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
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Open Access Article
1 - Deep Transformer-based Representation for Text Chunking
Parsa Kavehzadeh Mohammad Mahdi Abdollah Pour Saeedeh MomtaziIssue 43 , Vol. 11 , Summer 2023Text chunking is one of the basic tasks in natural language processing. Most proposed models in recent years were employed on chunking and other sequence labeling tasks simultaneously and they were mostly based on Recurrent Neural Networks (RNN) and Conditional Random F MoreText chunking is one of the basic tasks in natural language processing. Most proposed models in recent years were employed on chunking and other sequence labeling tasks simultaneously and they were mostly based on Recurrent Neural Networks (RNN) and Conditional Random Field (CRF). In this article, we use state-of-the-art transformer-based models in combination with CRF, Long Short-Term Memory (LSTM)-CRF as well as a simple dense layer to study the impact of different pre-trained models on the overall performance in text chunking. To this aim, we evaluate BERT, RoBERTa, Funnel Transformer, XLM, XLM-RoBERTa, BART, and GPT2 as candidates of contextualized models. Our experiments exhibit that all transformer-based models except GPT2 achieved close and high scores on text chunking. Due to the unique unidirectional architecture of GPT2, it shows a relatively poor performance on text chunking in comparison to other bidirectional transformer-based architectures. Our experiments also revealed that adding a LSTM layer to transformer-based models does not significantly improve the results since LSTM does not add additional features to assist the model to achieve more information from the input compared to the deep contextualized models. Manuscript profile -
Open Access Article
2 - Deep Learning-based Educational User Profile and User Rating Recommendation System for E-Learning
Pradnya Vaibhav Kulkarni Sunil Rai Rajneeshkaur Sachdeo Rohini KaleIssue 43 , Vol. 11 , Summer 2023In the current era of online learning, the recommendation system for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or MoreIn the current era of online learning, the recommendation system for the eLearning process is quite important. Since the COVID-19 pandemic, eLearning has undergone a complete transformation. Existing eLearning Recommendation Systems worked on collaborative filtering or content-based filtering based on historical data, students’ previous grade, results, or user profiles. The eLearning system selected courses based on these parameters in a generalized manner rather than on a personalized basis. Personalized recommendations, information relevancy, choosing the proper course, and recommendation accuracy are some of the issues in eLearning recommendation systems. In this paper, existing conventional eLearning and course recommendation systems are studied in detail and compared with the proposed approach. We have used, the dataset of User Profile and User Rating for a recommendation of the course. K Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Nave Bayes, Linear Regression, Linear Discriminant Analysis, and Neural Network were among the Machine Learning techniques explored and deployed. The accuracy achieved for all these algorithms ranges from 0.81 to 0.97. The proposed algorithm uses a hybrid approach by combining collaborative filtering and deep learning. We have improved accuracy to 0.98 which indicate that the proposed model can provide personalized and accurate eLearning recommendation for the individual user. Manuscript profile -
Open Access Article
3 - Implementation of Machine Learning Algorithms for Customer Churn Prediction
Manal Loukili Fayçal Messaoudi Raouya El YoubiIssue 43 , Vol. 11 , Summer 2023Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, w MoreChurn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them. Manuscript profile -
Open Access Article
4 - Recognition of Facial and Vocal Emotional Expressions by SOAR Model
Matin Ramzani Shahrestani Sara Motamed Mohammadreza YamaghaniIssue 43 , Vol. 11 , Summer 2023Todays, facial and vocal emotional expression recognition is considered one of the most important ways of human communication and responding to the ambient and the attractive fields of machine vision. This application can be used in different cases, including emotion an MoreTodays, facial and vocal emotional expression recognition is considered one of the most important ways of human communication and responding to the ambient and the attractive fields of machine vision. This application can be used in different cases, including emotion analysis. This article uses six basic emotional expressions (anger, disgust, fear, happiness, sadness, and surprise), and its main goal is to present a new method in cognitive science, based on the functioning of the human brain system. The stages of the proposed model include four main parts: pre-processing, feature extraction, feature selection, and classification. In the pre-processing stage, facial images and verbal signals are extracted from videos taken from the enterface’05 dataset, noise removal and resizing is performed on them. In the feature extraction stage, PCA is applied to the images, and the 3D-CNN network is used to find the best features of the images. Moreover, MFCC is applied to emotional verbal signals, and the CNN Network will also be applied to find the best features. Then, fusion is performed on the resulted features and finally Soar classification will be applied to the fused features, to calculate the recognition rate of emotional expression based on face and speech. This model will be compared with competing models in order to examine the performance of the proposed model. The highest rate of recognition based on audio-image was related to the emotional expression of disgust with a rate of 88.1%, and the lowest rate of recognition was related to fear with a rate of 73.8%. Manuscript profile -
Open Access Article
5 - Long-Term Software Fault Prediction Model with Linear Regression and Data Transformation
Momotaz Begum Jahid Hasan Rony Md. Rashedul Islam Jia UddinIssue 43 , Vol. 11 , Summer 2023The validation performance is obligatory to ensure the software reliability by determining the characteristics of an implemented software system. To ensure the reliability of software, not only detecting and solving occurred faults but also predicting the future fault i MoreThe validation performance is obligatory to ensure the software reliability by determining the characteristics of an implemented software system. To ensure the reliability of software, not only detecting and solving occurred faults but also predicting the future fault is required. It is performed before any actual testing phase initiates. As a result, various works on software fault prediction have been done. In this paper presents, we present a software fault prediction model where different data transformation methods are applied with Poisson fault count data. For data pre-processing from Poisson data to Gaussian data, Box-Cox power transformation (Box-Cox_T), Yeo-Johnson power transformation (Yeo-Johnson_T), and Anscombe transformation (Anscombe_T) are used here. And then, to predict long-term software fault prediction, linear regression is applied. Linear regression shows the linear relationship between the dependent and independent variable correspondingly relative error and testing days. For synthesis analysis, three real software fault count datasets are used, where we compare the proposed approach with Naïve gauss, exponential smoothing time series forecasting model, and conventional method software reliability growth models (SRGMs) in terms of data transformation (With_T) and non-data transformation (Non_T). Our datasets contain days and cumulative software faults represented in (62, 133), (181, 225), and (114, 189) formats, respectively. Box-Cox power transformation with linear regression (L_Box-Cox_T) method, has outperformed all other methods with regard to average relative error from the short to long term. Manuscript profile -
Open Access Article
6 - A survey on NFC Payment: Applications, Research Challenges, and Future Directions
Mehdi Sattarivand Shahram Babaie Amir Masoud RahmaniIssue 43 , Vol. 11 , Summer 2023Near Field Communication (NFC), as a short-range wireless connectivity technology, makes it easier for electronic devices to stay in touch. This technology, due to its advantages such as secure access, compatibility, and ease of use, can be utilized in multiple applicat MoreNear Field Communication (NFC), as a short-range wireless connectivity technology, makes it easier for electronic devices to stay in touch. This technology, due to its advantages such as secure access, compatibility, and ease of use, can be utilized in multiple applications in various domains such as banking, file transferring reservations, booking tickets, redeeming, entry/exit passes, and payment. In this survey paper, various aspects of this technology, including operating modes, their protocol stacks, and standard message format are investigated. Moreover, future direction of NFC in terms of design, improvement, and user-friendliness is presented for further research. In addition, due to the disadvantages of banknote-based payment methods such as the high temptation to steal and the need for a safe, mobile payments, which include mobile wallets and mobile money transfers, are explored as a new alternative to these methods. In addition, the traditional payment methods and their limitations are surveyed along with NFC payment as a prominent application of this technology. Furthermore, security threats of NFC payment along with future research directions for NFC payment and its challenges, including protocols and standards, and NFC payment security requirements are addressed in this paper. It is hoped that effective policies for NFC payment development will be provided by addressing the important challenges and formulating appropriate standards. Manuscript profile -
Open Access Article
7 - Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction
Simin RajaeeNejad Farahnaz MohannaIssue 43 , Vol. 11 , Summer 2023One of the most important databases in the e-commerce is tile and ceramic database, for which no specific retrieval method has been provided so far. In this paper, a method is proposed for the content-based retrieval of digital images of tiles and ceramics databases. Fi MoreOne of the most important databases in the e-commerce is tile and ceramic database, for which no specific retrieval method has been provided so far. In this paper, a method is proposed for the content-based retrieval of digital images of tiles and ceramics databases. First, a database is created by photographing different tiles and ceramics on the market from different angles and directions, including 520 images. Then a query image and the database images are divided into nine equal sub-images and all are grouped based on their sub-images. Next, the selected color and texture features are extracted from the sub-images of the database images and query image, so, each image has a feature vector. The selected features are the minimum features that are required to reduce the amount of computations and information stored, as well as speed up the retrieval. Average precision is calculated for the similarity measure. Finally, comparing the query feature vector with the feature vectors of all database images leads to retrieval. According to the retrieving results by the proposed method, its accuracy and speed are improved by 16.55% and 23.88%, respectively, compared to the most similar methods. Manuscript profile -
Open Access Article
8 - Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems
MOUSUMI HAQUE Tetsuya ShimamuraIssue 43 , Vol. 11 , Summer 2023Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectr MoreCognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments. Manuscript profile -
Open Access Article
9 - Trip Timing Algorithm for GTFS Data with Redis Model to Improve the Performance
Mustafa Alzaidi Aniko VagnerIssue 43 , Vol. 11 , Summer 2023Accessing public transport plays an essential role in the daily life productivity of people in urban regions. Therefore, it is necessary to represent the spatiotemporal diversity of transit services to evaluate public transit accessibility appropriately. That can be acc MoreAccessing public transport plays an essential role in the daily life productivity of people in urban regions. Therefore, it is necessary to represent the spatiotemporal diversity of transit services to evaluate public transit accessibility appropriately. That can be accomplished by determining the shortest path or shortest travel time trip plan. Many applications like ArcGIS provide tools to estimate the trip time using GTFS data. They can perform well in finding travel time. Still, they can be computationally inefficient and impractical with increasing the data dimensions like searching all day time or in case of huge data. Some research proposed recently provides more computationally efficient algorithms to solve the problem. This paper presents a new algorithm to find the timing information for a trip plan between two start and destination points. Also, we introduce RMH (Range Mapping Hash) as a new approach using Redis NoSQL to find and calculate the accessibility of a trip plan with fixed time complexity of O(2) regardless of the city size (GTFS size). We experimented with the performance of this approach and compared it with the traditional run-time algorithm using GTFS data of Debrecen and Budapest. This Redis model can be applied to similar problems where input can be divided into ranges with the same output. Manuscript profile
Most Viewed Articles
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Open Access Article
1 - Privacy Preserving Big Data Mining: Association Rule Hiding
Golnar Assadat Afzali shahriyar mohammadiIssue 14 , Vol. 4 , Spring 2016Data 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 MoreData 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 FarsiIssue 15 , Vol. 4 , Summer 2016This 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 MoreThis 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 ZakerolhossieniIssue 14 , Vol. 4 , Spring 2016The 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 MoreThe 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 KadkhodaIssue 18 , Vol. 5 , Spring 2017The 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 MoreThe 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 AlieeIssue 15 , Vol. 4 , Summer 2016The 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 MoreThe 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 AziziIssue 21 , Vol. 6 , Winter 2018Information 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 MoreInformation 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 PalizdarIssue 16 , Vol. 4 , Autumn 2016Predicting 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 MorePredicting 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 NaeinyIssue 29 , Vol. 8 , Winter 2020In 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 MoreIn 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 KhajehIssue 15 , Vol. 4 , Summer 2016Users 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 MoreUsers 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 ChinipardazIssue 24 , Vol. 6 , Autumn 2018By 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 MoreBy 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
