Pattern Analysis and Intelligent Systems
Zahra narimani; Farhad Soleimanian Gharehchopogh
Articles in Press, Accepted Manuscript, Available Online from 07 July 2022
Abstract
One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes ...
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One of the most critical issues in studying complex networks is detecting communities widely used in sociology, social media (such as Instagram, Twitter, or email networks), biology, physics, data networks, and information technology. Graphs usually implement complex network modelling. In a graph, nodes represent objects, and edges represent connections between objects. Communities are groups of nodes that have many internal connections and few intergroup connections. Although in social network research, the detection of communities has attracted much attention, most of them face functional weaknesses because the structure of the community is not clear or the characteristics of nodes in a community are not the same. Besides, many existing algorithms have complex and costly calculations. In this paper, a model based on Harris Hawk Optimization (HHO) algorithm and Opposition-based Learning (OBL) is proposed for Community Detection (CD). The proposed model uses an OBL to balance exploration and exploitation. The balance between exploration and exploitation is effective in achieving optimal solutions. The evaluation of the proposed model is performed on four different datasets based on modularity criteria and NMI (Normalized Mutual Information). The results show that the proposed model has higher modularity, NMI, and generalizability compared to other methods.
Pattern Analysis and Intelligent Systems
Farhad Soleimanian Gharehchopogh; Berivan Rostampnah
Volume 7, Issue 3 , August 2021, , Pages 177-186
Abstract
Abstract— Clustering is one of the most popular techniques in unsupervised learning in which data is divided into different groups without any prior knowledge, and for this reason, clustering is used in various applications today. One of the most popular algorithms in the field of clustering is ...
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Abstract— Clustering is one of the most popular techniques in unsupervised learning in which data is divided into different groups without any prior knowledge, and for this reason, clustering is used in various applications today. One of the most popular algorithms in the field of clustering is the k-means clustering algorithm. The most critical weakness of k-means clustering is that it is sensitive to initial values for parameterization and may stop at local minima. Despite its many advantages, such as high speed and ease of implementation due to its dependence on the initial parameters, this algorithm is in the optimal local configuration and does not always produce the optimal answer for clustering. Therefore, this paper proposes a new model using the Bald Eagle Search (BES) Algorithm with the Sine Cosine Algorithm (SCA) for clustering. The evaluation of the proposed model is based on the number of iterations, convergence, number of generations, and execution time on 8 UCI datasets. The proposed model is compared with Flower Pollination Algorithm (FPA), Crow Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Sine-Cosine Algorithm (SCA). The results show that the proposed model has a better fit compared to other algorithms. According to the analysis, it can be claimed that the proposed model is about 10.26% superior to other algorithms and also has an extraordinary advantage over k-means.
Pattern Analysis and Intelligent Systems
reza molaee fard
Volume 7, Issue 2 , May 2021, , Pages 137-146
Abstract
Recommending systems are systems that, by taking limited information from the user and features such as what the user has searched for in the past and what product they have rated, can correctly identify the user and the desired items Offer the user. The user's desired items are suggested to him through ...
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Recommending systems are systems that, by taking limited information from the user and features such as what the user has searched for in the past and what product they have rated, can correctly identify the user and the desired items Offer the user. The user's desired items are suggested to him through the user profile. In this research, a new method is presented to recommend the user's interests in the form of the user's personalized profile. The way to do this is to use other users' searched information in the form of a database to recommend to new users. The procedure is that we first collect a log file from the items searched by users, then we pre-process this log file to remove the data from the raw state and clean it. Then, using data weighting and using the score function, we extract the most searched items of users in the past and provide them to the user in the form of a recommendation system based on participatory filtering. Finally, we use our data using an algorithm. We optimize the cuckoo that this information can be of interest to the user. The results of this study showed 99% accuracy and 97% frequency, which can to a large extent correctly predict the user's favorite items and pages and start with the problem that is the problem of most recommender systems To confront.
Pattern Analysis and Intelligent Systems
Razieh Asgarnezhad; Karrar Ali Mohsin Alhameedawi
Volume 7, Issue 2 , May 2021, , Pages 147-156
Abstract
Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, ...
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Due to the importance of automatic identification of brain conditions, many researchers concentrate on Epilepsy disorder to aim to the detecting of eye states and classification systems. Eye state recognition has a vital role in biomedical informatics such as controlling smart home devices, driving detection, etc. This issue is known as electroencephalogram signals. There are many works in this context in which traditional techniques and manually extracted features are used. The extraction of effective features and the selection of proper classifiers are challenging issues. In this study, a classification system named PEML-E was proposed in which a different pre-processing stage is used. The ensemble methods in the classification stage are compared to the base classifiers and the most important works in this context. To evaluate, a freely available public EEG eye state dataset from UCI is applied. The highest accuracy, precision, recall, F1, specificity, and sensitivity are obtained 95.88, 95.39, 96.25, 96.18, 96.25, and 95.44%, respectively.
Pattern Analysis and Intelligent Systems
Somayeh Lotfi; Mohammad Ghasemzadeh; Mehran Mohsenzadeh; Mitra Mirzarezaee
Volume 7, Issue 1 , February 2021, , Pages 55-66
Abstract
The decision tree is one of the popular methods for learning and reasoning through recursive partitioning of data space. To choose the best attribute in the case on numerical features, partitioning criteria should be calculated for individual values or the value range of each attribute should be divided ...
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The decision tree is one of the popular methods for learning and reasoning through recursive partitioning of data space. To choose the best attribute in the case on numerical features, partitioning criteria should be calculated for individual values or the value range of each attribute should be divided into two or more intervals using a set of cut points. In partitioning range of attribute, the fuzzy partitioning can be used to reduce the noise sensitivity of data and to increase the stability of decision trees. Since the tree-building algorithms need to keep in main memory the whole training dataset, they have memory restrictions. In this paper, we present an algorithm that builds the fuzzy decision tree on the large dataset. In order to avoid storing the entire training dataset in main memory and overcome the memory limitation, the algorithm builds DTs in an incremental way. In the discretization stage, a fuzzy partition was generated on each continuous attribute based on fuzzy entropy. Then, in order to select the best feature for branches, two criteria, including fuzzy information gain and occurrence matrix are used. Besides, real datasets are used to evaluate the behavior of the algorithm in terms of classification accuracy, decision tree complexity, and execution time as well. The results show that proposed algorithm without a need to store the entire dataset in memory and reduce the complexity of the tree is able to overcome the memory limitation and making balance between accuracy and complexity .
Pattern Analysis and Intelligent Systems
Mehrnaz Mirhasani; Reza Ravanmehr
Volume 6, Issue 4 , November 2020, , Pages 251-264
Abstract
The movie recommendation systems are always faced with the new movie cold start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie cold start problem. In ...
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The movie recommendation systems are always faced with the new movie cold start problem. Nowadays, social media platform such as Twitter is considered as a rich source of information in various domains, like movies, motivated us to exploit Twitter's content to tackle the movie cold start problem. In this study, we propose a hybrid movie recommendation method utilizing microblogs, movie features, and sentiment lexicon to reduce the effect of data sparsity. For this purpose, first, the movie features are extracted from the Internet Movie Database (IMDB), and the average IMDB score is calculated during the 7-days opening of the movie. Second, the related tweets of the movie and the cast are retrieved by the Twitter API. Third, the polarity of tweets and the public’s feeling towards the target movie is extracted using sentiment lexicon analysis. Finally, the results of the three previous steps are integrated, and the prediction is obtained. Our results are compared with the sales volume of the target movie in 7-days opening, which is available in the Mojo Box office. In addition to the real-world benchmarking, we performed extensive experiments to demonstrate the accuracy and effectiveness of our proposed approach in comparison with the other state-of-the-art methods.
Pattern Analysis and Intelligent Systems
Farhad Soleimanian Gharehchopogh; Sevda Haggi
Volume 6, Issue 2 , May 2020, , Pages 79-90
Abstract
The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques ...
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The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the clustering technique is to find the centrality of the clusters and the distance between the samples of each cluster and the center of the cluster. The problem with clustering techniques, such as k-modes, is the failure to precisely detect the centrality of clusters. Therefore, in this paper, Elephant Herding Optimization (EHO) Algorithm and k-modes are used for clustering and detecting the crime by means of detecting the similarity of crime with each other. The proposed model consists of two basic steps: First, the cluster centrality should be detected for optimized clustering; in this regard, the EHO Algorithm is used. Second, k-modes are used to find the clusters of crimes with close similarity criteria based on distance. The proposed model was evaluated on the Community and Crime dataset consisting of 1994 samples with 128 characteristics. The results showed that purity accuracy of the proposed model is equal to 91.45% for 400 replicates.
Pattern Analysis and Intelligent Systems
Esther N Khakata; Vincent Oteke Omwenga; Simon S. Msanjila
Volume 6, Issue 2 , May 2020, , Pages 107-118
Abstract
This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These ...
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This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found within the students learning environment. To obtain the learning styles, a data mining technique was used and this explicitly involved the use of pattern analysis in order to identify the underlying learning styles in the data collected from the learners. This paper highlights the five major learning styles that describe the patterns extracted from the collected data. Therefore, considering the changed learning ecosystem, it is clear that prediction of student learning styles can be done when the various factor inputs within the student environment are brought together and analyzed to focus on learning within internet-mediated environments.
Pattern Analysis and Intelligent Systems
Samira Amjad; Farhad Soleimanian Gharehchopogh
Volume 5, Issue 3 , August 2019, , Pages 181-194
Abstract
Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users ...
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Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the field of hardware and software. Security is the top priority to prevent a cyber-attack that users should initially be detecting the type of attacks because virtual environments are not monitored. Today, email is the foundation of many internet attacks that have happened. The Hackers and penetrators are using email spam as a way to penetrate into computer systems junk. Email can contain viruses, malware, and malicious code. Therefore, the type of email should be detected by security tools and avoid opening suspicious emails. In this paper, a new model has been proposed based on the hybrid of Scatter Searching Algorithm (SSA) and K-Nearest Neighbors (KNN) to email spam detection. The Results of proposed model on Spambase dataset shows which our model has more accuracy with Feature Selection (FS) and in the best case, its percentage of accuracy is equal to 94.54% with 500 iterations and 57 features. Also, the comparison shows that the proposed model has better accuracy compared to the evolutionary algorithm (data mining and decision detection such as C4.5).
Pattern Analysis and Intelligent Systems
Milad Keshtkar Langaroudi; Mohammadreza Yamaghani
Volume 5, Issue 1 , February 2019, , Pages 27-36
Abstract
In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available ...
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In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system.
Pattern Analysis and Intelligent Systems
Md Golam Sarowar; Azim Khan; Maruf Hasan Shakil; Mohammad Arafat Ullah
Volume 4, Issue 4 , November 2018, , Pages 237-246
Abstract
this research explores the manipulation of biomedical big data and diseases detection using automated computing mechanisms. As efficient and cost effective way to discover disease and drug is important for a society so computer aided automated system is a must. This paper aims to understand the importance ...
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this research explores the manipulation of biomedical big data and diseases detection using automated computing mechanisms. As efficient and cost effective way to discover disease and drug is important for a society so computer aided automated system is a must. This paper aims to understand the importance of computer aided automated system among the people. The analysis result from collected data contributes to finding an effective result that people have enough understanding and much better knowledge about big data and computer aided automated system. moreover, perspective and trustworthiness of people regarding recent advancement of computer aided technologies in biomedical science have been demonstrated in this research. however, appearance of big data in the field of medical science and manipulation of those data have been concentrated on this research. Finally suggestions have been developed for further research related to computer technology in manipulation of big data, disease detection and drug discovery.
Pattern Analysis and Intelligent Systems
Saman Khalandi; Farhad Soleimanian Gharehchopogh
Volume 4, Issue 3 , August 2018, , Pages 167-184
Abstract
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features ...
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With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, forming feature vectors, and final classification. In the presented model, the authors formed a feature vector for each document by means of weighting features use for IWO. Then, documents are trained with NB classifier; then using the test, similar documents are classified together. FS do increase accuracy and decrease the calculation time. IWO-NB was performed on the datasets Reuters-21578, WebKb, and Cade 12. In order to demonstrate the superiority of the proposed model in the FS, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been used as comparison models. Results show that in FS the proposed model has a higher accuracy than NB and other models. In addition, comparing the proposed model with and without FS suggests that error rate has decreased.
Pattern Analysis and Intelligent Systems
Marzieh Faridi Masouleh
Volume 4, Issue 1 , February 2018, , Pages 13-20
Abstract
Business intelligent (BI) technologies have been adopted by different types of organizations. The banking sector is among the service industry that has been largely influenced by technology currently. This has been manifested in the way the operations of banking have evolved from the pure exchange of ...
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Business intelligent (BI) technologies have been adopted by different types of organizations. The banking sector is among the service industry that has been largely influenced by technology currently. This has been manifested in the way the operations of banking have evolved from the pure exchange of cheques, cash, as well as other negotiable platforms to the application of IT (information Technology) to transact business in this service industry. The study conducted on impacts of business technologies adoption among Iranian Banks revealed that the adoption has made banking industry in Iran to be competitive and have improved operational efficiencies. However, in terms of Risk reduction, BI technologies if not used appropriately it can lead to the downfall of these banks. BI solutions allow banking industry in Iran to use the available data to exploit the competitive advantage as well as have an improved understanding of the demands and needs of customers by facilitating effective communication.
Pattern Analysis and Intelligent Systems
Vahid Golmah; Mina Tashakori
Volume 3, Issue 3 , August 2017, , Pages 125-134
Abstract
Telecommunication companies have received a great deal of research attention, which have many advantages such as low cost, higher qualification, simple installation and maintenance, and high reliability. However, the using of technical maintenance approaches in Telecommunication companies could improve ...
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Telecommunication companies have received a great deal of research attention, which have many advantages such as low cost, higher qualification, simple installation and maintenance, and high reliability. However, the using of technical maintenance approaches in Telecommunication companies could improve system reliability and users' satisfaction from Asymmetric digital subscriber line (ADSL) services. In ADSL systems, there are many variables giving some noise for classification and there are many fault patterns with overlapping data. Therefore, this paper proposes a multilayer perceptron (MLP) classifier integrated with Self Organization Map (SOM) models for fault detection and diagnosis (FDD) of occurred ADSL systems. The interest of this paper is to improve the performance of single MLP by dividing the fault pattern space into a few smaller sub-spaces using SOM clustering technique and triggering the right local classifier by designing a supervisor agent. The performances of this method are evaluated on the fault data of Iranian Telecommunication Company which develop ADSL services and then the proposed algorithm is also compared against single MLP. Finally, the results obtained by this algorithm are analyzed to increase user's satisfaction with reducing occurred faults for them with predicting before they face it.
Pattern Analysis and Intelligent Systems
Zahra Shahpar; Vahid Khatibi; Asma Tanavar; Rahil Sarikhani
Volume 2, Issue 4 , November 2016, , Pages 31-38
Abstract
In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become ...
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In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.
Pattern Analysis and Intelligent Systems
Farzaneh Famoori; Vahid Khatibi bardsiri; Shima Javadi Moghadam; Fakhrosadat Fanian
Volume 2, Issue 3 , August 2016, , Pages 15-26
Abstract
One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of ...
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One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of clustering approach for estimating required effort in software projects. The effort estimation is carried out through SWR (StepWise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification And Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, clustering of projects is applied to the estimation process. As indicated by the results of this study, the combination of clustering method and algorithmic estimation techniques can improve the accuracy of estimates.
Pattern Analysis and Intelligent Systems
Hamid Parvin; Hosein Alizadeh; Mohsen Moshki
Volume 2, Issue 2 , May 2016, , Pages 1-10
Abstract
Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in ...
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Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition, have been subject to this transition. The classifier ensemble which uses a number of base classifiers is considered as meta-classifier to learn any classification problem in pattern recognition. Although some researchers think they are better than single classifiers, they will not be better if some conditions are not met. The most important condition among them is diversity of base classifiers. Generally in design of multiple classifier systems, the more diverse the results of the classifiers, the more appropriate the aggregated result. It has been shown that the necessary diversity for the ensemble can be achieved by manipulation of dataset features, manipulation of data points in dataset, different sub-samplings of dataset, and usage of different classification algorithms. We also propose a new method of creating this diversity. We use Linear Discriminant Analysis to manipulate the data points in dataset. Although the classifier ensemble produced by proposed method may not always outperform all of its base classifiers, it always possesses the diversity needed for creation of an ensemble, and consequently it always outperforms all of its base classifiers on average.
Pattern Analysis and Intelligent Systems
Mozhgan Rahimirad; Mohammad Mosleh; Amir Masoud Rahmani
Volume 1, Issue 2 , May 2015, , Pages 1-8
Abstract
With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to ...
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With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.