• 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

    The JIST has taken the decision that abroad authors pay model as an author-pays Open Access (OA) model, effective from the 1st March, 2021 volume, which comes into effect for all new submissions to the journal from this date.

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

     

     


    Latest published articles

    • Open Access Article

      1 - Phase Transition in the Social Impact Model of Opinion Formation in Log-Normal Networks
      Alireza Mansouri Fattaneh Taghiyareh
      Issue 33 , Volume 9 , Winter 2021
      People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marke Full Text
      People may change their opinions as a consequence of interacting with others. In the literature, this phenomenon is expressed as opinion formation and has a wide range of applications, including predicting social movements, predicting political voting results, and marketing. The interactions could be face-to-face or via online social networks. The social opinion phases are categorized into consensus, majority, and non-majority. In this research, we study phase transitions due to interactions between connected people with various noise levels using agent-based modeling and a computational social science approach. Two essential factors affect opinion formations: the opinion formation model and the network topology. We assumed the social impact model of opinion formation, a discrete binary opinion model, appropriate for both face-to-face and online interactions for opinion formation. For the network topology, scale-free networks have been widely used in many studies to model real social networks, while recent studies have revealed that most social networks fit log-normal distributions, which we considered in this study. Therefore, the main contribution of this study is to consider the log-normal distribution network topology in phase transitions in the social impact model of opinion formation. The results reveal that two parameters affect the phase transition: noise level and segregation. A non-majority phase happens in equilibrium in high enough noise level, regardless of the network topology, and a majority phase happens in equilibrium in lower noise levels. However, the segregation, which depends on the network topology, affects opinion groups’ population. A comparison with the scale-free network topology shows that in the scale-free network, which have a more segregated topology, resistance of segregated opinion groups against opinion change causes a slightly different phase transition at low noise levels. EI (External-Internal) index has been used to measure segregations, which is based on the difference between between-group (External) links and within-group (Internal) links. Manuscript Document

    • Open Access Article

      2 - Drone Detection by Neural Network Using GLCM and SURF Features
      Tanzia  Ahmed Tanvir  Rahman Bir  Ballav Roy Jia Uddin
      Issue 33 , Volume 9 , Winter 2021
      This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extrac Full Text
      This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error. Manuscript Document

    • Open Access Article

      3 - Confronting DDoS Attacks in Software-Defined Wireless Sensor Networks based on Evidence Theory
      Nazbanoo Farzaneh Reyhaneh Hoseini
      Issue 33 , Volume 9 , Winter 2021
      DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a secur Full Text
      DDoS attacks aim at making the authorized users unable to access the network resources. In the present paper, an evidence theory based security method has been proposed to confront DDoS attacks in software-defined wireless sensor networks. The security model, as a security unit, is placed on the control plane of the software-defined wireless sensor network aiming at detecting the suspicious traffic. The main purpose of this paper is detection of the DDoS attack using the central controller of the software-defined network and entropy approach as an effective light-weight and quick solution in the early stages of the detection and, also, Dempster-Shafer theory in order to do a more exact detection with longer time. Evaluation of the attacks including integration of data from the evidence obtained using Dempster-Shafer and entropy modules has been done with the purpose of increasing the rate of detection of the DDoS attack, maximizing the true positive, decreasing the false negative, and confronting the attack. The results of the paper show that providing a security unit on the control plane in a software-defined wireless sensor network is an efficient method for detecting and evaluating the probability of DDoS attacks and increasing the rate of detection of an attacker. Manuscript Document

    • Open Access Article

      4 - Denoising and Enhancement Speech Signal Using Wavelet
      meriane brahim
      Issue 33 , Volume 9 , Winter 2021
      Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techni Full Text
      Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems. Manuscript Document

    • Open Access Article

      5 - Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features
      Azar Mahmoodzadeh
      Issue 33 , Volume 9 , Winter 2021
      During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper pro Full Text
      During the past decades, recognition of human activities has attracted the attention of numerous researches due to its outstanding applications including smart houses, health-care and monitoring the private and public places. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the ‘scale-invariant features transform’ (SIFT), ‘histogram of oriented gradient’ (HOG) and ‘global invariant features transform’ (GIST) descriptors and classifies the activities by means of the deep belief network (DBN). First, in order to avoid ineffective features, a pre-processing course is performed on any image in the dataset. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of features, a small and distinguishing feature set is produced using the bag of words (BoW) technique. Finally, these reduced features are given to a deep belief network in order to recognize the human activities. Comparing the simulation results of the proposed approach with some other existing methods applied to the standard PASCAL VOC Challenge 2010 database with nine different activities demonstrates an improvement in the accuracy, precision and recall measures (reaching 96.39%, 85.77% and 86.72% respectively) for the approach of this work with respect to the other compared ones in the human activity recognition. Manuscript Document

    • Open Access Article

      6 - Energy Efficient Routing-Based Clustering Protocol Using Computational Intelligence Algorithms in Sensor-Based IoT
      mohammad sedighimanesh Hessam  Zandhessami Mahmood  Alborzi Mohammadsadegh  Khayyatian
      Issue 33 , Volume 9 , Winter 2021
      Background: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The p Full Text
      Background: The main limitation of wireless IoT sensor-based networks is their energy resource, which cannot be charged or replaced because, in most applications, these sensors are usually applied in places where they are not accessible or rechargeable. Objective: The present article's main objective is to assist in improving energy consumption in the sensor-based IoT network and thus increase the network’s lifetime. Cluster heads are used to send data to the base station. Methods: In the present paper, the type-1 fuzzy algorithm is employed to select cluster heads, and the type-2 fuzzy algorithm is used for routing between cluster heads to the base station. After selecting the cluster head using the type-1 fuzzy algorithm, the normal nodes become the members of the cluster heads and send their data to the cluster head, and then the cluster heads transfer the collected data to the main station through the path which has been determined by the type-2 fuzzy algorithm. Results: The proposed algorithm was implemented using MATLAB simulator and compared with LEACH, DEC, and DEEC protocols. The simulation results suggest that the proposed protocol among the mentioned algorithms increases the network’s lifetime in homogeneous and heterogeneous environments. Conclusion: Due to the energy limitation in sensor-based IoT networks and the impossibility of recharging the sensors in most applications, the use of computational intelligence techniques in the design and implementation of these algorithms considerably contributes to the reduction of energy consumption and ultimately the increase in network’s lifetime. Manuscript Document

    • Open Access Article

      7 - Secured Access Control in Security Information and Event Management Systems
      Leila Rikhtechi Vahid Rafeh Afshin Rezakhani
      Issue 33 , Volume 9 , Winter 2021
      Nowadays, Security Information and Event Management (SIEM) is very important in software. SIEM stores and monitors events in software and unauthorized access to logs can prompt different security threats such as information leakage and violation of confidentiality. In t Full Text
      Nowadays, Security Information and Event Management (SIEM) is very important in software. SIEM stores and monitors events in software and unauthorized access to logs can prompt different security threats such as information leakage and violation of confidentiality. In this paper, a novel method is suggested for secured and integrated access control in the SIEM. First, the key points where the SIEM accesses the information within the software is specified and integrated policies for access control are developed in them. Accordingly, the threats entered into the access control module embedded in this system are carefully detected. By applying the proposed method, it is possible to provide the secured and integrated access control module for SIEM as well as the security of the access control module significantly increases in these systems. The method is implemented in the three stages of the requirements analysis for the establishment of a secure SIEM system, secure architectural design, and secure coding. The access control module is designed to create a secured SIEM and the test tool module is designed for evaluating the access control module vulnerabilities. Also, to evaluate the proposed method, the dataset is considered with ten thousand records, and the accuracy is calculated. The outcomes show the accuracy of the proposed method is significantly improved. The results of this paper can be used for designing an integrated and secured access control system in SIEM systems. Manuscript Document
    Most Viewed Articles

    • Open Access Article

      1 - Instance Based Sparse Classifier Fusion for Speaker Verification
      Mohammad Hasheminejad Hassan Farsi
      Issue 15 , Volume 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 Full Text
      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 Document

    • Open Access Article

      2 - Privacy Preserving Big Data Mining: Association Rule Hiding
      Golnar Assadat  Afzali shahriyar mohammadi
      Issue 14 , Volume 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 Full Text
      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 Document

    • Open Access Article

      3 - Node Classification in Social Network by Distributed Learning Automata
      Ahmad Rahnama Zadeh meybodi meybodi Masoud Taheri Kadkhoda
      Issue 18 , Volume 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 Full Text
      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 Document

    • Open Access Article

      4 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
      Hamideh Sadat Cheraghchi Ali Zakerolhossieni
      Issue 14 , Volume 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 Full Text
      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 Document

    • Open Access Article

      5 - A Bio-Inspired Self-configuring Observer/ Controller for Organic Computing Systems
      Ali Tarihi haghighi haghighi feridon Shams
      Issue 15 , Volume 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 Full Text
      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 Document

    • 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 , Volume 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 Full Text
      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 Document

    • Open Access Article

      7 - Safe Use of the Internet of Things for Privacy Enhancing
      hojatallah hamidi
      Issue 15 , Volume 4 , Summer 2016
      New technologies and their uses have always had complex economic, social, cultural, and legal implications, with accompanying concerns about negative consequences. So it will probably be with the IoT and their use of data and attendant location privacy concerns. It must Full Text
      New technologies and their uses have always had complex economic, social, cultural, and legal implications, with accompanying concerns about negative consequences. So it will probably be with the IoT and their use of data and attendant location privacy concerns. It must be recognized that management and control of information privacy may not be sufficient according to traditional user and public preferences. Society may need to balance the benefits of increased capabilities and efficiencies of the IoT against a possibly inevitably increased visibility into everyday business processes and personal activities. Much as people have come to accept increased sharing of personal information on the Web in exchange for better shopping experiences and other advantages, they may be willing to accept increased prevalence and reduced privacy of information. Because information is a large component of IoT information, and concerns about its privacy are critical to widespread adoption and confidence, privacy issues must be effectively addressed. The purpose of this paper is which looks at five phases of information flow, involving sensing, identification, storage, processing, and sharing of this information in technical, social, and legal contexts, in the IoT and three areas of privacy controls that may be considered to manage those flows, will be helpful to practitioners and researchers when evaluating the issues involved as the technology advances. Manuscript Document

    • Open Access Article

      8 - The Surfer Model with a Hybrid Approach to Ranking the Web Pages
      Javad Paksima - -
      Issue 15 , Volume 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 Full Text
      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 Document

    • Open Access Article

      9 - Promote Mobile Banking Services by using National Smart Card Capabilities and NFC Technology
      Reza Vahedi Sayed Esmaeail Najafi Farhad Hosseinzadeh Lotfi
      Issue 15 , Volume 4 , Summer 2016
      By the mobile banking system and install an application on the mobile phone can be done without visiting the bank and at any hour of the day, get some banking operations such as account balance, transfer funds and pay bills did limited. The second password bank account Full Text
      By the mobile banking system and install an application on the mobile phone can be done without visiting the bank and at any hour of the day, get some banking operations such as account balance, transfer funds and pay bills did limited. The second password bank account card, the only security facility predicted for use mobile banking systems and financial transactions. That this alone cannot create reasonable security and the reason for greater protection and prevent the theft and misuse of citizens’ bank accounts is provide banking services by the service limits. That by using NFC (Near Field Communication) technology can identity and biometric information and Key pair stored on the smart card chip be exchanged with mobile phone and mobile banking system. And possibility of identification and authentication and also a digital signature created documents. And thus to enhance the security and promote mobile banking services. This research, the application and tool library studies and the opinion of seminary experts of information technology and electronic banking and analysis method Dematel is examined. And aim to investigate possibility Promote mobile banking services by using national smart card capabilities and NFC technology to overcome obstacles and risks that are mentioned above. Obtained Results, confirmed the hypothesis of the research and show that by implementing the so-called solutions in the banking system of Iran. Manuscript Document

    • 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 , Volume 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 Full Text
      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 Document
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