• OpenAccess
  • About the journal

     The Journal of Information Systems and Telecommunication (JIST) accepts and publishes papers containing original researches and/or development results, representing an effective and novel contribution for knowledge in the area of information systems and Telecommunication. Contributions are accepted in the form of Regular papers or Correspondence. Regular papers are the ones with a well-rounded treatment of a problem area, whereas Correspondence focus on a point of a defined problem area. Under the permission of the editorial board, other kinds of papers may be published if they are found to be relevant or of interest to the readers. Responsibility for the content of the papers rests upon the Authors only. The Journal is aimed at not only a national target community, but also international audiences is taken into consideration. For this reason, authors are supposed to write in English.

    This Journal is Published under scientific support of Advanced Information Systems (AIS) Research Group and Digital & Signal Processing Group, ICTRC

    For further information on Article Processing Charges (APCs) policies, please visit our APC page or contact us infojist@gmail.com. 

     


    Recent Articles

    • Open Access Article

      1 - Implementation of Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) on Zync FPGA Device
      Ahmed Belhani Hichem Semira Rania Kheddara Ghada Hassis
      Issue 44 , Vol. 11 , Autumn 2023
      The non-orthogonal access schemes are one of the multiple access techniques that are candidates to become an access technique for the next generation access radio. Power-domain non-orthogonal multiple-access (NOMA) is among these promising technologies. Improving the ne More
      The non-orthogonal access schemes are one of the multiple access techniques that are candidates to become an access technique for the next generation access radio. Power-domain non-orthogonal multiple-access (NOMA) is among these promising technologies. Improving the network capacity by providing massive connectivity through sharing the same spectral resources is the main advantage that this technique offers. The NOMA technique consists of exploiting the power domain which multiplex multiple users on the same resources applying a superposition coding then separating the multiplexed users at the receiver side. Due to the non-orthogonality access technique, the main disadvantage of NOMA is the presence of interferences between users. That is why this scheme is based on a successive interference cancelation (SIC) detector that separates the multiplexed signals at the receiver. In this paper, an embedded system is considered for designing and implementation of the power-NOMA For two users. The implementation is realized by employing a Zynq FPGA (Field programmable gate array) device through the Zybo-Z7 board using MATLAB/Simulink environment and Xilinx System Generator. The features offered by this device, hemps to consider the design of an uplink and a downlink scenario over Rayleigh fading channel in additive white Gaussian noise (AWGN) environment. Manuscript profile

    • Open Access Article

      2 - A Recommender System for Scientific Resources Based on Recurrent Neural Networks
      Hadis Ahmadian Seyed Javad  Mahdavi Chabok Maryam  Kheirabadi
      Issue 44 , Vol. 11 , Autumn 2023
      Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses direct More
      Over the last few years, online training courses have had a significant increase in the number of participants. However, most web-based educational systems have drawbacks compared to traditional classrooms. On the one hand, the structure and nature of the courses directly affect the number of active participants; on the other hand, it becomes difficult for teachers to guide students in choosing the appropriate learning resource due to the abundance of online learning resources. Students also find it challenging to decide which educational resources to choose according to their condition. The resource recommender system can be used as a Guide tool for educational resource recommendations to students so that these suggestions are tailored to the preferences and needs of each student. In this paper, it was presented a resource recommender system with the help of Bi-LSTM networks. Utilizing this type of structure involves both long-term and short-term interests of the user and, due to the gradual learning property of the system, supports the learners' behavioral changes. It has more appropriate recommendations with a mean accuracy of 0.95 and a loss of 0.19 compared to a similar article. Manuscript profile

    • Open Access Article

      3 - A New Power Allocation Optimization for One Target Tracking in Widely Separated MIMO Radar
      Mohammad Akhondi Darzikolaei Mohammad Reza Karami-Mollaei Maryam Najimi
      Issue 44 , Vol. 11 , Autumn 2023
      In this paper, a new power allocation scheme for one target tracking in MIMO radar with widely dispersed antennas is designed. This kind of radar applies multiple antennas which are deployed widely dispersed from each other. Therefore, a target is observed simultaneousl More
      In this paper, a new power allocation scheme for one target tracking in MIMO radar with widely dispersed antennas is designed. This kind of radar applies multiple antennas which are deployed widely dispersed from each other. Therefore, a target is observed simultaneously from different uncorrelated angles and it offers spatial diversity. In this radar, a target’s radar cross section (RCS) is different in each transmit-receive path. So, a random complex Gaussian RCS is supposed for one target. Power allocation is used to allocate the optimum power to each transmit antenna and avoid illuminating the extra power in the environment and hiding it from interception. This manuscript aims to minimize the target tracking error with constraints on total transmit power and the power of each transmit antenna. For calculation of target tracking error, the joint Cramer Rao bound for a target velocity and position is computed and this is assumed as an objective function of the problem. It should be noted that a target RCS is also considered as unknown parameter and it is estimated along with target parameters. This makes a problem more similar to real conditions. After the investigation of the problem convexity, the problem is solved by particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms. Then, various scenarios are simulated to evaluate the proposed scheme. The simulation results validate the accuracy and the effectiveness of the power allocation structure for target tracking in MIMO radar with widely separated antennas. Manuscript profile

    • Open Access Article

      4 - Inferring Diffusion Network from Information Cascades using Transitive Influence
      Mehdi Emadi Maseud Rahgozar Farhad Oroumchian
      Issue 44 , Vol. 11 , Autumn 2023
      Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a so More
      Nowadays, online social networks have a great impact on people’s life and how they interact. News, sentiment, rumors, and fashion, like contagious diseases, are propagated through online social networks. When information is transmitted from one person to another in a social network, a diffusion process occurs. Each node of a network that participates in the diffusion process leaves some effects on this process, such as its transmission time. In most cases, despite the visibility of such effects of diffusion process, the structure of the network is unknown. Knowing the structure of a social network is essential for many research studies such as: such as community detection, expert finding, influence maximization, information diffusion, sentiment propagation, immunization against rumors, etc. Hence, inferring diffusion network and studying the behavior of the inferred network are considered to be important issues in social network researches. In recent years, various methods have been proposed for inferring a diffusion network. A wide range of proposed models, named parametric models, assume that the pattern of the propagation process follows a particular distribution. What's happening in the real world is very complicated and cannot easily be modeled with parametric models. Also, the models provided for large volumes of data do not have the required performance due to their high execution time. However, in this article, a nonparametric model is proposed that infers the underlying diffusion network. In the proposed model, all potential edges between the network nodes are identified using a similarity-based link prediction method. Then, a fast algorithm for graph pruning is used to reduce the number of edges. The proposed algorithm uses the transitive influence principle in social networks. The time complexity order of the proposed method is O(n3). This method was evaluated for both synthesized and real datasets. Comparison of the proposed method with state-of-the-art on different network types and various models of information cascades show that the model performs better precision and decreases the execution time too. Manuscript profile

    • Open Access Article

      5 - Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks
      maryam Najimi
      Issue 44 , Vol. 11 , Autumn 2023
      Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor networ More
      Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor network, in which the cognitive sensor transmitter has the capability of the energy harvesting. In the first slot, the cognitive sensor transmitter participates in spectrum sensing and in the existence of the primary user, it harvests the energy from the primary signal, otherwise the sensor transmitter sends its signal to the corresponding receiver while in the second slot, using the decode-and-forward (DF) protocol, a part of the bandwidth is used to forward the signal of the primary user and the remained bandwidth is used for transmission of the cognitive sensor. Therefore, our purposed algorithm is to maximize the cognitive network transmission rate by selection of the suitable cognitive sensor transmitters subject to the rate of the primary transmission and energy consumption of the cognitive sensors according to the mobility model of the cognitive sensors in the dynamic network. Simulation results illustrate the effectiveness of the proposed algorithm in performance improvement of the network as well as reducing the energy consumption. Manuscript profile

    • Open Access Article

      6 - Application of Machine Learning in the Telecommunications Industry: Partial Churn Prediction by using a Hybrid Feature Selection Approach
      Fatemeh Mozaffari Iman Raeesi Vanani Payam Mahmoudian Babak Sohrabi
      Issue 44 , Vol. 11 , Autumn 2023
      The telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic d More
      The telecommunications industry is one of the most competitive industries in the world. Because of the high cost of customer acquisition and the adverse effects of customer churn on the company's performance, customer retention becomes an inseparable part of strategic decision-making and one of the main objectives of customer relationship management. Although customer churn prediction models are widely studied in various domains, several challenges remain in designing and implementing an effective model. This paper addresses the customer churn prediction problem with a practical approach. The experimental analysis was conducted on the customers' data gathered from available sources at a telecom company in Iran. First, partial churn was defined in a new way that exploits the status of customers based on criteria that can be measured easily in the telecommunications industry. This definition is also based on data mining techniques that can find the degree of similarity between assorted customers with active ones or churners. Moreover, a hybrid feature selection approach was proposed in which various feature selection methods, along with the crowd's wisdom, were applied. It was found that the wisdom of the crowd can be used as a useful feature selection method. Finally, a predictive model was developed using advanced machine learning algorithms such as bagging, boosting, stacking, and deep learning. The partial customer churn was predicted with more than 88% accuracy by the Gradient Boosting Machine algorithm by using 5-fold cross-validation. Comparative results indicate that the proposed model performs efficiently compared to the ones applied in the previous studies. Manuscript profile

    • Open Access Article

      7 - Convolutional Neural Networks for Medical Image Segmentation and Classification: A Review
      Jenifer S Carmel Mary Belinda M J
      Issue 44 , Vol. 11 , Autumn 2023
      Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep le More
      Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep Convolutional Neural Networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the works exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pre-trained models and General Adversarial Networks that aid in improving convolutional networks’ performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on covid-19 detection and child bone age prediction. Manuscript profile

    • Open Access Article

      8 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
      Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem
      Issue 44 , Vol. 11 , Autumn 2023
      Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More
      Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile

    • Open Access Article

      9 - Software-Defined Networking Adoption Model: Dimensions and Determinants
      Elham Ziaeipour Ali Rajabzadeh Ghotri Alireza Taghizadeh
      Issue 44 , Vol. 11 , Autumn 2023
      The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Know More
      The recent technical trend in the field of communication networks shows a paradigm change from hardware to software. Software Defined Networking (SDN) as one of the enablers of digital transformation could have prominent role in this paradigm shift and migration to Knowledge-based network. In this regard, telecom operators are interested in deploying SDN to migrate their infrastructure from a static architecture to a dynamic and programmable platform. However, it seems that they do not consider SDN as one of their priorities and still depend on traditional methods to manage their network (especially in some developing countries such as Iran). Since the first step in applying new technologies is to accept them, we have proposed a comprehensive SDN adoption model with the mixed-method research methodology. At first, the theoretical foundations related to the research problem were examined. Then, based on Grounded theory, in-depth interviews were conducted with 12 experts (including university professors and managers of the major telecom operators). In result, more than a thousand initial codes were determined, which in the review stages and based on semantic commonalities, a total of 112 final codes, 14 categories and 6 themes have been extracted using open, axial and selective coding. Next, in order to confirm the indicators extracted from the qualitative part, the fuzzy Delphi method has been used. In the end, SPSS and SmartPLS 3 software were used to analyze the data collected from the questionnaire and to evaluate the fit of the model as well as confirm and reject the hypotheses. Manuscript profile
    Most Viewed Articles

    • Open Access Article

      1 - Privacy Preserving Big Data Mining: Association Rule Hiding
      Golnar Assadat  Afzali shahriyar mohammadi
      Issue 14 , Vol. 4 , Spring 2016
      Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to More
      Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to cover relationships between seemingly unrelated data in a data base.. Association rule hiding is a research area in privacy preserving data mining (PPDM) which addresses a solution for hiding sensitive rules within the data problem. Many researches have be done in this area, but most of them focus on reducing undesired side effect of deleting sensitive association rules in static databases. However, in the age of big data, we confront with dynamic data bases with new data entrance at any time. So, most of existing techniques would not be practical and must be updated in order to be appropriate for these huge volume data bases. In this paper, data anonymization technique is used for association rule hiding, while parallelization and scalability features are also embedded in the proposed model, in order to speed up big data mining process. In this way, instead of removing some instances of an existing important association rule, generalization is used to anonymize items in appropriate level. So, if necessary, we can update important association rules based on the new data entrances. We have conducted some experiments using three datasets in order to evaluate performance of the proposed model in comparison with Max-Min2 and HSCRIL. Experimental results show that the information loss of the proposed model is less than existing researches in this area and this model can be executed in a parallel manner for less execution time Manuscript profile

    • Open Access Article

      2 - Instance Based Sparse Classifier Fusion for Speaker Verification
      Mohammad Hasheminejad Hassan Farsi
      Issue 15 , Vol. 4 , Summer 2016
      This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers More
      This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and pre-enrolled target speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receives some scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification by adding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers for each test instance. We address both problems, by an instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, minDCF and minCLLR show the effectiveness of the proposed method. Manuscript profile

    • Open Access Article

      3 - Node Classification in Social Network by Distributed Learning Automata
      Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda
      Issue 18 , Vol. 5 , Spring 2017
      The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitio More
      The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved. Manuscript profile

    • Open Access Article

      4 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
      Hamideh Sadat Cheraghchi Ali Zakerolhossieni
      Issue 14 , Vol. 4 , Spring 2016
      The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social More
      The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript profile

    • Open Access Article

      5 - A Bio-Inspired Self-configuring Observer/ Controller for Organic Computing Systems
      Ali Tarihi Hassan Haghighi Fereidoon  Shams Aliee
      Issue 15 , Vol. 4 , Summer 2016
      The increase in the complexity of computer systems has led to a vision of systems that can react and adapt to changes. Organic computing is a bio-inspired computing paradigm that applies ideas from nature as solutions to such concerns. This bio-inspiration leads to the More
      The increase in the complexity of computer systems has led to a vision of systems that can react and adapt to changes. Organic computing is a bio-inspired computing paradigm that applies ideas from nature as solutions to such concerns. This bio-inspiration leads to the emergence of life-like properties, called self-* in general which suits them well for pervasive computing. Achievement of these properties in organic computing systems is closely related to a proposed general feedback architecture, called the observer/controller architecture, which supports the mentioned properties through interacting with the system components and keeping their behavior under control. As one of these properties, self-configuration is desirable in the application of organic computing systems as it enables by enabling the adaptation to environmental changes. However, the adaptation in the level of architecture itself has not yet been studied in the literature of organic computing systems. This limits the achievable level of adaptation. In this paper, a self-configuring observer/controller architecture is presented that takes the self-configuration to the architecture level. It enables the system to choose the proper architecture from a variety of possible observer/controller variants available for a specific environment. The validity of the proposed architecture is formally demonstrated. We also show the applicability of this architecture through a known case study. Manuscript profile

    • Open Access Article

      6 - Publication Venue Recommendation Based on Paper’s Title and Co-authors Network
      Ramin Safa Seyed Abolghassem Mirroshandel Soroush Javadi Mohammad Azizi
      Issue 21 , Vol. 6 , Winter 2018
      Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying More
      Information overload has always been a remarkable topic in scientific researches, and one of the available approaches in this field is employing recommender systems. With the spread of these systems in various fields, studies show the need for more attention to applying them in scientific applications. Applying recommender systems to scientific domain, such as paper recommendation, expert recommendation, citation recommendation and reviewer recommendation, are new and developing topics. With the significant growth of the number of scientific events and journals, one of the most important issues is choosing the most suitable venue for publishing papers, and the existence of a tool to accelerate this process is necessary for researchers. Despite the importance of these systems in accelerating the publication process and decreasing possible errors, this problem has been less studied in related works. So in this paper, an efficient approach will be suggested for recommending related conferences or journals for a researcher’s specific paper. In other words, our system will be able to recommend the most suitable venues for publishing a written paper, by means of social network analysis and content-based filtering, according to the researcher’s preferences and the co-authors’ publication history. The results of evaluation using real-world data show acceptable accuracy in venue recommendations. Manuscript profile

    • Open Access Article

      7 - Using Residual Design for Key Management in Hierarchical Wireless Sensor Networks
      Vahid Modiri Hamid Haj Seyyed Javadi Amir Masoud  Rahmani Mohaddese Anzani
      Issue 29 , Vol. 8 , Winter 2020
      Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial stru More
      Combinatorial designs are powerful structures for key management in wireless sensor networks to address good connectivity and also security against external attacks in large scale networks. Many researchers have used key pre-distribution schemes using combinatorial structures in which key-rings, are pre-distributed to each sensor node before deployment in a real environment. Regarding the restricted resources, key distribution is a great engagement and challenging issue in providing sufficient security in wireless sensor networks. To provide secure communication, a unique key should be found from their stored key-rings. Most of the key pre-distribution protocols based on public-key mechanisms could not support highly scalable networks due to their key storage overhead and communication cost that linearly increasing. In this paper, we introduce a new key distribution approach for hierarchical clustered wireless sensor networks. Each cluster has a construction that contains new points or that reinforces and builds upon similar ideas of their head clusters. Based on Residual Design as a powerful algebraic combinatorial architecture and hierarchical network model, our approach guarantees good connectivity between sensor nodes and also cluster heads. Compared with similar existing schemes, our approach can provide sufficient security no matter if the cluster head or normal sensor node is compromised Manuscript profile

    • Open Access Article

      8 - Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm
      keyvan Borna Sepideh Palizdar
      Issue 16 , Vol. 4 , Autumn 2016
      Predicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, lin More
      Predicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, linear prediction methods and neural networks and fuzzy logic have been studied and emulated. An optimized fuzzy-wavelet prediction method is proposed to predict the price of electricity. In this method, in order to have a better prediction, the membership functions of the fuzzy regression along with the type of the wavelet transform filter have been optimized using the E.Coli Bacterial Foraging Optimization Algorithm. Then, to better compare this optimal method with other prediction methods including conventional linear prediction and neural network methods, they were analyzed with the same electricity price data. In fact, our fuzzy-wavelet method has a more desirable solution than previous methods. More precisely by choosing a suitable filter and a multiresolution processing method, the maximum error has improved by 13.6%, and the mean squared error has improved about 17.9%. In comparison with the fuzzy prediction method, our proposed method has a higher computational volume due to the use of wavelet transform as well as double use of fuzzy prediction. Due to the large number of layers and neurons used in it, the neural network method has a much higher computational volume than our fuzzy-wavelet method. Manuscript profile

    • Open Access Article

      9 - The Innovation Roadmap and Value Creation for Information Goods Pricing as an Economic Commodity
      Hekmat Adelnia Najafabadi Ahmadreza Shekarchizadeh Akbar Nabiollahi Naser Khani Hamid Rastegari
      Issue 26 , Vol. 7 , Spring 2019
      Nowadays, most books and information resources or even movies and application programs are produced and reproduced as information goods. Regarding characteristics of information goods, its cost structure and market, the usual and traditional pricing methods for such com More
      Nowadays, most books and information resources or even movies and application programs are produced and reproduced as information goods. Regarding characteristics of information goods, its cost structure and market, the usual and traditional pricing methods for such commodity are not useful and the information goods pricing has undergone innovative approaches. The purpose of product pricing is to find an optimal spot for maximizing manufacturers' profits and consumers' desirability. Undoubtedly, in order to achieve this goal, it is necessary to adopt appropriate strategies and implement innovative strategies. Innovative strategies and tactics reflect the analysis of market share, customer behavior change, pattern of cost, customer preferences, quick response to customer needs, market forecast, appropriate response to market changes, customer retention, discovery of their specific requirements, cost reduction and customer satisfaction increase. In this research, 32 papers have been selected among 540 prestigious articles to create a canvas containing more than 20 possible avenues for innovations in the field of information goods pricing, which can be used in the companies producing information goods, regardless of their size, nationality, and type of information goods they produce. Introduction of some key ideas on how to increase both profits and customer satisfaction and also three open issues for future research in the field of information goods pricing is one of the achievements of this research. Manuscript profile

    • Open Access Article

      10 - Low-Complexity Iterative Detection for Uplink Multiuser Large-Scale MIMO
      Mojtaba Amiri Mahmoud Ferdosizade Naeiny
      Issue 29 , Vol. 8 , Winter 2020
      In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative metho More
      In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative method is proposed for detection of the transmitted symbols in uplink multiuser massive MIMO systems. Linear detection algorithms such as minimum-mean-square-error (MMSE) and zero-forcing (ZF), are able to achieve the performance of the near optimal detector, when the number of base station (BS) antennas is enough high. But the complexity of linear detectors in Massive MIMO systems is high due to the necessity of the calculation of the inverse of a large dimension matrix. In this paper, we address the problem of reducing the complexity of the MMSE detector for massive MIMO systems. The proposed method is based on Gram Schmidt algorithm, which improves the convergence speed and also provides better error rate than the alternative methods. It will be shown that the complexity order is reduced from O(〖n_t〗^3) to O(〖n_t〗^2), where n_t is the number of users. The proposed method avoids the direct computation of matrix inversion. Simulation results show that the proposed method improves the convergence speed and also it achieves the performance of MMSE detector with considerable lower computational complexity. Manuscript profile
    Upcoming Articles
  • Affiliated to
    Iranian Academic Center for Education,Culture and Research
    Director-in-Charge
    Habibollah Asghari (Research Institute for Information and Communication Technology, ACECR)
    Editor-in-Chief
    Masood Shafiei (Amirkabir University)
    Executive Manager
    Shirin Gilaki (Research Institute for Information and Communication Technology, ACECR)
    Editorial Board
    Abdolali Abdipour (Amirkabir University of Technology) Aliakbar Jalali (University of Maryland) Ali Mohammad Djafari (Le Centre National de la Recherche Scientifique (CNRS)) Alireza Montazemi (McMaster University) Hamidreza Sadegh Mohammadi (ACECR) Mahmoud Moghavemi (University of Malaya) Mehrnoush Shamsfard (Shahid Beheshti University) Omid Mahdi Ebadati (Kharazmi University) Ramazan Ali Sadeghzadeh (K. N. Toosi University of Technology) Rahim Saeidi (eaglegenomics) Saeed Ghazimaghrebi (Islamic Azad University, Shahr-e-Rey) Shaban Elahi (Vali-e-asr University of Rafsanjan) Shohreh Kasaei (Sharif University of Technology) Zabih Ghasemlooy ( University of Northumbria )
    Print ISSN: 2322-1437
    Online ISSN:2345-2773

    Publication period: Quarterly
    Email
    infojist@gmail.com , info.jist@acecr.org
    Address
    No.5, Saeedi Alley, Kalej Intersection., Enghelab Ave., Tehran, Iran.
    Phone
    +98 21 88930150
    Fax
    +9821-88930157

    Search

    Statistics

    Last Update 3/5/2024