriate recommendations with a mean accuracy of 0.95 and a loss of 0.19 compared to a similar article. Manuscript profile

Open Access Article

73 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem

10.61186/jist.39680.11.44.359

Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More

Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile

Open Access Article

74 - Software-Defined Networking Adoption Model: Dimensions and Determinants
Elham Ziaeipour Ali Rajabzadeh Ghotri Alireza Taghizadeh

10.61186/jist.40088.11.44.368

The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Know More

The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Knowledge-based network. In this regard, telecom operators are interested in deploying SDN to migrate their infrastructure from a static architecture to a dynamic and programmable platform. However, it seems that they do not consider SDN as one of their priorities and still depend on traditional methods to manage their network (especially in some developing countries such as Iran). Since the first step in applying new technologies is to accept them, we have proposed a comprehensive SDN adoption model with the mixed-method research methodology. At first, the theoretical foundations related to the research problem were examined. Then, based on Grounded theory, in-depth interviews were conducted with 12 experts (including university professors and managers of the major telecom operators). In result, more than a thousand initial codes were determined, which in the review stages and based on semantic commonalities, a total of 112 final codes, 14 categories and 6 themes have been extracted using open, axial and selective coding. Next, in order to confirm the indicators extracted from the qualitative part, the fuzzy Delphi method has been used. In the end, SPSS and SmartPLS 3 software were used to analyze the data collected from the questionnaire and to evaluate the fit of the model as well as confirm and reject the hypotheses. Manuscript profile

Open Access Article

75 - Proposing an FCM-MCOA Clustering Approach Stacked with Convolutional Neural Networks for Analysis of Customers in Insurance Company
Motahareh Ghavidel meisam Yadollahzadeh tabari Mehdi Golsorkhtabaramiri

10.61186/jist.41465.12.45.62

To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimension More

To create a customer-based marketing strategy, it is necessary to perform a proper analysis of customer data so that customers can be separated from each other or predict their future behavior. The datasets related to customers in any business usually are high-dimensional with too many instances and include both supervised and unsupervised ones. For this reason, companies today are trying to satisfy their customers as much as possible. This issue requires careful consideration of customers from several aspects. Data mining algorithms are one of the practical methods in businesses to find the required knowledge from customer’s both demographic and behavioral. This paper presents a hybrid clustering algorithm using the Fuzzy C-Means (FCM) method and the Modified Cuckoo Optimization Algorithm (MCOA). Since customer data analysis has a key role in ensuring a company's profitability, The Insurance Company (TIC) dataset is utilized for the experiments and performance evaluation. We compare the convergence of the proposed FCM-MCOA approach with some conventional optimization methods, such as Genetic Algorithm (GA) and Invasive Weed Optimization (IWO). Moreover, we suggest a customer classifier using the Convolutional Neural Networks (CNNs). Simulation results reveal that the FCM-MCOA converges faster than conventional clustering methods. In addition, the results indicate that the accuracy of the CNN-based classifier is more than 98%. CNN-based classifier converges after some couples of iterations, which shows a fast convergence in comparison with the conventional classifiers, such as Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Naive Bayes (NB) classifiers. Manuscript profile

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