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    Journal of Information and Communication Technology ( Scientific )
  • OpenAccess
  • About Journal

    According to the letter No. 3/4817 dated 2007/9/02 of the Research Affairs Office of the Iranian Ministry of Science, Research and Technology and the statement of the vote of the Commission for the Review of Scientific Journals of the country on 2007/7/14, this journal has been awarded a scientific-research degree.

    Journal of Information and Communication Technology (JICT) belongs to the "Iranian Information and Communication Technology Association" and is a peer-reviewed open-access bi-quarterly journal publishing high-quality scientific papers covering all aspects of the information and communication technologies including computer science and engineering, information technology, communication technology, information technology management. It is an interdisciplinary journal devoted to the publication of original articles, review articles, etc., considering the research ethics and academic rules and regulations.
    All papers are subject to a blind reviewing process. The submitted manuscripts will be published after a thorough review and the editorial board's approval. 


    Recent Articles

    • Open Access Article

      1 - A Novel Multi-Step Ahead Demand Forecasting Model Based on Deep Learning Techniques and Time Series Augmentation
      Hossein Abbasimehr Reza Paki
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formula More
      In a business environment where there is fierce competition between companies, accurate demand forecasting is vital. If we collect customer demand data at discrete points in time, we obtain a demand time series. As a result, the demand forecasting problem can be formulated as a time series forecasting task. In the context of time series forecasting, deep learning methods have demonstrated good accuracy in predicting complex time series. However, the excellent performance of these methods is dependent on the amount of data available. For this purpose, in this study, we propose to use time series augmentation techniques to improve the performance of deep learning methods. In this study, three new methods have been used to test the effectiveness of the proposed approach, which are: 1) Long short-term memory, 2) Convolutional network 3) Multihead self-attention mechanism. This study also uses a multi-step forecasting approach that makes it possible to predict several future points in a forecasting operation. The proposed method is applied to the actual demand data of a furniture company. The experimental results show that the proposed approach improves the forecasting accuracy of the methods used in most different prediction scenarios Manuscript profile

    • Open Access Article

      2 - Improving polarity identification in sentiment analysis using sarcasm detection and machine learning algorithms in Persian tweets
      Shaghayegh hajiabdollah Mitra Mirzarezaee Mir Mohsen Pedram
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Sentiment analysis is a branch of computer science and natural language processing that seeks to familiarize machines with human emotions and make them recognizable. Both sentiment analysis and sarcasm which is a sub-field of the former, seek to correctly identify the h More
      Sentiment analysis is a branch of computer science and natural language processing that seeks to familiarize machines with human emotions and make them recognizable. Both sentiment analysis and sarcasm which is a sub-field of the former, seek to correctly identify the hidden positive and negative emotions of the text. The use of sarcasm on social media, where criticism can be exercised within the context of humor, is quite common. Detection of sarcasm has a special effect on correctly recognizing the polarization of an opinion, and thus not only it can help the machine to understand the text better, but also makes it possible for the respective author to get his message across more clearly. For this purpose, 8000 Persian tweets that have emotional labels and examined for the presence or absence of sarcasm have been used. The innovation of this research is in extracting keywords from sarcastic sentences. In this research, a separate classifier has been trained to identify irony of the text. The output of this classifier is provided as an added feature to the text recognition classifier. In addition to other keywords extracted from the text, emoticons and hashtags have also been used as features. Naive Bayes, support vector machines, and neural networks were used as baseline classifiers, and finally the combination of classifiers was used to identify the feeling of the text. The results of this study show that identifying the irony in the text and using it to identify emotions increases the accuracy of the results. Manuscript profile

    • Open Access Article

      3 - Modeling and evaluation of RPL routing protocol by colored Petri nets
      Mohamad Pishdar Younes Seifi
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      The Internet of Things (IoT) is a novel and widely used idea aimed at connecting objects through communication technologies. The problem of the prior technology adaptation has always been one of the most challenging issues in this area over the years. The Recognition of More
      The Internet of Things (IoT) is a novel and widely used idea aimed at connecting objects through communication technologies. The problem of the prior technology adaptation has always been one of the most challenging issues in this area over the years. The Recognition of Prior Learning (RPL) protocol has been proposed by scientists since 2012 as a solution for IoT routing. This protocol has been utilized by many researchers and hardware companies in the field of the mentioned technology. The present study evaluates RPL behavior from the perspective of the existence of stopping conditions, crossing multiple routes from a special route (loop conditions), and how it reacts to different inputs, while presenting a modular and readable model of this protocol. Manuscript profile

    • Open Access Article

      4 - The Effect of ICT Development on Economic and Political Risk: A Cross Country Study
      َAmir Hossein Mozayani Sajjad  Faraji Dizaji Hossein Karimi
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Today, information and communication technology has influenced all areas of human life, including economics and politics. In research on the effects of information and communication technology, its effects on economic risk and political risk have not been considered so More
      Today, information and communication technology has influenced all areas of human life, including economics and politics. In research on the effects of information and communication technology, its effects on economic risk and political risk have not been considered so far. Therefore, this study has examined the effect of ICT development on economic and political risk for three selected groups of developed, developing, and OPEC countries in the period 2007- 2019. The panel data method was used to estimate the model. Based on the model estimation results for all sample countries, the deployment of information and communication technology reduces economic and political risk; But the results are different for each group. As the development of information and communication technology in selected OPEC members and developing countries increases economic risk; But in developed countries, it reduces economic risk. Also, in OPEC member countries, no significant relationship was found between ICT and political risk, and in selected developed and developing countries, ICT increases political risk. Manuscript profile

    • Open Access Article

      5 - Increasing Total Throughput, Reducing Outage to Zero, and Reducing Power Consumption in a Cellular Network
      Mohsen Seyyedi Saravi Mohammadreza Binesh Marvasti Seyedeh Leili Mirtaheri Seyyed Amir Asghari
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by as More
      Quality assurance of providing remote services in cellular networks necessitates attention to significant criteria such as throughput, power consumption, and interference in these networks. Accordingly, this paper presents a framework for optimizing these criteria by assuming a limited transmission capacity for mobile nodes in a wireless cellular network as limitations in the transmission capacity often exist both in terms of hardware, battery limitations, and regulatory rules in the real world. In presenting this framework, a new idea was proposed once the existing methods were examined and their advantages and disadvantages were compared, respectively. After the formula was proved, the idea's simulation steps were performed via MATLAB. Present methods either increased the throughput by assuming unlimited transmission power or prevented some nodes from accessing the communication service. The simulation results showed that the proposed algorithm reduced the power consumption of mobile nodes in the network by a quarter in addition to increasing the throughput by 27%, and further operated in a way that no node would lose communication service Manuscript profile

    • Open Access Article

      6 - Synthesizing an image dataset for text detection and recognition in images
      Fatemeh Alimoradi Farzaneh Rahmani Leila Rabiei Mohammad Khansari Mojtaba Mazoochi
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Text detection in images is one of the most important sources for image recognition. Although many researches have been conducted on text detection and recognition and end-to-end models (models that provide detection and recognition in a single model) based on deep lear More
      Text detection in images is one of the most important sources for image recognition. Although many researches have been conducted on text detection and recognition and end-to-end models (models that provide detection and recognition in a single model) based on deep learning for languages such as English and Chinese, the main obstacle for developing such models for Persian language is the lack of a large training data set. In this paper, we design and build required tools for synthesizing a data set of scene text images with parameters such as color, size, font, and text rotation for Persian. These tools are used to generate a large still varied data set for training deep learning models. Due to considerations in synthesizing tools and resulted variety of texts, models do not depend on synthesis parameters and can be generalized. 7603 scene text images and 39660 cropped word images are synthesized as sample data set. The advantage of our method over real images is to synthesize any arbitrary number of images, without the need for manual annotations. As far as we know, this is the first open-source and large data set of scene text images for Persian language. Manuscript profile

    • Open Access Article

      7 - An Intelligent Pricing System for Cloud Services aims at Increasing Implementation Simplicity and Flexibility
      Mahboubeh Zandieh Sepideh Adabi Samaneh Yazdani
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goa More
      Most of the previous pricing models for cloud resources which are defined based on auction suffer from high implementation complexity in real cloud environments. Therefore, the main challenge for researchers is to design dynamic pricing models that can achieve three goals: 1) low computation complexity, 2) high accuracy, and 3) high implementation simplicity in real cloud environments. CMM (Cloud Market Maker) is one of the most popular dynamic pricing models that has two advantages of computation accuracy and the possibility to implement in the real cloud environments. This model calculates the bid price based on a linear function. In designing this linear function, the parameters: buyer’s urgency, number of competitors and number of opponents are considered. Despite the advantages of this pricing function, the importance ratio of the constructor parameters of it is considered the same in various market conditions. Ignoring this issue reduces both system flexibility and computation accuracy in tangible changes in the cloud market. Therefore, the authors of this paper focus on designing a new cloud market-aware intelligent pricing system (which developed in customer side of the market) to tackle the mentioned problem. At the same time, high implementation simplicity of the proposed system should be guaranteed. For this purpose, an agent-based intelligent pricing system by combining support vector machine (SVM) and hierarchical analysis process (AHP) techniques is proposed. Simulation results show the better performance of the proposed solution which is named as DPMA in comparison to CMM. Manuscript profile

    • Open Access Article

      8 - E-Government Service Supply Chain: Identifying Performance Evaluation Indicators (Case Study of e-Customs System in Iran)
      jalal zare Rosa Hendijani
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Today, many governments around the world are increasingly leveraging advances in information and communication technologies to provide electronic services to their citizens. But estimates indicate that e-government projects will fail miserably, both in part and in whole More
      Today, many governments around the world are increasingly leveraging advances in information and communication technologies to provide electronic services to their citizens. But estimates indicate that e-government projects will fail miserably, both in part and in whole. Incomplete formation and poor supply chain performance are the most important reasons for the failure of these projects by researchers. Due to the fact that few studies have been conducted in the field of e-government service supply chain and its performance evaluation indicators, this study has studied the e-government service supply chain in Iran by studying the e-customs system. Also, using SMART criteria and ELECTRE I technique, it has identified the performance evaluation indicators of this chain. This study shows that just as the principles of e-supply chain in the manufacturing sector have been proposed by researchers, this concept can be generalized to the public service sector. The results also show that unlike previous studies on performance appraisal indicators, e-government service supply chain indicators have significant differences with traditional service supply chains. Manuscript profile

    • Open Access Article

      9 - Three Dimensional Beamforming in Multi User Multi Antenna Cellular Networks with Randomly Distributed Users
      S. Mohammad Razavizadeh Nasim Mohammadi
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      In this paper, problem of using the 3D beamforming method (3DBF) in a multi-input-multi-output cellular communication network (MIMO) is discussed. The network consists of a cell with multiple users, in which users are distributed based on the Poisson point (PPP) process More
      In this paper, problem of using the 3D beamforming method (3DBF) in a multi-input-multi-output cellular communication network (MIMO) is discussed. The network consists of a cell with multiple users, in which users are distributed based on the Poisson point (PPP) process at the cell area, which is closer to the conditions in a real mobile network. In this case, the number of users inside the cell and their location will be random. Depending on the distribution of users in the space and the difference in their distance from the base station, their elevation angles will also be different. Considering the downlink transmission and the zero-forcing (ZF) precoder in the base station, with the aim of eliminating intra cell interference, we evaluate and analyze the probability of coverage in the cell and then we obtain the best antenna tilt angle to achieve maximum probability of coverage. Using the analysis of numerical results, the accuracy of the calculations and the value of the optimal tilt angle of the antenna array are confirmed. Manuscript profile

    • Open Access Article

      10 - Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
      Vahid Safari dehnavi masoud shafiee
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the vo More
      Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model. Manuscript profile

    • Open Access Article

      11 - Power Efficient allocation in C-RAN with Multi access technology selection approach
      ALI ASGHAR ANSARI Mohsen Eslami Mohammad Javad Dehghani Saeideh Parsaei Fard
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      : In this paper, we consider an uplink economy-efficient resource allocation in a multicellular virtual wireless network with a C-RAN architecture where a MNO interacts with a number of MVNOs with a predetermined business model. In each cell of this system, two types of More
      : In this paper, we consider an uplink economy-efficient resource allocation in a multicellular virtual wireless network with a C-RAN architecture where a MNO interacts with a number of MVNOs with a predetermined business model. In each cell of this system, two types of multiple access technologies, namely OFDMA and Massive MIMO, are available for MVNO at two different prices. In this setup, we propose a multi access technology selection approach (MATSA) with the objective to reduce operating costs and maximize the profit of the MVNOs subject to a set of constraints, and formulate this resource allocation problem with the new utility function. Due to the existence of continuous and binary variables in the formulated optimization problem and also the interference between cells in data rate functions, this optimization problem will be non-convex with very high computational complexity. To tackle this problem, by applying the complementary geometric programming (CGP) and the successive convex approximation (SCA), an effective two-step iterative algorithm is developed to convert the optimization problem into two sub problems with the aim to find optimum technology selection and power consumption parameters for each user in two steps, respectively. The simulation results demonstrate that our proposed approach (MATSA) with novel utility function is more efficient than the traditional approach, in terms of increasing total EE and reducing total power consumption. The simulation results illustrate that the profit of the MVNOs is enhanced more than 13% compared to that of the traditional approach. Manuscript profile

    • Open Access Article

      12 - Design and fabrication of the E-field probe for the measurement of the electromagnetic fields in 5G frequency band
      Reza Bahri Mahdi Fasanghari Ahmadreza Eskandari Vahid Yazdanian
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      In this paper, a device for measuring the electric fields intensity in the environment is designed and presented in the 5G frequency band, including the frequency range of 3400 ~ 3600 MHz. This device, called the 5G electric probe, is realized by three orthogonal antenn More
      In this paper, a device for measuring the electric fields intensity in the environment is designed and presented in the 5G frequency band, including the frequency range of 3400 ~ 3600 MHz. This device, called the 5G electric probe, is realized by three orthogonal antennas, in connection to filter circuits and power detectors. The proposed antenna is a strip monopole antenna, and these orthogonal antennas can receive the electric fields in all directions uniformly and isotropically. The proposed filter is a coupled-line microstrip filter that has the ability to remove out-of-band signals. The proposed power detector is able to operate linearly over a wide dynamic range and convert the fields received from the antenna and filter sections to suitable DC voltages for digital processing. Finally, the designed 5G electric probe is fabricated and tested. The measurements confirm the proper operation of the probe in terms of dynamic range, accuracy, sensitivity, and the linearity and isotropicity of the received electric fields. Manuscript profile

    • Open Access Article

      13 - A review of the application of meta-heuristic algorithms in load balancing in cloud computing
      Mehdi Morsali Abolfazl Toroghi Haghighat Sasan Hosseinali-Zade
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      By widespread use of cloud computing, the need to improve performance and reduce latency in the cloud increases. One of the problems of distributed environments, especially clouds, is unbalanced load which results in reducing speed and efficiency and increasing delay in More
      By widespread use of cloud computing, the need to improve performance and reduce latency in the cloud increases. One of the problems of distributed environments, especially clouds, is unbalanced load which results in reducing speed and efficiency and increasing delay in data storage and retrieval time. Various methods for load balancing in the cloud environment have been proposed, each of which has addressed the issue from its own perspective and has its advantages and disadvantages. In this research, we first provide some criteria for measuring load balance in the cloud and then examine the use of Metaheuristic methods in load balancing in the cloud environment. After introducing Metaheuristic load balancing methods, we have compared them based on the aforementioned criteria and discussed the advantages and disadvantages of each. Ant Colony Algorithms, Artificial Ant Colony, Bee Colony, Artificial Bee Colony, Bee Foraging Algorithm, Particle Swarm, Cat Swarm, Simulated Annealing, Genetic Algorithm, Tabu Search, Fish Swarm and Hybrid Algorithms and etc. examined in this research. Manuscript profile

    • Open Access Article

      14 - An approach to prioritize quality dimensions of based on cloud computing using Multiple Criteria Decision Making method
      Zahra Abbasi Somayeh Fatahi Mohammad Javad  Ershadi
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of com More
      Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of communications, a concept called cloud computing has expanded in the world of communications, which provides a new model for the supply, consumption and delivery of computing services. The purpose of this study is to make the optimal decision in choosing the appropriate cloud service according to the conditions of users so that they achieve the highest satisfaction. Fuzzy Delphi method, fuzzy hierarchical analysis method, fuzzy TOPSIS method and finally multi-criteria decision making method are the methods used in this research. The results of the fuzzy Delphi method show that the indicators of transparency, accessibility and reliability should be eliminated. The results of fuzzy hierarchical analysis identified the cost index as the most important index and the support index during demand as the least important index. According to the results of fuzzy TOPSIS based on the weights obtained from fuzzy hierarchical analysis, SAAS, IAAS and PAAS cloud services were ranked first to third, respectively. Using the SAAS service provides numerous benefits to employees and companies, such as reducing time and money spent on time-consuming tasks such as installing, managing, and upgrading software. Manuscript profile

    • Open Access Article

      15 - Data-driven Marketing in Digital Businesses from Dynamic Capabilities View
      Maede  Amini vlashani ayoub mohamadian Seyed Mohammadbagher Jafari
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dy More
      Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dynamic capabilities of data-driven marketing to focus on data in the development of marketing strategies, make effective decisions, and improve efficiency in marketing processes and operations. This research has been carried out in a qualitative method utilizing the content analysis strategy and interviews with specialists. The subjects were 18 professionals in the field of data analytics and marketing. They were selected by the purposeful sampling method. This study provides data-driven marketing dynamic capabilities, including; Ability to absorb marketing data, aggregate and analyze marketing data, the ability to data-driven decision-making, the ability to improve the data-driven experience with the customer, data-driven innovation, networking, agility, and data-driven transformation. The results of this study can be a step towards developing the theory of dynamic capabilities in the field of marketing with a data-driven approach. Therefore, it can be used in training and creating new organizational capabilities to use big data in the marketing activities of organizations, to develop and improve data-driven products and services, and improve the customer experience Manuscript profile

    • Open Access Article

      16 - A comprehensive survey on the influence maximization problem in social networks
      mohsen taherinia mahdi Esmaeili Behrooz Minaei
      Issue 53 , Vol. 14 , Autumn_Winter 2023
      With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency a More
      With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches Manuscript profile
    Most Viewed Articles

    • Open Access Article

      1 - Determining the factors affecting the collective financing of knowledge-based IT companies
      Ali Haji Gholam Saryazdi ali rajabzadeh ghotri alinaghi mashayekhi alireza hassanzade
      Issue 37 , Vol. 10 , Autumn_Winter 2019
      The method of crowdfunding in the world has expanded rapidly due to the need for financing in the early stages of start-up businesses as well as advances in information technology. In Iran, several financing platforms have been established so far, some of which have bee More
      The method of crowdfunding in the world has expanded rapidly due to the need for financing in the early stages of start-up businesses as well as advances in information technology. In Iran, several financing platforms have been established so far, some of which have been successful and some of which have been unsuccessful. Therefore, it is necessary to help the development of this method by examining the factors affecting it. Since mass financing is a new and new phenomenon, it is necessary to increase its awareness in the society in an appropriate way while determining the factors affecting this method. The collective modeling method is based on social networks and the Web 2 with the aim of recognizing new phenomena. Therefore, in this article, using collective modeling, the factors affecting crowdfunding in Iran in order to support start-up companies in the field of IT are discussed. Manuscript profile

    • Open Access Article

      2 - طراحی اولین پایگاه داده کلمات دستنویس کردی برای سیستم های تشخیص تصویری کلمات
      fatemeh daneshfar basir alagheband vahid sharafi
      Issue 17 , Vol. 5 , Spring_Summer 1393
      چکیده: یکی از اجزای زیربنایی سیستم های تشخیص تصویری کلمات پایگاه داده هاست. هر سیستمی که در این زمینه طراحی گردد لاجرم می بایست از یک نوع پایگاه داده ها استفاده کند. بدیهی است چون موضوع مورد مطالعه در این سیستم ها شکل نوشتاری زبان های مختلف میباشد پس برای هر زبان مشخص More
      چکیده: یکی از اجزای زیربنایی سیستم های تشخیص تصویری کلمات پایگاه داده هاست. هر سیستمی که در این زمینه طراحی گردد لاجرم می بایست از یک نوع پایگاه داده ها استفاده کند. بدیهی است چون موضوع مورد مطالعه در این سیستم ها شکل نوشتاری زبان های مختلف میباشد پس برای هر زبان مشخص پایگاه داده بخصوصی لازم است. زبانی که این مقاله بر آن متمرکز شده کردی است و در این مقاله مراحل مختلف چگونگی طراحی اولین پایگاه داده دستنویس برای زبان کردی شرح داده شده است. از آنجا که تاکنون هیچ پایگاه داده ای مخصوص تشخیص تصویری کلمات، مربوط به زبان کردی طراحی نشده است بنابراین زمینه ای بکر و مستعد برای انجام تحقیق محسوب می گردد. همچنین با توجه به اینکه زبان کردی دارای دو رسم الخط مختلف لاتین و آرامی می باشد در این مقاله منحصرا به رسم الخط آرامی البته از نوع دستنویس آن پرداخته شده است. Manuscript profile

    • Open Access Article

      3 - Image Processing of steel sheets for Defect Detection by using Gabor Wavelet
      masoud shafiee mostafa sadeghi
      Issue 13 , Vol. 4 , Spring 1391
      In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process More
      In different steps of steel production, various defects appear on the surface of the sheet. Putting aside the causes of defects, precise identification of their kinds helps classify steel sheet correctly, thereby it allocates a high percentage of quality control process. QC of steel sheet for promotion of product quality and maintaining the competitive market is of great importance. In this paper, in addition to quick review of image process techniques used, using image process by means of Gabor wavelet, a fast and precise solution for detection of texture defects in steel sheet is presented. In first step, the approach extracts considerable texture specification from image by using Gabor wavelet. The specification includes both different directions and different frequencies. Then using statistical methods, images are selected that have more obvious defects, and location of defects is determined. Offering the experimental samples, the accuracy and speed of the method is indicated. Manuscript profile

    • Open Access Article

      4 -
      mostafa sadeghi masoud shafiee
      Issue 14 , Vol. 4 , Autumn_Winter 2012

    • Open Access Article

      5 - A model information technology adoption in academic research projects in the filed ICT based on Information Technology Adoption Integrated Modeling (ITAIM)
      Shahram Aliyari masoud movahedi sirous kazemian
      Issue 41 , Vol. 11 , Autumn_Winter 2020
      Today, the emergence and expansion of technologies that provide the widest possible connection have brought about significant changes in the private life and professional life of individuals. Correct implementation of information technology is the source of economic and More
      Today, the emergence and expansion of technologies that provide the widest possible connection have brought about significant changes in the private life and professional life of individuals. Correct implementation of information technology is the source of economic and cultural development and the promotion of quality of life through the exchange of information and the provision of public and private services. The purpose of this research is to present the model of information technology acceptance in Iranian ICT research centers. Employed experts worked on ICT projects in this research Statistical population. It was provided in one of the university searching centers. And so a convenience and purposeful Nonprobability sampling does (30 person). This paper examines the factors and parameters affecting the acceptance of information technology in ICT projects of the university research centers in the field of information technology. To collect the required data and information, a questionnaire was used & to analyze the data and information obtained from the questionnaires using the Spss 22 and Smart pls3 software. According to the calculations, the factors affecting the acceptance of information technology in university research centers can be divided into four categories: IT related factors, organizational factors, factors related to executive director and individual factors that are related to management (0.497), IT (0.460) and Individual factors (0.457) have an impact on the individual acceptance of information technology respectively and organizational factors (0.469) on the adoption of an IT organization Manuscript profile

    • Open Access Article

      6 - An Investigation on the Effect of Multifactor Model of Improving Critical Thinking in E-learning Environments
      mohammadreza nili jamshid heydari hossein moradi
      Issue 21 , Vol. 6 , Spring 1394
      In the third millennium, people deal with multiple, diverse, and complicated problems as they cannot possess full control over the information, which is constantly produced and accumulated. Having a high skill of critical thinking for assessing the results of different More
      In the third millennium, people deal with multiple, diverse, and complicated problems as they cannot possess full control over the information, which is constantly produced and accumulated. Having a high skill of critical thinking for assessing the results of different issues and decision making about them based on evidences is an unavoidable necessity. The researchers of this work proposed a model with seven factors (components) for critical thinking in e-learning environments. The statistical group of this work is the M.Sc. medical education students of  AZAD university e-learning environments, and the students of the same field from Islamic Azad University traditional education system studying during 2011-2012. Among the research community, 47 members were selected based on a simple random method and divided into two trial (with 23 members) and reference (with 42 members) groups. To train the trial group, the seven-factor critical thinking training scale was utilized in e-learning environments in 15 sessions with empirical sciences course. In the reference group, the same seven-factor critical thinking training scale was used in the classroom environment in lecturing in 15 sessions with empirical sciences course. The model factors and components are challenge, representation, creation of opportunity, creation of motivation, logical analysis, encouragement, responsibility, and commitment. Both groups were subject to two pretest and posttest steps within two trial groups, which were considered as reference to each other. Both groups responded to the Watson- Glaser™ Critical Thinking Appraisal within two pretest and posttest steps, while the covariance analysis statistical test was used for analysis of the results. The results indicate significant difference between the scores between trial and reference groups in improving the critical thinking of the students in terms of inferential, assumption detection, deduction, interpretation, and logical reasoning evaluation components (p=0.001). According to the results, in terms of improving critical thinking, the trial group trained in the e-learning environment indicates higher scores as compared to the group trained in the traditional classroom environment. Manuscript profile

    • Open Access Article

      7 - A Satellite Control Method Using Laguerre Model Predictive Control Approach
      shekoofeh jafari fesharaki farzad tihidkhah heydarali talebi
      Issue 15 , Vol. 5 , Summer 1392
      In this paper a Model Predictive Method based controller is proposed to control a satellite. Model Predictive Control (MPC) has been well known as a practical control method for various systems in industry. A problem with this method is its computational effort and time More
      In this paper a Model Predictive Method based controller is proposed to control a satellite. Model Predictive Control (MPC) has been well known as a practical control method for various systems in industry. A problem with this method is its computational effort and time consuming. To reduce computational load Laguerre functions have been proposed in this literature. Simulation results are given to show feasibility and the validity of the design. A comparison between the time consumed in the presence and the absence of the Laguerre functions is done too. Manuscript profile

    • Open Access Article

      8 - Using web analytics in forecasting the stock price of chemical products group in the stock exchange
      amir daee Omid Mahdi Ebadati E. keyvan borna
      Issue 39 , Vol. 11 , Spring_Summer 2019
      Forecasting markets, including stocks, has been attractive to researchers and investors due to the high volume of transactions and liquidity. The ability to predict the price enables us to achieve higher returns by reducing risk and avoiding financial losses. News plays More
      Forecasting markets, including stocks, has been attractive to researchers and investors due to the high volume of transactions and liquidity. The ability to predict the price enables us to achieve higher returns by reducing risk and avoiding financial losses. News plays an important role in the process of assessing current stock prices. The development of data mining methods, computational intelligence and machine learning algorithms have led to the creation of new models in prediction. The purpose of this study is to store news agencies' news and use text mining methods and support vector machine algorithm to predict the next day's stock price. For this purpose, the news published in 17 news agencies has been stored and categorized using a thematic language in Phoenician. Then, using text mining methods, support vector machine algorithm and different kernels, the stock price forecast of the chemical products group in the stock exchange is predicted. In this study, 300,000 news items in political and economic categories and stock prices of 25 selected companies in the period from November to March 1997 in 122 trading days have been used. The results show that with the support vector machine model with linear kernel, prices can be predicted by an average of 83%. Using nonlinear kernels and the quadratic equation of the support vector machine, the prediction accuracy increases by an average of 85% and other kernels show poorer results. ارسال Manuscript profile

    • Open Access Article

      9 - Routing improvement to control congestion in software defined networks by using distributed controllers
      saied bakhtiyari Ardeshir Azarnejad
      Issue 39 , Vol. 11 , Spring_Summer 2019
      Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way th More
      Software defined networks (SDNs) are flexible for use in determining network traffic routing because they separate data plane and control plane. One of the major challenges facing SDNs is choosing the right locations to place and distribute controllers; in such a way that the delay between controllers and switches in wide area networks can be reduced. In this regard, most of the proposed methods have focused on reducing latency. But latency is just one factor in network efficiency and overall cost reduction between controllers and related switches. This article examines more factors to reduce the cost between controllers and switches, such as communication link traffic. In this regard, a cluster-based algorithm is provided for network segmentation. Using this algorithm, it can be ensured that each part of the network can reduce the maximum cost (including delays and traffic on links) between the controller and its related switches. In this paper, using Topology Zoo, extensive simulations have been performed under real network topologies. The results of the simulations show that when the probability of congestion in the network increases, the proposed algorithm has been able to control the congestion in the network by identifying the bottleneck links in the communication paths of each node with other nodes. Therefore, considering the two criteria of delay and the degree of busyness of the links, the process of placing and distributing the controllers in the clustering operation has been done with higher accuracy. By doing so, the maximum end-to-end cost between each controller and its related switches, in the topologies Chinanet of China, Uunet of the United States, DFN of Germany, and Rediris of Spain, is decreased 41.2694%, 29.2853%, 21.3805% and 46.2829% respectively. Manuscript profile

    • Open Access Article

      10 - Medial-axis Enhancement of Tubular Structures and its Application in the Extraction of Portal Veins
      amirhossein forouza reza aghaeizade youshi sato masa houri
      Issue 13 , Vol. 4 , Spring 1391
      I In this paper, a new filter is designed to enhance medial-axis of tubular structures. Based on a multi-scale method and using eigenvectors of Hessian matrix, the distance of a point to the edges of the tube is found. To do this, a hypothetical line with a deliberate d More
      I In this paper, a new filter is designed to enhance medial-axis of tubular structures. Based on a multi-scale method and using eigenvectors of Hessian matrix, the distance of a point to the edges of the tube is found. To do this, a hypothetical line with a deliberate direction is passed through the point which cuts the tube at its edges. For points which are located on the medial-axis, this distance is symmetric with respect to any deliberate direction. We find samples of the distances in different directions and assign a measure to the points based on this symmetry property. The output of this step is an enhanced image in which noise is removed and tubes can be seen more clearly. Then, we employ the filter developed by Pock et al. to enhance medial axis. Evaluation of the proposed method is performed using 2D/3D synthetic/clinical datasets both quantitatively and qualitatively. Manuscript profile
    Upcoming Articles

    • Open Access Article

      1 - The effect of emotional intelligence of project managers on the effectiveness of team communication in Iranian research institutes (Case study: Information and Communication Technology Research Institute)
      Mansoureh  Mohammadnezhad Fadard Ehram Safari
      Management is doing things with and through others. However, in managing a research projects, having technical capabilities is considered as the main criterion and less attention is paid to the behavioral aspects of project managers such as emotional intelligence as an More
      Management is doing things with and through others. However, in managing a research projects, having technical capabilities is considered as the main criterion and less attention is paid to the behavioral aspects of project managers such as emotional intelligence as an important component in communicating with people. The results of several studies have shown that not paying attention to this issue has reduced effective communication in project teams and ultimately the failure of projects. The present study was conducted to assess the effect of different dimensions of project manager's emotional intelligence on the effectiveness of team communication. The method of this research is descriptive-analytical correlation that the statistical population consists of managers and members of project teams in the Information and Communication Technology Research Institute. The statistical sample includes 19 project teams that have been selected by census method. In order to collect data, the Bar-On emotional intelligence questionnaire and the senior interpersonal relationships questionnaire were used. Pearson correlation coefficient, multivariate regression and imaginary variable regression as well as dependent t-test were used to analyze the data. The results show that project manager's emotional intelligence affects effective interpersonal relationships in the project team. However, only interpersonal skills, interpersonal skills, and adjustment can predict effective interpersonal relationships in the team, and the dimensions of general mood and stress management do not affect these relationships. Manuscript profile

    • Open Access Article

      2 - Improving Opinion Aspect Extraction Using Domain Knowledge and Term Graph
      Mohammadreza Shams Ahmad  Baraani Mahdi Hashemi
      With the advancement of technology, analyzing and assessing user opinions, as well as determining the user's attitude toward various aspects, have become a challenging and crucial issue. Opinion mining is the process of recognizing people’s attitudes from textual commen More
      With the advancement of technology, analyzing and assessing user opinions, as well as determining the user's attitude toward various aspects, have become a challenging and crucial issue. Opinion mining is the process of recognizing people’s attitudes from textual comments at three different levels: document-level, sentence-level, and aspect-level. Aspect-based Opinion mining analyzes people’s viewpoints on various aspects of a subject. The most important subtask of aspect-based opinion mining is aspect extraction, which is addressed in this paper. Most previous methods suggest a solution that requires labeled data or extensive language resources to extract aspects from the corpus, which can be time consuming and costly to prepare. In this paper, we propose an unsupervised approach for aspect extraction that uses topic modeling and the Word2vec technique to integrate semantic information and domain knowledge based on term graph. The evaluation results show that the proposed method not only outperforms previous methods in terms of aspect extraction accuracy, but also automates all steps and thus eliminates the need for user intervention. Furthermore, because it is not reliant on language resources, it can be used in a wide range of languages. Manuscript profile

    • Open Access Article

      3 - Using limited memory to store the most recent action in XCS learning classifier systems in maze problems
      Ali Yousefi kambiz badie mohamad mehdi ebadzade Arash  Sharifi
      Nowadays, learning classifier systems have received attention in various applications such as sensory robots, humanoid robots, intelligent rescue and rescue systems, and control of physical robots in continuous time environments. In these systems, it is usually possib More
      Nowadays, learning classifier systems have received attention in various applications such as sensory robots, humanoid robots, intelligent rescue and rescue systems, and control of physical robots in continuous time environments. In these systems, it is usually possible to use the combination of an evolutionary algorithm or intuitive methods to search the space of existing rules with another learning process to assign the way of acting with the environment to the existing rules in the category. The basic challenge of using learning classifier systems in the environment of maze problems is that the system must work with higher accuracy and speed. In other words, the stimulus should avoid standing still and hitting the obstacles around it, and by moving in the existing corridor, it will lead to an increase in the probability of reaching the food. In this article, an intelligent learning classifier algorithm based on XCS in not encountering obstacles is presented. In this method, according to the input and actions applied to the environment and the stimulus reaction, the optimal rules are identified and these rules are added as a new category set to the set of rules in XCS. With the addition of this category, the value of its various parameters is changed and the probability of using those rules in the next stages of the stimulus increases. Among the advantages of this method, we can mention the reduction of the number of necessary steps and the increase in the speed of the stimulus reaching the target. Manuscript profile

    • Open Access Article

      4 - Emerging technologies in future generations of high performance computing: introduction, taxonomy and future research directions
      mahmood nematollahzadeh ehsan arianyan Masoud Hayeri Khyavi niloofar gholipoor abdollah sepahvand
      Due to the rapid growth of science and technology, their need for high performance computing is increasing everyday. So far, the majority of the world's high performance computing needs have been based on conventional silicon-based technologies, but the end of the age o More
      Due to the rapid growth of science and technology, their need for high performance computing is increasing everyday. So far, the majority of the world's high performance computing needs have been based on conventional silicon-based technologies, but the end of the age of silicon-based technologies is near, and this fact has led scientists to use emerging technologies such as quantum computing, bio computing, optical computing and similar technologies. Although some of these technologies are not new and the initial introduction of some of them dates back to some decades ago, but due to the attractiveness of classical silicon-based computing and the speed of development in it, have been neglected to date. However, recently, these technologies have begun to be used to build scalable high performance computers. In this paper, we introduce these technologies and how they participate in the field of high performance computing, their current and future status, and their challenges. Also, the taxonomy related to each of these technologies from the computational point of view as well as their research topics are presented, which can be utilized for future research in this field. Manuscript profile

    • Open Access Article

      5 - Improving IoT resource management using fog calculations and ant lion optimization algorithm
      payam shams Seyedeh Leili Mirtaheri reza shahbazian ehsan arianyan
      In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (proc More
      In this paper, a model based on meta-heuristic algorithms for optimal allocation of IoT resources based on fog calculations is proposed. In the proposed model, the user request is first given to the system as a workflow; For each request, the resource requirements (processing power, storage memory, and bandwidth) are first extracted. This component determines the requested traffic status of the application in terms of real-time. If the application is not real-time and is somewhat resistant to latency, the request will be referred to the cloud environment, but if the application needs to respond promptly and is sensitive to latency, it will be dealt with as a fog calculation. It will be written to one of the Cloudletes. In this step, in order to select the best solution in allocating resources to serve the users of the IoT environment, the ant milk optimization algorithm was used. The proposed method is simulated in MATLAB software environment and to evaluate its performance, five indicators of fog cells energy consumption, response time, fog cell imbalance, latency and bandwidth have been used. The results show that the proposed method reduces the energy consumption, latency rate in fog cells, bandwidth consumption rate, load balance rate and response time compared to the base design (ROUTER) 22, 18, 12, 22 and 47, respectively. Percentage has improved Manuscript profile

    • Open Access Article

      6 - Automatic Lung Diseases Identification using Discrete Cosine Transform-based Features in Radiography Images
      Shamim Yousefi Samad Najjar-Ghabel
      The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods More
      The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches. Manuscript profile

    • Open Access Article

      7 - Multicore Fuzzy Clustering of Big Data in the Context of Hadoop MapReduce
      Seyed Omid Azarkasb Seyed Hossein Khasteh Mostafa  Amiri
      Due to the large volume and increasing complexity of data generated or collected, data management, big data analysis has been identified as one of the most important emerging needs in recent years. Fuzzy clustering is a more advanced type of clustering method because fu More
      Due to the large volume and increasing complexity of data generated or collected, data management, big data analysis has been identified as one of the most important emerging needs in recent years. Fuzzy clustering is a more advanced type of clustering method because fuzzy logic shows and reflects how humans think. Fuzzy logic leads to newer intelligent systems that think and make decisions like humans. Accordingly, a logical solution to consider cluster overlap is to assign a set of membership degrees to each data. Due to the reduction of segregation and shrinkage of the search space, fuzzy clustering generally has less computational overhead, and it is easy to detect and manage vague, noisy, and fragmented data. In the meantime, the multicore learning model is able to detect complex relationships between data and, in addition, removes the limitation of spherical cluster detection. The proposed method approach is based on the ideas of feasibility, and uses multicenter learning in the context of Hadoop reduction mapping to identify clusters with complex macro data structures. Hadoop is a distributed big data management and processing framework. Data processing in Hadoop is batch. The degree of feasibility of each data represents the percentage of the property that the data has from the clusters. The proposed method avoids problems such as the use of inefficient cores or irrelevant features by automatically adjusting the weight of the cores in an optimization framework. Manuscript profile

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      8 - The effect of implementation of Internet of Things (IOT) on the Rail Freight Industry
      علی شایان Noureddin Taraz Monfared ali rajabzadeh ghotri
      Internet of Things leverage is rapidly increasing in railway industries. Iran’s rail freight industry has not involved in, yet. The aim of this survey is to identify the effects of implementation of IoT in the Rail Freight Industry in Iran. To gathering the data, Delphi More
      Internet of Things leverage is rapidly increasing in railway industries. Iran’s rail freight industry has not involved in, yet. The aim of this survey is to identify the effects of implementation of IoT in the Rail Freight Industry in Iran. To gathering the data, Delphi method selected and the Snowball technique used for organizing a Panel including twenty experts. To evaluate the outcomes, Inter Quartile Range, Binomial tests, and Mean calculated. Several statements identified and there was broad consensus on the most of the statements which approved that their implementation affects the Iranian rail freight industry, but in different ranks. Finally, the results formed in the Balanced Scorecard’s format. The internal business process has been affected more than the other aspects by the approved statements. Manuscript profile

    • Open Access Article

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

    • Open Access Article

      10 - Improving energy consumption in the IoT using the Krill Heard optimization algorithm and Mobile Sink
      Shayesteh Tabatabaei
      IoT technology includes a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. This paper presents a routing protocol based on a new clustering called KHCMSBA . Propose More
      IoT technology includes a large number of sensor nodes that generate large amounts of data. Optimal energy consumption of sensor nodes is a major challenge in this type of network. This paper presents a routing protocol based on a new clustering called KHCMSBA . Proposed protocol of krill group optimization algorithm based on krill food to cluster sensor nodes. KHCMSBA considers a realistic energy model in the network that is tested in opnet simulator and the results of the simulation are compared with afsrp protocol (Artifical Fish Swarm Routing Protocol). The results of simulation indicate that the proposed method has better performance in terms of energy consumption by 12.71%, passing rate of 14.22%, end-to-end delay by 76.07%, signal to noise ratio of 46.82% compared to AFSRP protocol. Manuscript profile

    • Open Access Article

      11 - Extracting Innovation Strategies and Requirements for Telecommunication Companies: Case Study of Telecommunications Infrastructure company
      Alireza Esmaeeli Alireza Asgharian Takavash Bahreini Nasrin Dastranj Mahshid Ghaffarzadegan Kolsoum Abbasi-Shahkooh Mandana Farzaneh  
      The purpose of this study is to identify effective innovation strategies for a governmental organization and mission-oriented in the field of communication and information technology. The characteristics of the company under study are: governmental organization, having More
      The purpose of this study is to identify effective innovation strategies for a governmental organization and mission-oriented in the field of communication and information technology. The characteristics of the company under study are: governmental organization, having a monopoly market, scattered actions in the field of innovation, having managers interested in organizational innovation and has a clear and up-to-date strategy and structure. In this paper, innovation strategies were collected using comparative studies of similar international companies. Then by using the method of thematic analysis on the data obtained from semi-structured interviews, the strengths and weaknesses related to the innovation were identified. By matching these two categories of information, a number of appropriate strategies have been proposed and their implementation considerations have been expressed based on the specific characteristics of this company. Accordingly, suggestions for future research of this company have been presented to identify appropriate methods of implementation of organizational innovation in similar circumstances. Manuscript profile

    • Open Access Article

      12 - Indigenous model of commercialization of complex technologies based on partnership in the ICT sector
      Mahdi Fardinia Fatemeh saghafi Jalal Haghighat Monfared
       

    • Open Access Article

      13 - Design of Distributed Consensus Controller for Leader Follower Singular Multi agent Systems in the Presence of Sensor Fault
      Saeid Poormirzaee Hamidreza Ahmadzadeh masoud Shafiee
      In this paper, the problem of sensor fault estimation and design of a distributed fault-tolerant controller is investigated to guarantee the leader-follower consensus for homogeneous singular multi-agent systems. First, a novel augmented model for the system is proposed More
      In this paper, the problem of sensor fault estimation and design of a distributed fault-tolerant controller is investigated to guarantee the leader-follower consensus for homogeneous singular multi-agent systems. First, a novel augmented model for the system is proposed. Based on this model, the state and sensor faults of the system are simultaneously estimated by designing a distributed singular observer. The proposed observer also has the ability to estimate time-varying sensor fault. Then, a distributed controller is designed to guarantee the leader-follower consensus using estimation of state and sensor fault. Finally, the validation and efficiency of the proposed control system for the leader-follower consensus of singular multi-agent systems exposed to sensor faults is illustrated by computer simulations. Manuscript profile

    • Open Access Article

      14 - Phenomenological study of managers' lived experience in successful IT strategy execution: Designing an integrated framework of antecedents, processes and consequences
      Mona Jami Pour Shahnaz Akbari Emami Safora Firozeh
      IT strategy is a key factor in improving the process and performance of companies in using IT. Hence, many companies have a strategic planning process, but only a few succeed in implementing strategies efficiently. Therefore, the purpose of this study is to design a pro More
      IT strategy is a key factor in improving the process and performance of companies in using IT. Hence, many companies have a strategic planning process, but only a few succeed in implementing strategies efficiently. Therefore, the purpose of this study is to design a process framework for implementing IT strategy; To identify the drivers, processes and consequences of implementing IT strategy in organizations. The present study is a qualitative research with a phenomenological approach and in order to collect data, open and in-depth interviews were conducted with 10 experts in the field of IT using theoretical sampling. The results of the analysis show that the inputs under the headings of IT strategy implementation include environmental requirements of business continuity, structural-system cohesion, technology-oriented human resources, IT strategic leadership, skill requirements and common values. The second aspect of the IT strategy implementation model includes the dimensions of IT program monitoring and communication, structural appropriateness, development of support policies, budgeting and resource allocation, appropriate training, and the development of supportive culture. Finally, the implications of implementing an IT strategy, including those related to finance, internal process, customer, and growth and learning, were categorized Manuscript profile

    • Open Access Article

      15 - Valuation of digital services in Iran: Empirical proof for Google and Instagram
      FARHAD ASGHARI ESTIAR AMIR MOHAMMADZADEH Ibrahim  Abbasi
      This article surveys the fundamental value of digital platforms, such as Instagram and Google. Despite the commutable nature of digital technologies, it is challenging to value digital services, given that the usage is free of charge. Applying the methodology of discret More
      This article surveys the fundamental value of digital platforms, such as Instagram and Google. Despite the commutable nature of digital technologies, it is challenging to value digital services, given that the usage is free of charge. Applying the methodology of discrete choice experiments, we estimated the value of digital free goods. For the first time in the literature, we obtained data for the willingness-to-pay and willingness-to-accept, together with socio-economic variables. The customer’s valuation of free digital services is on average, for Google, 4.9m Rial per week and Instagram, 3.27. This paper corroborates that Instagram and Google have an intrinsic value to users, despite the fact that the service of the digital platforms is free of charge. This is the beginning of the valuation of free services such as Shad, Rubika, Zarebeen, etc. in Iran, which has played a significant role in the communication industry since the beginning of the Covid-19 pandemic, and in the discussion of the national information network, the market value of the provider companies will be very important. Manuscript profile

    • Open Access Article

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

    • Open Access Article

      17 - Liquidity Risk Prediction Using News Sentiment Analysis
      albadvi albadvi hamed mirashk mehrdad kargari Mohammad Ali Rastegar Mohammad Talebi
      One of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional m More
      One of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional methods of measuring liquidity risk are complex, time-consuming and expensive, which makes its prediction far from possible. Predicting liquidity risk at the right time can prevent serious problems or crises in the bank. In this study, it has been tried to provide an innovative solution for predicting bank liquidity risk and leading scenarios by using the approach of news sentiment analysis. The news sentiment analysis approach about one of the Iranian banks has been used in order to identify dynamic and effective qualitative factors in liquidity risk to provide a simpler and more efficient method for predicting the liquidity risk trend. The proposed method provides practical scenarios for real-world banking risk decision makers. The obtained liquidity risk scenarios are evaluated in comparison with the scenarios occurring in the bank according to the guidelines of the Basel Committee and the opinion of banking experts to ensure the correctness of the predictions and its alignment. The result of periodically evaluating the studied scenarios indicates a relatively high accuracy. The accuracy of prediction in possible scenarios derived from the Basel Committee is 95.5% and in scenarios derived from experts' opinions, 75%. Manuscript profile

    • Open Access Article

      18 - Estimating the Value of Digital Economy Core Spillover in Iran
      Niloufar Moradhassel Bita Mohebikhah
      Background and Purpose: In most of the studies, the direct effects of the ICT sector have been discussed, but the indirect effects (spillover) and how to measure them have not been addressed. This issue is on the agenda of this article. For this purpose, while determini More
      Background and Purpose: In most of the studies, the direct effects of the ICT sector have been discussed, but the indirect effects (spillover) and how to measure them have not been addressed. This issue is on the agenda of this article. For this purpose, while determining the territory of the digital economy, the gross value of the core of the country's digital economy has been estimated. Methodology: In this article, using the Solow growth model, the spillover effects of the core of the digital economy (ICT) have been estimated for the period of 2002-2019. Findings: The results imply that in the period under review, according to the elasticity of labor productivity relative to the share of net capital formation of the ICT sector in the national economy (about 0.3), the spillover effects of the digital economy core have increased from 210 thousand billion Rials in 2015 to 279 thousand billion of Rials in 2019. Manuscript profile

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      19 - Identifying and ranking factors affecting the digital transformation strategy in Iran's road freight transportation industry focusing on the Internet of Things and data analytics
      Mehran Ehteshami Mohammad Hasan Cheraghali Bita Tabrizian Maryam Teimourian sefidehkhan
      This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-stru More
      This research has been done with the aim of identifying and ranking the factors affecting the digital transformation strategy in Iran's road freight transportation industry, focusing on the Internet of Things and data analytics. After reviewing the literature, semi-structured interviews were conducted with 20 academic and road freight transportation industry experts in Iran, who were selected using the purposive sampling method and saturation principle. In the quantitative part, the opinions of 170 employees of this industry, who were selected based on Cochran's formula and stratified sampling method, were collected using a researcher-made questionnaire. Delphi technique, literature review and coding were used to analyze the data in the qualitative part. In the quantitative part, inferential statistics and SPSS and smartPLS software were used. Finally, 40 indicators were extracted in the form of 8 factors and ranking of indicators and affecting factors was done using factor analysis. The result of this research shows that the internal factors have the highest rank and software infrastructure, hardware infrastructure, economic, external factors, legal, cultural and penetration factor are in the next ranks respectively. Therefore, it is suggested that organizations consider their human resource empowerment program in line with the use of technology and digital tools. Manuscript profile

    • Open Access Article

      20 - Presenting a model for predicting the survival of melanoma patients based on data mining algorithms
      farinaz sanaei Seyed Abdollah  Amin Mousavi Abbas Toloie Eshlaghy ali rajabzadeh ghotri
      Introduction: Melanoma is the most common diagnostic cancer among individuals and the second cause of cancer death. The number of people suffering from it is increasing. Melanoma is the rarest and most malignant type of skin cancer. Melanoma is the rarest and most malig More
      Introduction: Melanoma is the most common diagnostic cancer among individuals and the second cause of cancer death. The number of people suffering from it is increasing. Melanoma is the rarest and most malignant type of skin cancer. Melanoma is the rarest and most malignant type of skin cancer. In advanced conditions melanoma has the ability to spread to internal organs in advanced conditions and can lead to death. According to the National Cancer Institute, about 91,270 people were diagnosed with melanoma in 2018 and about 9,320 people died. Therefore, the purpose of this study is to design the most accurate algorithm for predicting the survival of these patients. Methodology: The present method is descriptive-analytical and retrospective in terms of practical nature and purpose. The study population consisted of patients with melanoma cancer database of the National Cancer Research Center affiliated to Shahid Beheshti University located in Shohada Tajrish Hospital (between 2008 and 2012) who were followed up for 5 years. The melanoma survival prediction model was selected based on the evaluation indices of data mining algorithms. Neural network performance was higher in all evaluation indicators than other algorithms. Results: Neural network, simple Bayes, Bayesian network and decision tree combination with simple Bayes, logistic regression, J48, ID3 algorithms were selected as the the country's database mode Conclusion: The neural network performed better in the data set in terms of accuracy, precision and specificity indices, sub-curve surface and kappa test. Therefore, it was selected as a model for predicting melanoma cancer survival. Manuscript profile

    • Open Access Article

      21 - Identify and analyze decision points and key players in procurement process in the EPC companies
      Seyedeh Motahareh  Hosseini aghdasim aghdasim
      Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in o More
      Correct and timely decisions have a significant impact on the performance and achievement of the company's goals. In other words, business process management depends on making and implementing rational decisions. By increasing the integration of information systems in organizations and using tools such as process mining, a platform is provided for the use of data analysis approaches and better analysis of decisions, and managers can act in agile decision making. Selecting a supplier in the process of purchasing in complex projects is one of the basic and key decisions that affect the quality, cost and performance of the project. In this article, with a process perspective, the decision points in the purchasing process in a complex construction project in an EPC company have been discovered and the key players in the implementation of the process have been identified and analyzed through social network analysis. The results of this research have led to the investigation of decision points in the process, the performance of decision points and the identification of key people in decision making, which can be used to improve the company's future performance. Manuscript profile

    • Open Access Article

      22 - Priority based Deployment of IoT Applications in Fog
      Masomeh Azimzadeh Ali Rezaee Somayyeh  Jafarali Jassbi MohammadMahdi Esnaashari
      Fog computing technology has emerged to respond to the need for modern IoT applications for low latency, high security, etc. On the other hand, the limitations of fog computing such as heterogeneity, distribution, and resource constraints make service management in this More
      Fog computing technology has emerged to respond to the need for modern IoT applications for low latency, high security, etc. On the other hand, the limitations of fog computing such as heterogeneity, distribution, and resource constraints make service management in this environment challenging. Intelligent service placement means placing application services on fog nodes to ensure their QoS and effective use of resources. Using communities to organize nodes for service placement is one of the approaches in this area, where communities are mainly created based on the connection density of nodes, and applications are placed based on a single-criteria prioritization approach. This leads to the creation of unbalanced communities and inefficient placement of applications. This paper presents a priority-based method for deploying applications in the fog environment. To this end, balanced communities are created and applications are placed in balanced communities based on a multi-criteria prioritization approach. This leads to optimal use of network capacities and increases in QoS. The simulation results show that the proposed method improves deadline by up to 22%, increases availability by about 12%, and increases resource utilization by up to 10%. Manuscript profile

    • Open Access Article

      23 - Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
      mohammadreza zeraatkarmoghaddam majid ghayori
      Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or i More
      Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or intrusions. In deep learning, the most important challenge for training neural networks is determining the hyperparameters in these networks. To overcome this challenge, we have presented a hybrid approach to automate hyperparameter tuning in deep learning architecture by eliminating the human factor. In this article, an IDS in IIoT based on convolutional neural networks (CNN) and recurrent neural network based on short-term memory (LSTM) using metaheuristic algorithms of particle swarm optimization (PSO) and Whale (WOA) is used. This system uses a hybrid method based on neural networks and metaheuristic algorithms to improve neural network performance and increase detection rate and reduce neural network training time. In our method, considering the PSO-WOA algorithm, the hyperparameters of the neural network are determined automatically without the intervention of human agent. In this paper, UNSW-NB15 dataset is used for training and testing. In this research, the PSO-WOA algorithm has use optimized the hyperparameters of the neural network by limiting the search space, and the CNN-LSTM neural network has been trained with this the determined hyperparameters. The results of the implementation indicate that in addition to automating the determination of hyperparameters of the neural network, the detection rate of are method improve 98.5, which is a good improvement compared to other methods. Manuscript profile

    • Open Access Article

      24 - Presenting a web recommender system for user nose pages using DBSCAN clustering algorithm and machine learning SVM method.
      reza molaee fard mohammad mosleh
      Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can s More
      Recommender systems can predict future user requests and then generate a list of the user's favorite pages. In other words, recommender systems can obtain an accurate profile of users' behavior and predict the page that the user will choose in the next move, which can solve the problem of the cold start of the system and improve the quality of the search. In this research, a new method is presented in order to improve recommender systems in the field of the web, which uses the DBSCAN clustering algorithm to cluster data, and this algorithm obtained an efficiency score of 99%. Then, using the Page rank algorithm, the user's favorite pages are weighted. Then, using the SVM method, we categorize the data and give the user a combined recommender system to generate predictions, and finally, this recommender system will provide the user with a list of pages that may be of interest to the user. The evaluation of the results of the research indicated that the use of this proposed method can achieve a score of 95% in the recall section and a score of 99% in the accuracy section, which proves that this recommender system can reach more than 90%. It detects the user's intended pages correctly and solves the weaknesses of other previous systems to a large extent. Manuscript profile

    • Open Access Article

      25 - Improving the load balancing in Cloud computing using a rapid SFL algorithm (R-SFLA)
      Kiomars Salimi Mahdi Mollamotalebi
      Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of th More
      Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of the load distribution among the processing nodes in order to have the best usage of resources while having minimum response time for the users’ requests. Several methods for load balancing in Cloud computing have been proposed in the literature. The shuffled frog leaping algorithm for load balancing is a dynamic, evolutionary, and inspired by nature. This paper proposed a modified rapid shuffled frog leaping algorithm (R-SFLA) that converge the defective evolution of frogs rapidly. In order to evaluate the performance of R-SFLA, it is compared to Shuffled Frog Leaping Algorithm (SFLA) and Augmented Shuffled Frog Leaping Algorithm (ASFLA) by the overall execution cost, Makespan, response time, and degree of imbalance. The simulation is performed in CloudSim, and the results obtained from the experiments indicated that the proposed algorithm acts more efficient compared to other methods based on the above mentioned factors. Manuscript profile

    • Open Access Article

      26 - Applying deep learning for improving the results of sentiment analysis of Persian comments of Online retail stores
      faezeh forootan Mohammad  Rabiei
      The retail market industry is one of the industries that affects the economies of countries, the life of which depends on the level of satisfaction and trust of customers to buy from these markets. In such a situation, the retail market industry is trying to provide con More
      The retail market industry is one of the industries that affects the economies of countries, the life of which depends on the level of satisfaction and trust of customers to buy from these markets. In such a situation, the retail market industry is trying to provide conditions for customer feedback and interaction with retailers based on web pages and online platforms. Because the analysis of published opinions play a role not only in determining customer satisfaction but also in improving products. Therefore, in recent years, sentiment analysis techniques in order to analyze and summarize opinions, has been considered by researchers in various fields, especially the retail market industry. Manuscript profile

    • Open Access Article

      27 - Evaluation of Interpolation Methods for Estimating the Fading Channels in Digital TV Broadcasting
      Ali Pouladsadeh Mohammadali Sebghati
      Variations in telecommunication channels is a challenge of the wireless communication which makes the channel estimation and equalization a noteworthy issue. In OFDM systems, some subcarriers can be considered as pilots for channel estimation. In the pilot-aided channel More
      Variations in telecommunication channels is a challenge of the wireless communication which makes the channel estimation and equalization a noteworthy issue. In OFDM systems, some subcarriers can be considered as pilots for channel estimation. In the pilot-aided channel estimation procedure, interpolation is an essential step to achieve channel response in data subcarriers. Choosing the best interpolation method has been the subject of various researches, because there is no interpolator as the best method in all conditions, and their performance depends on the fading model, signal-to-noise ratio and pilot overhead ratio. In this paper, the effect of different interpolation methods on the quality of DVB-T2 broadcast links is evaluated. A simulation platform is prepared in which different channel models are defined according to the real-world measurements. The interpolation is performed by five widely-used methods (nearest neighbor, linear, cubic, spline, and Makima) for different pilot ratios. After channel equalization by the results of the interpolator, the bit error rate is calculated as the main criterion for evaluation and comparison. The rules of selecting the appropriate interpolator in different conditions is presented. It is generally concluded that for fading scenarios close to flat fading or high pilot overhead ratio, the simple interpolators such as linear interpolator are proper choices. But in harsh conditions, i.e. severe frequency-selective fading channels or low pilot overhead ratio, the more complicated interpolators such as cubic and spline methods yield better results. The amount of improvements and differences are quantified in this study. Manuscript profile

    • Open Access Article

      28 - WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling
      Arash Ghorbannia Delavar Reza Akraminejad sahar mozafari
      Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for sche More
      Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for scheduling of compressed-instance workflows in things-internet by cluster processing in cloud. A method is presented for scheduling cloud workflows with aim of optimizing energy, throughput, and latency. In the proposed method, time and energy consumption has been improved in comparison to previous methods by creating distance parameters, clustering inputs, and considering real execution time. In WSTMOS method by considering special parameters and real execution time, we managed to reach the optimized objective function. Moreover, in the proposed method parameter of time distance of tasks to virtual machines for reduction of number of migration in virtual machines was applied. In WSTMOS method by organizing the workflow inputs to low, medium and heavy groups and also by distributing appropriate load on more suitable servers for processors threshold, we accomplished to optimize energy and cost. Energy consumption was reduced by 4.8 percent while the cost was cut down by 4.4 percent using this method in comparison to studied method. Finally, average delay time, power and workload are optimized in comparison to previous methods Manuscript profile

    • Open Access Article

      29 - A framework for architecture the electronic trust in e-commerce:online shopping segment
      amir Mohtarami Akbar amini
      Today, e-commerce is rapidly expanding as a way of doing business in the modern world due to its advantages and benefits. The purpose of this study is to extract dimensions and criteria for providing electronic trust arrangements in B2C services, improving the internal More
      Today, e-commerce is rapidly expanding as a way of doing business in the modern world due to its advantages and benefits. The purpose of this study is to extract dimensions and criteria for providing electronic trust arrangements in B2C services, improving the internal processes of the business environment, and also determining the importance and priority of each criterion to ensure electronic trust in order to gain the trust and satisfaction of the customer. A mixed method of research is employed includes: litrature review, field study and opinion gathering alongside of statistical techniques. The statistical population includes all expert customers of online stores in the city of Tehran, among which random sampling has been done. Questions in the context of the electronic trust and provision of e-business services and their priority in relation to each other, are discussed through inferential statistics. The results of the data analysis show that there is a meaningful relationship between the 12 criteria identified and customer's trust. The results obtained in the context of the conceptual framework show the impact of three dimensions of psychological, technical and legal, according to the criteria and indicators of electronic trust. Manuscript profile

    • Open Access Article

      30 - Energy procurement of a cellular base station in independent microgrids with electric vehicles and renewable energy sources: Mixed-integer nonlinear programming model
      Reza Bahri saeed zeynali
      The cellular base stations are communication devices that ensure the connection in the world. Nevertheless, they are usually installed in remote places. This paper, studied the energy procurement of a cellular base stations in an independent microgrid with a hydrogen-ba More
      The cellular base stations are communication devices that ensure the connection in the world. Nevertheless, they are usually installed in remote places. This paper, studied the energy procurement of a cellular base stations in an independent microgrid with a hydrogen-based energy storage system, photovoltaic (PV) system, electric vehicles and a diesel generator. A new mixed-integer nonlinear programming model was used to deal with nonlinearities of the system components. The paper studied different uncertainties, such as the connection rate in cellular base stations, the driver of the electric vehicle, and PV generation, using stochastic programming method. The potency of the proposed method was studied in different case studies. The results prove that smart electric vehicle chargers reduce the risks and also cost/emission objective functions. The usage of this model can reduce the emissions as much as 18.60%. Manuscript profile

    • Open Access Article

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

    • Open Access Article

      32 - Fake Websites Detection Improvement Using Multi Layered Artificial Neural Network Classifier with Ant Lion Optimizer Algorithm
      Farhang Padidaran Moghaddam Mahshid Sadeghi B.
      In phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social More
      In phishing attacks, a fake site is forged from the main site, which looks very similar to the original one. To direct users to these sites, Phishers or online thieves usually put fake links in emails and send them to their victims, and try to deceive users with social engineering methods and persuade them to click on fake links. Phishing attacks have significant financial losses, and most attacks focus on banks and financial gateways. Machine learning methods are an effective way to detect phishing attacks, but this is subject to selecting the optimal feature. Feature selection allows only important features to be considered as learning input and reduces the detection error of phishing attacks. In the proposed method, a multilayer artificial neural network classifier is used to reduce the detection error of phishing attacks, The feature selection phase is performed by the ant lion optimization (ALO) algorithm. Evaluations and experiments on the Rami dataset, which is related to phishing, show that the proposed method has an accuracy of about 98.53% and has less error than the multilayer artificial neural network. The proposed method is more accurate in detecting phishing attacks than BPNN, SVM, NB, C4.5, RF, and kNN learning methods with feature selection mechanism by PSO algorithm Manuscript profile

    • Open Access Article

      33 - Artefacts and Producers Mapping of Iran's Artificial Intelligence Ecosystem based on Transformational Levels
      hamed ojaghi Iman Zohoorian Nadali Fatemeh Soleymani Roozbahani
      As an emerging technological field, artificial intelligence has received increasing attention from companies and governments. The development of artificial intelligence both at business and country levels depends on knowing the current situation. This paper identifies t More
      As an emerging technological field, artificial intelligence has received increasing attention from companies and governments. The development of artificial intelligence both at business and country levels depends on knowing the current situation. This paper identifies the artifacts and producers presented in this field and maps them to transformational levels. Products/services and producers are achieved through capabilities provided by artificial intelligence. Then, based on the classification methodology and meta-characteristics, the transformational levels of the artifacts of Iran's artificial intelligence ecosystem have been extracted. 562 products/services were identified, which were offered by 112 companies. Machine vision and natural language processing have been at the top of the technologies used, with 44 and 27 percent of the products allocated to them, respectively. Artifacts and producers were classified into seven transformative levels: individual, organization, industry, electronic chip/hardware, society, platform, code/algorithm/library, and infrastructure. Iran's artificial intelligence productions have not grown in a balanced way. The three levels of platform, code/algorithm/library, and infrastructure as the main generator of other artificial intelligence products/services have had the lowest amount of production. It is suggested that a specialized marketplace for the supply of artificial intelligence application programming interfaces should be put on the agenda to stimulate the formation of the ecosystem Manuscript profile

    • Open Access Article

      34 - Anomaly and Intrusion Detection through Datamining and Feature Selection using PSO Algorithm
      Fereidoon Rezaei Mohamad Ali Afshar Kazemi Mohammad Ali Keramati
      Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, th More
      Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as J48 and PSO, we were able to significantly improve the accuracy of detecting anomalies and attacks. Manuscript profile

    • Open Access Article

      35 - Drivers, Obstacles and consequences of digital entrepreneurship in Iran's road freight transportation industry
      Azam sadta Mortazavi kahangi Parviz Saketi Javad Mehrabi
      The purpose of this research is to identify the drivers, obstacles and consequences of digital entrepreneurship in Iran's road freight transportation industry. The statistical society of this research in the qualitative part was made up of 20 experts in this field who w More
      The purpose of this research is to identify the drivers, obstacles and consequences of digital entrepreneurship in Iran's road freight transportation industry. The statistical society of this research in the qualitative part was made up of 20 experts in this field who were selected using theoretical saturation. In the quantitative part, using Cochran's formula and cluster sampling method, 170 employees of this industry were selected as samples. In order to collect data, a semi-structured interview was used in the qualitative part and a researcher-made questionnaire was used in the quantitative part, whose validity and reliability were checked and confirmed. In the data analysis, systematic literature review and coding and Maxqda software were used in the qualitative part, and inferential statistics and SPSS and Lisrel software were used in the quantitative part. Finally, 9 indicators in 4 driver factors, 11 indicators in 3 obstacle factors and 55 indicators in 8 consequence categories were extracted and prioritized using factor analysis. The result of this research shows that the political component is a priority as a driver and political obstacles are a priority as an obstacle. Therefore, the role of the government in this field is very important. Manuscript profile

    • Open Access Article

      36 - Intrusion Detection Based on Cooperation on the Permissioned Blockchain Platform in the Internet of Things Using Machine Learning
      Mohammad Mahdi  Abdian majid ghayori Seyed Ahmad  Eftekhari
      Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauth More
      Intrusion detection systems seek to realize several objectives, such as increasing the true detection rate, reducing the detection time, reducing the computational load, and preserving the resulting logs in such a way that they cannot be manipulated or deleted by unauthorized people. Therefore, this study seeks to solve the challenges by benefiting from the advantages of blockchain technology, its durability, and relying on IDS architecture based on multi-node cooperation. The proposed model is an intrusion detection engine based on the decision tree algorithm implemented in the nodes of the architecture. The architecture consists of several connected nodes on the blockchain platform. The resulting model and logs are stored on the blockchain platform and cannot be manipulated. In addition to the benefits of using blockchain, reduced occupied memory, the speed, and time of transactions are also improved by blockchain. In this research, several evaluation models have been designed for single-node and multi-node architectures on the blockchain platform. Finally, proof of architecture, possible threats to architecture, and defensive ways are explained. The most important advantages of the proposed scheme are the elimination of the single point of failure, maintaining trust between nodes, and ensuring the integrity of the model, and discovered logs. Manuscript profile

    • Open Access Article

      37 - Face recognition and Liveness Detection Based on Speech Recognition for Electronical Authentication
      ahmad dolatkhah بهنام  درستکار یاقوتی raheb hashempour
      As technology develops, institutions and organizations provide many services electronically and intelligently over the Internet. The police, as an institution that provides services to people and other institutions, aims to make its services smarter. Various electronic More
      As technology develops, institutions and organizations provide many services electronically and intelligently over the Internet. The police, as an institution that provides services to people and other institutions, aims to make its services smarter. Various electronic and intelligent systems have been offered in this regard. Because these systems lack authentication, many services that can be provided online require a visit to +10 police stations. Budget and equipment limitations for face-to-face responses, limitations of the police force and their focus on essential issues, a lack of service offices in villages and a limited number of service offices in cities, and the growing demand for online services, especially in crisis situations like Corona disease, electronic authentication is becoming increasingly important. This article reviews electronic authentication and its necessity, liveness detection methods and face recognition which are two of the most important technologies in this area. In the following, we present an efficient method of face recognition using deep learning models for face matching, as well as an interactive liveness detection method based on Persian speech recognition. A final section of the paper presents the results of testing these models on relevant data from this field. Manuscript profile
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    Iranian Information and Communication Technology Association
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    Masoud Shafiee (Amirkabir University of Technology)
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    Mohammad-Shahram Moin (Research Institute of Communication and Information Technology)
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    Mohammad-Shahram Moin (Research Institute of Communication and Information Technology)
    Editorial Board
    H. Nezamabadi-pour Ramezan Ali Sadeghzadeh Hassan Rashidi Alireza Behrad Mir Mohsen Pedram Fatemeh saghafi (Associate Prof. of University of Tehran ) ميرهادي سيد عربي mohamad hesam tadayon mohsen Ebrahimi-Moghaddam Masoud Shafiee (Amirkabir University of Technology) Mohamadreza Aref (Sharif University of Technology) Ahmad Motamedi (Amirkabir University of Technology) Ahmad-Reza Sherafat Reza FarajiDana Mohammad Teshnelab (Khajeh Nasir al-Din Tusi University of Technology) Mohammad-Shahram Moin (Research Institute of Communication and Information Technology) mahmoud kamarei (tehran university)
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