• List of Articles


      • Open Access Article

        1 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
        Alireza Mansouri Fattaneh Taghiyareh
        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 Full Text
        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 Document
      • Open Access Article

        2 - Drone Detection by Neural Network Using GLCM and SURF Features
        Tanzia  Ahmed Tanvir  Rahman Bir  Ballav Roy Jia Uddin
        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 Full Text
        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 Document
      • Open Access Article

        3 - Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory
        Nazbanoo Farzaneh Reyhaneh Hoseini
        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 Full Text
        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 Document
      • Open Access Article

        4 - Denoising and Enhancement Speech Signal Using Wavelet
        Meriane Brahim
        Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techni Full Text
        Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems. Manuscript Document
      • Open Access Article

        5 - Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features
        Azar Mahmoodzadeh
        During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper pro Full Text
        During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘scale-invariant features transform’ (SIFT), ‘histogram of oriented gradient’ (HOG) and ‘global invariant features transform’ (GIST) descriptors and classifies the activities by means of the deep belief network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a deep belief network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96.39%, 85.77% and 86.72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition. Manuscript Document
      • Open Access Article

        6 - Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT
        Mohammad sedighimanesh Hessam  Zandhessami Mahmood  Alborzi Mohammadsadegh  Khayyatian
        Background: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The p Full Text
        Background: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The present article's main objective is to assist in improving energy consumption in the sensor-based IoT network and thus increase the network’s lifetime. Cluster heads are used to send data to the base station. Methods: In the present paper, the type-1 fuzzy algorithm is employed to select cluster heads, and the type-2 fuzzy algorithm is used for routing between cluster heads to the base station. After selecting the cluster head using the type-1 fuzzy algorithm, the normal nodes become the members of the cluster heads and send their data to the cluster head, and then the cluster heads transfer the collected data to the main station through the path which has been determined by the type-2 fuzzy algorithm. Results: The proposed algorithm was implemented using MATLAB simulator and compared with LEACH, DEC, and DEEC protocols. The simulation results suggest that the proposed protocol among the mentioned algorithms increases the network’s lifetime in homogeneous and heterogeneous environments. Conclusion: Due to the energy limitation in sensor-based IoT networks and the impossibility of recharging the sensors in most applications, the use of computational intelligence techniques in the design and implementation of these algorithms considerably contributes to the reduction of energy consumption and ultimately the increase in network’s lifetime. Manuscript Document
      • Open Access Article

        7 - Secured Access Control in Security Information and Event Management Systems
        Leila Rikhtechi Vahid Rafeh Afshin Rezakhani
        Nowadays, Security Information and Event Management (SIEM) is very important in software. SIEM stores and monitors events in software and unauthorized access to logs can prompt different security threats such as information leakage and violation of confidentiality. In t Full Text
        Nowadays, Security Information and Event Management (SIEM) is very important in software. SIEM stores and monitors events in software and unauthorized access to logs can prompt different security threats such as information leakage and violation of confidentiality. In this paper, a novel method is suggested for secured and integrated access control in the SIEM. First, the key points where the SIEM accesses the information within the software is specified and integrated policies for access control are developed in them. Accordingly, the threats entered into the access control module embedded in this system are carefully detected. By applying the proposed method, it is possible to provide the secured and integrated access control module for SIEM as well as the security of the access control module significantly increases in these systems. The method is implemented in the three stages of the requirements analysis for the establishment of a secure SIEM system, secure architectural design, and secure coding. The access control module is designed to create a secured SIEM and the test tool module is designed for evaluating the access control module vulnerabilities. Also, to evaluate the proposed method, the dataset is considered with ten thousand records, and the accuracy is calculated. The outcomes show the accuracy of the proposed method is significantly improved. The results of this paper can be used for designing an integrated and secured access control system in SIEM systems. Manuscript Document