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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 - Broadband Low-Cost Reflectarray Based on a New Phase Synthesis Technique and a Class of Cross Bow-tie Cells
      Mahmood Rafaei-Booket Mahdieh Bozorgi Seyed Mostafa Mousavi
      Issue 42 , Vol. 11 , Spring 2023
      In this paper, a class of Bow-tie unit cell on FR4 substrate is designed and investigated to be used in implementing a single-layer broadband Reflectarray Antenna (RA). In the analyzing step, two different parameters (length and angle) of the grounded Cross Bow-Tie (CBT More
      In this paper, a class of Bow-tie unit cell on FR4 substrate is designed and investigated to be used in implementing a single-layer broadband Reflectarray Antenna (RA). In the analyzing step, two different parameters (length and angle) of the grounded Cross Bow-Tie (CBT) are varied to obtain the phase diagrams. Various degrees of freedom in the CBT structure are very helpful in designing a broadband RA. In the antenna design procedure, an efficient phase synthesis technique is applied to minimize the adverse effects of frequency dispersion causing by the differential space phase delay at different frequencies. This technique optimizes the metallic CBTs arrangement on the aperture of RA, and reduces the dependency of RA design to the CBT’s phase variation. Consequently, combination of the CBT’s phase behavior and the phase synthesis technique leads to designing a broadband RA with a good frequency response. In addition to, the Side Lobe Level (SLL) of the resultant RA is reduced remarkably. For validation of the obtained numerical results, an RA is designed and fabricated in 8.7~12.3GHz frequency bandwidth. The measurements show 27.03 dB as a maximum gain value at 10.2 GHz with a 1.5dB gain bandwidth of 34%. It is also shown that the implemented RA exhibits a reduced SLL (<-18dBi) within its operating bandwidth. Manuscript profile

    • Open Access Article

      2 - Comparative Study of 5G Signal Attenuation Estimation Models
      Md Anoarul Islam Manabendra Maiti Judhajit Sanyal Quazi Md Alfred
      Issue 42 , Vol. 11 , Spring 2023
      Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent y More
      Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios. Manuscript profile

    • Open Access Article

      3 - Hoax Identification of Indonesian Tweeters Using Ensemble Classifier
      Gus Nanang Syaifuddiin Rizal Arifin Desriyanti Desriyanti Ghulam Asrofi Buntoro Zulkham Umar Rosyidin Ridwan Yudha Pratama Ali Selamat
      Issue 42 , Vol. 11 , Spring 2023
      Fake information or better known as hoaxes are often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but there are also those who use it to spread hate speech and false information. Hoaxes are very danger More
      Fake information or better known as hoaxes are often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but there are also those who use it to spread hate speech and false information. Hoaxes are very dangerous in social life, especially in countries with large populations and ethnically diverse cultures, such as Indonesia. To help prevent the spread of these hoaxes, we built a model to identify false information in Indonesian using an ensemble classifier which is a combination of the n-gram method, term frequency-inverse document frequency, and passive-aggressive classifier method. In this study, the evaluation process was carried out using 5000 samples from Twitter social media accounts. The testing process is carried out using four schemes by dividing the dataset into training data and test data based on the ratios of 90:10, 80:20, 70:30, and 60:40. The inspection results show that our software can accurately detect hoaxes at a rate of 91.8%. We also found an increase in the accuracy and precision of hoax detection testing using the proposed method compared to several previous studies. The results show that our proposed method can be developed and used in detecting hoaxes in Indonesian on various social media platforms. Manuscript profile

    • Open Access Article

      4 - Improving Image Processing in the Minds of People with Visual Agnosia Using Fuzzy Cognitive Map
      elham askari Sara Motamed
      Issue 42 , Vol. 11 , Spring 2023
      The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. P More
      The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods. Manuscript profile

    • Open Access Article

      5 - Performance Analysis and Activity Deviation Discovery in Event Log Using Process Mining Tool for Hospital System
      Shanmuga Sundari M Rudra Kalyan Nayak Vijaya Chandra Jadala Sai Kiran Pasupuleti
      Issue 42 , Vol. 11 , Spring 2023
      Nowadays, all service and manufacturing industries are resilient in the current environment and work towards efficient and better outcome. Data mining is data centric and requires large volume of data for evaluating the pattern and to feed the model for training. If the More
      Nowadays, all service and manufacturing industries are resilient in the current environment and work towards efficient and better outcome. Data mining is data centric and requires large volume of data for evaluating the pattern and to feed the model for training. If the data is improper and is not obtained from proper sources and mislead the analysis. To solve this problem, we train the process where the dataset is split into test and train dataset with particular ratio. In order to streamline the process, process mining technique is used that will identify the traces of activities and help data mining to lead more efficient outcome. Health care industry is the most responsible domain. Therefore, process mining is the effective way to incorporate performance analysis and finding the deviation in hospital system. In fact it reduced the time to analyze data. In this research, we collected the activity data from the hospital and applied the process mining algorithms like alpha miner and fuzzy miner using ProM tool. Process mining has been done using the event log generated from the hospital data which will undergo through process mining algorithms. Using the process mining, the conformance checking in the event log and performance analysis is done and pattern of accuracy is displayed. Finally, we showed the deviation flow and corrected the flow of the process using process mining algorithms. This research results showed the deviation occurred in the flow and performance of process in the hospital as well. Manuscript profile

    • Open Access Article

      6 - Cache Point Selection and Transmissions Reduction in WSN networks using LSTM
      Malihe  Bahekmat Mohammad Hossein  Yaghmaee
      Issue 42 , Vol. 11 , Spring 2023
      Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance More
      Reliability of data transmission in wireless sensor networks (WSN) is very important in the case of high lost packet rate due to link problems or buffer congestion. In this regard, mechanisms such as middle cache points and congestion control can improve the performance of the reliability of transmission protocols when the packet is lost. On the other hand, the issue of energy consumption in this type of networks has become an important parameter in their reliability. In this paper, considering the energy constraints in the sensor nodes and the direct relationship between energy consumption and the number of transmissions made by the nodes, the system tries to reduce the number of transmissions needed to send a packet from source to destination as much as possible by optimal selection of the cache points and packet caching. In order to select the best cache points, the information extracted from the network behavior analysis by deep learning algorithm has been used. In the training phase, long-short term memory (LSTM) capabilities as an example of recurrent neural network (RNN) deep learning networks to learn network conditions. The results show that the proposed method works better in examining the evaluation criteria of transmission costs, end-to-end delays, cache use and throughput. Manuscript profile

    • Open Access Article

      7 - A novel expanded version of Shor’s Algorithm and a real-time experiment using IBM Q-Experience Platform
      Afshin Rezakhani Sepehr Goodarzi Mahdi Maleki
      Issue 42 , Vol. 11 , Spring 2023
      Data are stored on the memory of the classical computer in small units of classical bits, which could be either 0 or 1.On a Quantum Computer, The Quantum States of each Quantum Bit (Qbit), would be every possible number between 0 and 1, including themselves. By placing More
      Data are stored on the memory of the classical computer in small units of classical bits, which could be either 0 or 1.On a Quantum Computer, The Quantum States of each Quantum Bit (Qbit), would be every possible number between 0 and 1, including themselves. By placing the photons on a special state, which is a spot located at the middle of the two-dimensional space vectors (█(1@0)) and (█(1@1)) on the Unit Circle, which is called Superposition and we can take advantage of properties of this state when we place lots of vectors of N-dimensional spaces in superposition and we can do a parallelization and factorization for getting significant speedup. In fact, in Quantum Computing we are taking advantage of Quantum Dynamic Principles to process the data, which Classical Computers lack on, by considering the limitations of logical concepts behind them. Through this paper, we expand a quantum algorithm for the number of n Qbits in a new way and by implementing circuits using IBM-Q Experience, we are going to have some practical results, which are more obvious to be demonstrable. By expanding the Quantum Algorithms and using Linear Algebra, we can manage to achieve the goals at a higher level, the ones that Classical Computers are unable to perform, as machine learning problems with complicated models and by expanding the subject we can mention majors in different sciences like Chemistry (predicting the Structure of proteins with higher percentage accuracy in less period) and astronomy. Manuscript profile

    • Open Access Article

      8 - A Novel Elite-Oriented Meta-Heuristic Algorithm: Qashqai Optimization Algorithm (QOA)
      Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi
      Issue 42 , Vol. 11 , Spring 2023
      Optimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and th More
      Optimization problems are becoming more complicated, and their resource requirements are rising. Real-life optimization problems are often NP-hard and time or memory consuming. Nature has always been an excellent pattern for humans to pull out the best mechanisms and the best engineering to solve their problems. The concept of optimization seen in several natural processes, such as species evolution, swarm intelligence, social group behavior, the immune system, mating strategies, reproduction and foraging, and animals’ cooperative hunting behavior. This paper proposes a new Meta-Heuristic algorithm for solving NP-hard nonlinear optimization problems inspired by the intelligence, socially, and collaborative behavior of the Qashqai nomad’s migration who have adjusted for many years. In the design of this algorithm uses population-based features, experts’ opinions, and more to improve its performance in achieving the optimal global solution. The performance of this algorithm tested using the well-known optimization test functions and factory facility layout problems. It found that in many cases, the performance of the proposed algorithm was better than other known meta-heuristic algorithms in terms of convergence speed and quality of solutions. The name of this algorithm chooses in honor of the Qashqai nomads, the famous tribes of southwest Iran, the Qashqai algorithm. Manuscript profile

    • Open Access Article

      9 - Analyzing Impact of Covid-19 Pandemic outbreak on Economy using Machine Learning Classifiers and Regressors
      Md. Nahid  Hasan Tanvir Ahmed Md  Ashik Md. Jahid  Hasan Tahaziba  Azmin Jia Uddin
      Issue 42 , Vol. 11 , Spring 2023
      The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Many studies have shown that t More
      The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Many studies have shown that the spread of the disease has experienced a significant change in the economy. Consequently, in this paper, we use machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN), Random Forest (RF) and Long Short-Term Memory (LSTM), and Random Forest (RF) Regressors to construct an early warning model for economic crisis detection. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of USA over 239 days (1 January 2020 to 12 May 2020). As a performance metric, we have used recession and prediction accuracy. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively). 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 - 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

      4 - 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

      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 - 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

      8 - 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

    • Open Access Article

      9 - The Surfer Model with a Hybrid Approach to Ranking the Web Pages
      Javad Paksima Homa  Khajeh
      Issue 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

    • Open Access Article

      10 - DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback
      Maral Kolahkaj Ali Harounabadi Alireza Nikravan shalmani Rahim Chinipardaz
      Issue 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
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    Iranian Academic Center for Education,Culture and Research
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    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 (Tarbiat Modaress University) Shohreh Kasaei (Sharif University of Technology) Zabih Ghasemlooy ( University of Northumbria )
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    Online ISSN:2345-2773

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