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  • About 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 - A High Performance Dual Stage Face Detection Algorithm Implementation using FPGA Chip and DSP Processor
      M V Ganeswara Rao P Ravi  Kumar T  Balaji
      Iss. 40 , Vol. 10 , Autumn 2022
      A dual stage system architecture for face detection based on skin tone detection and Viola and Jones face detection structure is presented in this paper. The proposed architecture able to track down human faces in the image with high accuracy within time constrain. A no More
      A dual stage system architecture for face detection based on skin tone detection and Viola and Jones face detection structure is presented in this paper. The proposed architecture able to track down human faces in the image with high accuracy within time constrain. A non-linear transformation technique is introduced in the first stage to reduce the false alarms in second stage. Moreover, in the second stage pipe line technique is used to improve overall throughput of the system. The proposed system design is based on Xil¬inx’s Virtex FPGA chip and Texas Instruments DSP processor. The dual port BRAM memory in FPGA chip and EMIF (External Memory Interface) of DSP processor are used as interface between FPGA and DSP processor. The proposed system exploits advantages of both the computational elements (FPGA and DSP) and the system level pipelining to achieve real time perform¬ance. The present system implementation focuses on high accurate and high speed face detec¬tion and this system evaluated using standard BAO image database, which include images with different poses, orientations, occlusions and illumination. The proposed system attained 16.53 FPS frame rate for the input image spatial resolution of 640X480, which is 23.4 times faster detection of faces compared to MATLAB implementation and 12.14 times faster than DSP implementation and 2.1 times faster than FPGA implementation. Manuscript profile

    • Open Access Article

      2 - A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems
      Mojtaba Amiri Amir Akhavan
      Iss. 40 , Vol. 10 , Autumn 2022
      Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spe More
      Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods. Manuscript profile

    • Open Access Article

      3 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
      hadi shakibian Jalaledin Nasiri
      Iss. 40 , Vol. 10 , Autumn 2022
      An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More
      An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile

    • Open Access Article

      4 - Detection of Attacks and Anomalies in the Internet of Things System using Neural Networks Based on Training with PSO Algorithms, Fuzzy PSO, Comparative PSO and Mutative PSO
      Mohammad  Nazarpour navid nezafati Sajjad  Shokouhyar
      Iss. 40 , Vol. 10 , Autumn 2022
      Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detect More
      Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore,in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB. Manuscript profile

    • Open Access Article

      5 - ARASP: An ASIP Processor for Automated Reversible Logic Synthesis
      Zeinab Kalantari Marzieh Gerami Mohammad eshghi
      Iss. 40 , Vol. 10 , Autumn 2022
      Reversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversib More
      Reversible logic has been emerged as a promising computing paradigm to design low power circuits in recent years. The synthesis of reversible circuits is very different from that of non-reversible circuits. Many researchers are studying methods for synthesizing reversible combinational logic. Some automated reversible logic synthesis methods use optimization algorithms Optimization algorithms are used in some automated reversible logic synthesis techniques. In these methods, the process of finding a circuit for a given function is a very time-consuming task, so it’s better to design a processor which speeds up the process of synthesis. Application specific instruction set processors (ASIP) can benefit the advantages of both custom ASIC chips and general DSP chips. In this paper, a new architecture for automatic reversible logic synthesis based on an Application Specific Instruction set Processors is presented. The essential purpose of the design was to provide the programmability with the specific necessary instructions for automated synthesis reversible. Our proposed processor that we referred to as ARASP is a 16-bit processor with a total of 47 instructions, which some specific instruction has been set for automated synthesis reversible circuits. ARASP is specialized for automated synthesis of reversible circuits using Genetic optimization algorithms. All major components of the design are comprehensively discussed within the processor core. The set of instructions is provided in the Register Transform Language completely. Afterward, the VHDL code is used to test the proposed architecture. Manuscript profile

    • Open Access Article

      6 - Propose an E-CRM Model based on Mobile Computing Technology in Pharma Distribution Industry
      Alireza Kamanghad Gholamreza Hashemzade Mohammadali Afshar kazemi Nosratollah Shadnoosh
      Iss. 40 , Vol. 10 , Autumn 2022
      In today’s world, the competition between all business areas and companies including pharma distributor companies has increased dramatically, so it is very important for active companies in the pharma distribution industry which deal with a large number of customers in More
      In today’s world, the competition between all business areas and companies including pharma distributor companies has increased dramatically, so it is very important for active companies in the pharma distribution industry which deal with a large number of customers in a B2B market to establish a deep and long-term relationship with their customers and manage that relationship effectively. Since the CRM which now is enriched by new emerging technologies in terms of e-CRM and m-CRM is under developing rapidly, it can play a critical role for empowering these companies to strengthen their relationship with their customers. In this research it has been tried to have a complete review of mobile computing technology concept and its effect on CRM. The research methodology is basically qualitative. After a literature review, using qualitative research methods and deep interviews with a group of 8 industry experts, the whole concept of the initial model was derived using Thematic Analysis method. The Grounded Theory approach was applied to extract the main factors and sub-factors of the final model. Additionally, some of \research techniques such as Dematel, ANP and Super Decision software were used to investigate the interdependency, importance and priority of factors and sub-factors. At the last stage a new model for e-CRM in pharma distribution industry based on mobile computing technology has been proposed. The four key components of the model are Quality of Content and Services, Organizational Readiness, Quality of System and Communication, Customer Mobile App. Manuscript profile

    • Open Access Article

      7 - A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques
      Abbas Mirzaei Shahram Zandian
      Iss. 40 , Vol. 10 , Autumn 2022
      Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomen More
      Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput. Manuscript profile

    • Open Access Article

      8 - Energy-Efficient User Pairing and Power Allocation for Granted Uplink-NOMA in UAV Communication Systems
      Seyed Hadi Mostafavi-Amjad Vahid Solouk Hashem Kalbkhani
      Iss. 40 , Vol. 10 , Autumn 2022
      With the rapid deployment of users and increasing demands for mobile data, communication networks with high capacity are needed more than ever. Furthermore, there are several challenges, such as providing efficient coverage and reducing power consumption. To tackle thes More
      With the rapid deployment of users and increasing demands for mobile data, communication networks with high capacity are needed more than ever. Furthermore, there are several challenges, such as providing efficient coverage and reducing power consumption. To tackle these challenges, using unmanned aerial vehicles (UAVs) would be a good choice. This paper proposes a scheme for uplink non-orthogonal multiple access (NOMA) in UAV communication systems in the presence of granted and grant-free users. At first, the service area users, including granted and grant-free users, are partitioned into some clusters. We propose that the hover location for each cluster is determined considering the weighted mean of users’ locations. We aim to allocate transmission power and form NOMA pairs to maximize the energy efficiency in each cluster subject to the constraints on spectral efficiency and total transmission power. To this end, the transmission powers of each possible pair are obtained, and then Hungarian matching is used to select the best pairs. Finally, finding the flight path of the UAV is modeled by the traveling salesman problem (TSP), and the genetic algorithm method obtains its solution. The results show that the increasing height of the UAV and density of users increases the spectral and energy efficiencies and reduces the outage probability. Also, considering the quality of service (QoS) of granted users for determining the UAV's hover location enhances the transmission's performance. Manuscript profile
    Most Viewed Articles

    • Open Access Article

      1 - Privacy Preserving Big Data Mining: Association Rule Hiding
      Golnar Assadat  Afzali shahriyar mohammadi
      Iss. 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
      Iss. 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 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
      Hamideh Sadat Cheraghchi Ali Zakerolhossieni
      Iss. 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

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

      5 - A Bio-Inspired Self-configuring Observer/ Controller for Organic Computing Systems
      Ali Tarihi Hassan Haghighi Fereidoon  Shams Aliee
      Iss. 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
      Iss. 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 - Low-Complexity Iterative Detection for Uplink Multiuser Large-Scale MIMO
      Mojtaba Amiri Mahmoud Ferdosizade Naeiny
      Iss. 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

    • Open Access Article

      8 - DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback
      Maral Kolahkaj Ali Harounabadi Alireza Nikravan shalmani Rahim Chinipardaz
      Iss. 24 , Vol. 6 , Autumn 2018
      By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropr More
      By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures. Manuscript profile

    • Open Access Article

      9 - Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm
      keyvan Borna Sepideh Palizdar
      Iss. 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

      10 - The Surfer Model with a Hybrid Approach to Ranking the Web Pages
      Javad Paksima Homa  Khajeh
      Iss. 15 , Vol. 4 , Summer 2016
      Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information retrieval, it is highly impor More
      Users who seek results pertaining to their queries are at the first place. To meet users’ needs, thousands of webpages must be ranked. This requires an efficient algorithm to place the relevant webpages at first ranks. Regarding information retrieval, it is highly important to design a ranking algorithm to provide the results pertaining to user’s query due to the great deal of information on the World Wide Web. In this paper, a ranking method is proposed with a hybrid approach, which considers the content and connections of pages. The proposed model is a smart surfer that passes or hops from the current page to one of the externally linked pages with respect to their content. A probability, which is obtained using the learning automata along with content and links to pages, is used to select a webpage to hop. For a transition to another page, the content of pages linked to it are used. As the surfer moves about the pages, the PageRank score of a page is recursively calculated. Two standard datasets named TD2003 and TD2004 were used to evaluate and investigate the proposed method. They are the subsets of dataset LETOR3. The results indicated the superior performance of the proposed approach over other methods introduced in this area. Manuscript profile
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