• List of Articles


      • Open Access Article

        1 - Assessing E-Government Readiness in Iranian Universities, Case Study: Tarbiat Modares University
        Saeed Haydari gholamali montazer
        Due to the recent advances in electronic government (e-government) and given the fact that universities are recognized as the Leading of innovation and progress in society, the expectation of electronic-government maturity levels in universities is higher than other gov Full Text
        Due to the recent advances in electronic government (e-government) and given the fact that universities are recognized as the Leading of innovation and progress in society, the expectation of electronic-government maturity levels in universities is higher than other governmental agencies. Given the high human capital capacity, universities are able to adapt themselves to e-government faster than other governmental organizations. In order to meet these expectations, e-government in universities and higher education institutions must be continuously evaluated. In this paper, we developed a model for assessing e-government readiness at university level, which includes "university-affiliated" indicators and "university-independent" indicators; university affiliates indicators are: " Trust "," Executive Policy and Laws "," Human Resources "," Integrity "," Budget "," Technological Infrastructure "," Digital Culture" and "Supervision, Support and Coordination ", as well as the "National Infrastructure" indicator is an independent indicator of the university. In the field study, the model was used to evaluate e-government readiness at Tarbiat Modares University Manuscript Document
      • Open Access Article

        2 - An Improved Method for Detecting Phishing Websites Using Data Mining on Web Pages
        mahdiye baharloo Alireza Yari
        Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is Full Text
        Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. Then, a two-stage feature reduction method based on feature selection and extraction were proposed to enhance the efficiency of phishing detection systems, which was able to reduce the number of features significantly. Finally, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated{cke_protected_1}{cke_protected_2}{cke_protected_3}{cke_protected_4} on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods. Manuscript Document
      • Open Access Article

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

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

        5 - Using a multi-objective optimization algorithm for tasks allocate in the cloud-based systems to reduce energy consumption
        sara tabaghchimilan nima jafari novimipour
        Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amount Full Text
        Nowadays, new technologies have increased the demand for business in the web environment.Increasing demand will increase the variety and number of services. As a result, the creation of large-scale computing data centers has high operating costs and consumes huge amounts of electrical power. On the other hand, inadequate and inadequate cooling systems not only cause excessive heating of resources and shorten the life of the machines. It also produces carbon that plays an important role in the weather. Therefore, they should reduce the total energy consumption of these systems with proper methods. In this research, an efficient energy management approach is provided in virtual cloud data centers, which reduces energy consumption and operational costs, and brings about an increase in the quality of services. It aims to provide a resource allocation strategy for cloud systems with the goal of reducing energy, cost of implementation and examining its use in cloud computing. The results of the simulation show that the proposed method in comaprision to NPA, DVFS, ST and MM methods can reduce the average energy consumption up to 0.626 kWh, also the need to immigration and SLA violation declined up to 186 and 30.91% respectively. Manuscript Document
      • Open Access Article

        6 - Incentive reward mechanism for Participants to the human computing system of Intrusion Detection Based on Game Theory
        yahya lormohammad hasani esfandghe majid ghayori
        Despite the tremendous advances in the design of human computing systems, most of them suffer from low participation or poor quality participation and a high percentage of them fail. To a large extent, the success of these systems depends on people who really behave in Full Text
        Despite the tremendous advances in the design of human computing systems, most of them suffer from low participation or poor quality participation and a high percentage of them fail. To a large extent, the success of these systems depends on people who really behave in the system. Because human computing systems include small units of work, and each job yields little benefit to the participants, humans display a good behavior in the system if they are well-stimulated for doing so. In this paper, this issue investigated in the Intrusion Detection Human Computation system. Our purpose of creating the stimulus for increasing of employee participation is to do their jobs carefully and effortlessly with the lowest possible cost. After selecting the appropriate stimuli for this system, we designed the mechanism of rewarding incentives. The idea behind this mechanism is to use the skill of the staff in determining their rewards. After designing this mechanism, we used the theory of games to analyze and determine the game's balance. Then, we determine the minimum possible reward for each category of work using the results obtained from the mechanism analysis based on game theory. We validate of this mechanism using game theory and the results of implementation. Designing this mechanism will increase the accuracy of respondents in answering and as a result, increase the accuracy of the human intelligence detection system in identifying new attacks and reducing their erroneous alert rates. Also, by allocating the lowest financial resources required to employees based on the analysis obtained from the game theory and managing human computing system of Intrusion Detection, encourages participants to participate in the system and, as a result, prevent the failure of the human computing system of intrusion detection. Manuscript Document
      • Open Access Article

        7 - Fast and accurate concept drift detection from event logs
        mahdi yaghoobi ali sebti Soheila Karbasi
        In organizations and large companies that are using business process management systems (BPMSs), process model can change due to upstream laws, market conditions. BPMSs have flexible to these changes. Effect of these change are saved in storage devises and event logs; t Full Text
        In organizations and large companies that are using business process management systems (BPMSs), process model can change due to upstream laws, market conditions. BPMSs have flexible to these changes. Effect of these change are saved in storage devises and event logs; these changes are sometimes applied suddenly or gradually on the event logs. Changing the season or starting a new financial term can be a factor to make these changes. This change is called concept drift in business process model. On time detection and recognition of process concept drift can affect the decision making of managers and administrations of systems. An analysis of the event logs in BPMS allows the automatic detection of the concept drift. This paper presents an innovative method by introducing a modified distance function to identify the concept drift. Experimental results were performed on 72 datasets in the research history, which included 648 concept drifts in 12 different types. It shows that the proposed method detects 98.18% of the drifts, while the proposed method is much faster than other state of the art methods. Manuscript Document
      • Open Access Article

        8 - Textual analysis of central bank news in forecasting long-term trend of Tehran stock exchange index
        meisam hashemi Mehran Rezaei marjan kaedi
        Financial markets have always been under influence of media news; therefore, text analysis of news is considered as an effective method of stock exchange forecasting. Research in this context has been conducted with the help of information retrieval techniques, in which Full Text
        Financial markets have always been under influence of media news; therefore, text analysis of news is considered as an effective method of stock exchange forecasting. Research in this context has been conducted with the help of information retrieval techniques, in which high frequency words in a document that appeared sporadically in the whole corpus received higher weight than others. In contrast, the words which appeared in many news of a corpus, during a certain time, indicate the importance of an event. In our research, to address this contradiction, a new technique of assigning weight to influential words of news is presented. Financial news of Iran Central Bank (CBI) and actual data of Tehran Stock Exchange Index (TSEI) in the duration of 2005 to 2020 AD were utilized to evaluate the proposed method. The empirical results show 64% and 41% accuracy of trend prediction when TSEI moves upward and downward respectively and about 10% decreasing in Mean Absolute Error (MAE) to compare with prevalent techniques. While, the changes of the ratio between the number of positive and negative words in news does not offer predictive or analytical evidences, our results show that, there still exists a meaningful relationship between CBI news and TSEI fluctuations. Manuscript Document
      • Open Access Article

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

        10 - The aware genetic algorithm of the best member, applied to graph coloring and metric-dimension of the graph problems
        mahmood amintoosi Hashem Ezzati
        Genetic algorithm is one of the most famous methods for solving Combinatorial Optimization Problems. It had various applications in different field of studies such as Electronics, Computer Science and Mathematics and still has. In this algorithm, the population members Full Text
        Genetic algorithm is one of the most famous methods for solving Combinatorial Optimization Problems. It had various applications in different field of studies such as Electronics, Computer Science and Mathematics and still has. In this algorithm, the population members which contribute for producing the next generation are selected according to their fitness values. The combination of the members is through Crossover Operator; And in some versions a few of the best members migrate to the next generation directly. Normally, the weak members of population may participate to the next generation. In this study, the combination operators are aware of the best member of generation; Only those child which are as good as the best member, are allowed to form the next generation. The proposed method is applied on graph coloring and finding metric-dimension of graph problems. The results are compared with the common genetic algorithm. Experimental results shows the superior performance of the proposed method in comparison to common genetic algorithm. Manuscript Document
      • Open Access Article

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

        12 - Modeling and simulation of the central generator of the pattern to produce curved-linear motions in the snake robot
        morteza vasegh yaghoub pourasad
        With the advancement of science and technology and the importance of using robots, the need to use automated systems seems essential. Since most applications of snake robots move in unfamiliar and sometimes complex environments, there is a need to develop different cont Full Text
        With the advancement of science and technology and the importance of using robots, the need to use automated systems seems essential. Since most applications of snake robots move in unfamiliar and sometimes complex environments, there is a need to develop different control methods for them. The product of the integration of the two sciences of neuroscience and robotics, are motor neuron producers known as model central generators, which is the problem of producing motion in the robot. In this paper, we control the movement of a snake-like robot with a central pattern generator (CPG) that is able to produce coordinated patterns of output signals with different frequencies. For this purpose, it is necessary to model the snake robot first and then apply control Be. In this paper, the control of robot motion control in two modes of open loop and closed loop for CPG network is presented. At the same time, this study with simulations shows that the lower the level of stimulation and the lower its level, the lower the frequency of motion production and vice versa. Then, the effect of CPG models, which are used as neural networks, is simulated in motion control. In this paper, the remarkable point in comparison with other controllers is that in the central generating neural networks, the pattern of simple signals is sufficient to stimulate and induce the movement of robots, which is shown in the simulation Manuscript Document