clustering

Open Access Article

1 - Novel Automatic Clustering Technique Based on the Artificial Immune Algorithm
Seyed-Hamid Zahiri

20.1001.1.16823745.1387.6.14.12.4

In this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been a More

In this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the immune algorithm to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed algorithm (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering system. The method has been tested on the different kinds of the complex artificial data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering algorithm (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic algorithm based clustering technique (as an evolutionary clustering algorithm). Manuscript profile

Open Access Article

2 - A Method for Automatic Printing Carpet Map Reading and Comparing to C-Means Clustering
Ahmad Izadipour E. Kabir

20.1001.1.16823745.1389.8.1.6.6

The subject of this paper is to read carpet pattern automatically by computer. This is composed of two steps: detection of vertical and horizontal lines in the pattern and color reduction. Color reduction is essential because of limitation of the number of colors that i More

The subject of this paper is to read carpet pattern automatically by computer. This is composed of two steps: detection of vertical and horizontal lines in the pattern and color reduction. Color reduction is essential because of limitation of the number of colors that is used in a carpet. To accomplish of this process, we must detect the grid lines on the carpet pattern automatically. These lines are two types: thin lines and thick lines. At the first stage, the distance between thin lines is obtained. Having the first thin line detected, the other thin lines are drawn using this distance. We use a Comb method for detection of thick lines. The major problem in line detection is lagging or leading of the lines due to the mismatch between sampling frequency of the scanner and image resolution. We compensate this distortion in various steps in our algorithm. In the second step, we want all the pixels in the same square, to have the same color. This is obtained by mapping colors to the best color in the palette. We propose three methods. In first method the user selects two selections per any colors. Palette is obtained from some processes in these selections. Those pixels that are in the middle of the squares are mapped to the palette. Then color histogram is computed. The color that has the maximum histogram value is assigned to the square. In order to decrease user’s interference, C-means clustering algorithm is used in two types. The centers of initial clusters are determined once with user’s interference and once randomly. Results of these three methods are compared. We tested our methods on 20 samples of carpet patterns, and the error rate was variable from 0.07% to 0.5% between samples. Manuscript profile

Open Access Article

3 - Ensemble Feature Selection Strategy Based on Hierarchical Clustering in Electronic Nose
M. A. Bagheri Gh. A. Montazer

20.1001.1.16823745.1389.8.3.5.9

The redundancy problem of sensor response in electronic noses is still remarkable due to the cross-selectivity of chemical gas sensors which can degrade the classification performance. In such situations, a more efficient multiple classifier system can be obtained in ra More

The redundancy problem of sensor response in electronic noses is still remarkable due to the cross-selectivity of chemical gas sensors which can degrade the classification performance. In such situations, a more efficient multiple classifier system can be obtained in random feature space rather than in the original one. Ensemble Feature Selection (EFS) methods assume that there is redundancy in the overall feature set and better performance can be achieved by choosing different subsets of input features for multiple classifiers. By combining these classifiers the higher recognition rate can be achieved. In this paper, we propose a feature subset selection method based on hierarchical clustering of transient features in order to enhance the classifier diversity and efficiency of learning algorithms. Our algorithm is tested on the UCI benchmark data sets and then used to design an odor recognition system. The experimental results of proposed method based on hierarchical clustering feature subset selection and multiple classifier system demonstrate the more efficient classification performance. Manuscript profile

Open Access Article

4 - Extracting Bottlenecks Using Object Recognition in Reinforcement Learning
B. Ghazanfari N. Mozayani M. R. Jahed Motlagh

20.1001.1.16823745.1391.10.2.6.2

Extracting bottlenecks improves considerably the speed of learning and the ability knowledge transferring in reinforcement learning. But, extracting bottlenecks is a challenge in reinforcement learning and it typically requires prior knowledge and designer’s help. This More

Extracting bottlenecks improves considerably the speed of learning and the ability knowledge transferring in reinforcement learning. But, extracting bottlenecks is a challenge in reinforcement learning and it typically requires prior knowledge and designer’s help. This paper will propose a new method that extracts bottlenecks for reinforcement learning agent automatically. We have inspired of biological systems, behavioral analysts and routing animals and the agent works on the basis of its interacting to environment. The agent finds landmarks based in clustering and hierarchical object recognition. If these landmarks in actions space are close to each other, bottlenecks are extracted using the states between them. The Experimental results show a considerable improvement in the process of learning in comparison to some key methods in the literature. Manuscript profile

Open Access Article

5 - Sub-Word Image Clustering in Old Printed Documents Using Template Matching
M. R. Soheili E. Kabir

20.1001.1.16823745.1392.11.4.3.0

Due to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications More

Due to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications dealing with large uniform documents. We assumed that the whole document is printed in a single font and print quality is not good. To test our method, we created a dataset of all sub-words of a Farsi book. The book has 233 pages with more than 111000 sub-words manually labeled. We use an incremental clustering algorithm. Four simple features are extracted from each sub-word and compared with the corresponding features of each cluster center. If all features' differences lie within certain thresholds, the sub-word and the winner cluster center are finely compared using a template matching algorithm. In our experiments, we show that all sub-words of the book are recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members. Manuscript profile

Open Access Article

6 - Learners Grouping in Adaptive Learning Systems Using Fuzzy Grafting Clustering
M. S. Rezaei Gh. A. Montazer

20.1001.1.16823745.1394.13.4.5.6

Quality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clusterin More

Quality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clustering methods by combination of them and improving methods. This work makes the complexity of grouping methods increased and quality of result’s groups decreased. In this paper, new method for selection appropriate clusters based on fuzzy theory is proposed. In this method, each cluster is defined as a fuzzy set and the corresponding clusters are determined. So the best cluster is selected among each corresponding clusters. The results of an empirical evaluation of the proposed method based on two criteria: “Davies-Bouldin” and “Purity and Gathering” indicate that this method has better performance than other clustering methods such as FCM, K-means, hybrid clustering method (HCM), evolutionary fuzzy clustering (EFC) and ART neural network. Manuscript profile

Open Access Article

7 - Botnet Detection Based on Computing Negative Reputation Score by Use of a Clustering Method and DNS Traffic
R. Sharifnyay Dizboni A. Manafi Murkani

20.1001.1.16823745.1395.14.1.14.6

Today, botnets are known as one of the most important threats against Internet infrastructure. A botnet is a network of compromised hosts (bots) remotely controlled by a so-called botmaster through one or more command and control (C&C) servers. Since DNS is one of the m More

Today, botnets are known as one of the most important threats against Internet infrastructure. A botnet is a network of compromised hosts (bots) remotely controlled by a so-called botmaster through one or more command and control (C&C) servers. Since DNS is one of the most important services on Internet, botmasters use it to resistance their botnet. By use of DNS service, botmasters implement two techniques: IP-flux and domain-flux. These techniques help an attacker to dynamically change C&C server addresses and prevent it from becoming blacklisted. In this paper, we propose a reputation system used a clustering method and DNS traffic for online fluxing botnets detection .we first cluster DNS queries with similar characteristics at the end of each time period. We then identify hosts that generate suspicious domain names and add them to a so-called suspicious group activity matrix. We finally calculate the negative reputation score of each host in the matrix and detect hosts with high negative reputation scores as bot-infected. The experimental results show that it can successfully detect fluxing botnets with a high detection rate and a low false alarm rate. Manuscript profile

Open Access Article

8 - Using Contour Information for Body Orientation Estimation in the Image
A. Sebti H. Hassanpour

20.1001.1.16823745.1395.14.4.13.1

Pose and orientation of a person relative to the camera are the important and useful information in many applications, including surveillance systems. This information can be used in the behavior analysis of the person. Low quality of the recorded surveillance images, n More

Pose and orientation of a person relative to the camera are the important and useful information in many applications, including surveillance systems. This information can be used in the behavior analysis of the person. Low quality of the recorded surveillance images, noisy data and cluttered backgrounds are some of the difficulties in this task. In the existing methods, histogram of orientation gradient (HOG) is used to estimate the orientation. The local properties of HOG is a weakness for orientation estimation. The edge surrounding the object, namely contour, is a useful information for orientation estimation. In this paper we present a general form of a contour. This hyper contour helps us to find the best contour which is matched to image of the person in a hierarchical fashion. These contours generated from a human 3D model. The matched contour as a high-level feature is combined with the low-level feature such as HOG, and considered as the final feature. The proposed feature is a linear combination of several types of contours with respect to different regions of the body. To show the impact of the proposed feature on orientation estimation, a support vector machine is trained on a hybrid feature space and then is evaluated on VIPeR dataset. The experimental results show that the accuracy of the orientation estimation is improved about 4% by using the extended feature. Manuscript profile

Open Access Article

9 - Proposing a Density-Based Clustering Algorithm with Ability to Discover Multi-Density Clusters in Spatial Databases
A. Zadedehbalaei A. Bagheri H. Afshar

20.1001.1.16823745.1396.15.3.10.3

Clustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits s More

Clustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits suffers from some issues such as difficulty in determining appropriate values for input parameters and inability to detect clusters with different densities. In this paper, we introduce a new clustering algorithm which unlike DBSCAN algorithm, can detect clusters with different densities. This algorithm also detects nested clusters and clusters sticking together. The idea of the proposed algorithm is as follows. First, we detect the different densities of the dataset by using a technique and Eps parameter is computed for each density. Then DBSCAN algorithm is adapted with the computed parameters to apply on the dataset. The experimental results which are obtained by running the suggested algorithm on standard and synthetic datasets by using well-known clustering assessment criteria are compared to the results of DBSCAN algorithm and some of its variants including VDBSCAN, VMDBSCAN, LDBSCAN, DVBSCAN and MDDBSCAN. All these algorithms have been introduced to solve the problem of multi-density data sets. The results show that the suggested algorithm has higher accuracy and lower error rate in comparison to the other algorithms. Manuscript profile

Open Access Article

10 - Handover Management between Femtocell and Macrocell Using Geo-Based Spectral Clustering
T. Bahraini M. Zamiri H. Sadoghi Yazdi

20.1001.1.16823745.1397.16.1.10.6

Available techniques in handover management in cellular communication networks can’t keep unnecessary events and delay decision at a low level state. The main purpose of this paper is to provide the intelligence method which is able to minimize the number of unnecessary More

Available techniques in handover management in cellular communication networks can’t keep unnecessary events and delay decision at a low level state. The main purpose of this paper is to provide the intelligence method which is able to minimize the number of unnecessary events and allowing the necessary requests to occur and so improves the overall network performance. In order to achieve such a goal, in the proposed method, we have used the geographical knowledge from building maps with spectral clustering in the area covered by femtocell. Therefore, we require to develop the spectral clustering based on geographical information. The experimental results on real dataset and performed simulations indicate that the superiority of the proposed method in allocating the user to appropriate cell and acceptable ability to manage the handover in heterogeneous layer of femtocell-macrocell. Manuscript profile

Open Access Article

11 - Proposing a New Method for Acquiring Skills in Reinforcement Learning with the Help of Graph Clustering
M. Davoodabadi Farahani N. Mozayani

20.1001.1.16823745.1397.16.2.5.3

Reinforcement learning is atype of machine learning methods in which the agent uses its transactions with the environment to recognize the environment and to improve its behavior.One of the main problems of standard reinforcement learning algorithms like Q-learning is t More

Reinforcement learning is atype of machine learning methods in which the agent uses its transactions with the environment to recognize the environment and to improve its behavior.One of the main problems of standard reinforcement learning algorithms like Q-learning is that they are not able to solve large scale problems in a reasonable time. Acquiring skills helps to decompose the problem to a set of sub-problems and to solve it with hierarchical methods. In spite of the promising results of using skills in hierarchical reinforcement learning, it has been shown in some previous studies that based on the imposed task, the effect of skills on learning performance can be quite positive. On the contrary, if they are not properly selected, they can increase the complexity of problem-solving. Hence, one of the weaknesses of previous methods proposed for automatically acquiring skills is the lack of a systematic evaluation method for each acquired skill. In this paper, we propose new methods based on graph clustering for subgoal extraction and acquisition of skills. Also, we present new criteria for evaluating skills, with the help of which, inappropriate skills for solving the problem are eliminated. Using these methods in a number of experimental environments shows a significant increase in learning speed. Manuscript profile

Open Access Article

12 - Identifying Primary User Emulation Attacks in Cognitive Radio Network Based on Bayesian Nonparametric Bayesian
K. Akbari J. Abouei

20.1001.1.16823745.1397.16.3.12.2

Cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack More

Cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack, thus malicious user attempts to send a signal same as primary user's signal to deceive secondary users and prevent them from sending signals in the spectrum holes. Meanwhile, causing traffic in CR network, malicious user obtains a frequency band to send their information. In this thesis, a method to identify primary user emulation attack is proposed. According to this method, primary users and malicious users are distinguished by clustering. In this method, the number of active users is recognized in the CR network by clustering. Indeed, by using Dirichlet process mixture model classification based on the Bayesian Nonparametric method, primary users are clustered. In addition, to achieve higher convergence rate, Chinese restaurant process method to initialize and non-uniform sampling is applied to select clusters parameter. Manuscript profile

Open Access Article

13 - Improved BIRCH Clustering by Chemical Reaction Optimization Algorithm to Health Fraud Detection
M. Abdolrazzagh-Nezhad M. Kherad

20.1001.1.16823745.1398.17.2.15.0

With regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are seve More

With regard to the scale of the financial transactions and the extent of the healthcare industry, it is one of the ideal systems for fraud. Therefore, suitable identifying fraud data is still one of the challenges facing the healthcare providers, although there are several fraud detection algorithms. In the paper, the BIRCH clustering algorithm, as one hierarchical clustering algorithm, is hybridized with a chemical reaction optimization algorithm (CRO). The BIRCH with linear time complexity is able for clustering large scale data and identifying their noises and the CRO, as one of new meta-heuristic algorithm inspired by the chemical reactions in the real world, explores the search space with a dynamic population size based on four reactions such as on-wall ineffective collision, decomposition, inter-molecular ineffective collision and synthesis. Due to the improved BIRCH-CRO removes the internal clustering process of the classic BIRCH and determines the optimal values of its main parameters, it causes that the computational time decreases and accuracy and precision of detecting fraud data increase since its experimental results is compared with the exist unsupervised algorithms. Also, the proposed fraud detection algorithm has the ability to perform on online data and large scale data, and given the obtained results, it provides a proper performance. Manuscript profile

Open Access Article

14 - Anomaly Detection in the Car Trajectories Using Sparse Reconstruction
Reyhane Taghizade Abbas Ebrahimi moghadam M. Khademi

20.1001.1.16823745.1401.20.1.1.5

In traffic control and vehicle registration systems a big challenge is achieving a system that automatically detects abnormal driving behavior. In this paper a system for detection of vehicle anomalies proposed, which at first extracts spatio-temporal features form clus More

In traffic control and vehicle registration systems a big challenge is achieving a system that automatically detects abnormal driving behavior. In this paper a system for detection of vehicle anomalies proposed, which at first extracts spatio-temporal features form clusters then creates dictionary from these features. This classification stage consists of processes such as, optimized clustering with the bee mating algorithm and sparse processing on spatiotemporal features derived from the training data. Finally the trained classifier is applied to the test data for anomaly detection. The distinction of this study from previous research is using new method of pre-processing to create a dictionary matrix and anomaly detection based on evaluation of matrix that related to each class dependency, which leads to higher accuracy of the proposed method compared to other leading methods. To evaluate the proposed method, UCSD database and video sequences recorded from vehicle traffic on Vakilabad Boulevard at the north side of Ferdowsi University of Mashhad are used and the performance of the proposed method is compare to other competing methods in this field. By analyzing the evaluation standards, we find that the proposed method performance is better than other methods. Manuscript profile

Open Access Article

15 - Feature Selection and Cancer Classification Based on Microarray Data Using Multi-Objective Cuckoo Search Algorithm
kh. Kamari f. rashidi a. Khalili

20.1001.1.16823745.1398.17.4.9.8

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Mo More

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy. Manuscript profile

Open Access Article

16 - Using Evolutionary Clustering for Topic Detection in Microblogging Considering Social Network Information
E. Alavi H. Mashayekhi H. Hassanpour B. Rahimpour Kami

20.1001.1.16823745.1398.17.4.11.0

Short texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must More

Short texts of social media like Twitter provide a lot of information about hot topics and public opinions. For better understanding of such information, topic detection and tracking is essential. In many of the available studies in this field, the number of topics must be specified beforehand and cannot be changed during time. From this perspective, these methods are not suitable for increasing and dynamic data. In addition, non-parametric topic evolution models lack appropriate performance on short texts due to the lack of sufficient data. In this paper, we present a new evolutionary clustering algorithm, which is implicitly inspired by the distance-dependent Chinese Restaurant Process (dd-CRP). In the proposed method, to solve the data sparsity problem, social networking information along with textual similarity has been used to improve the similarity evaluation between the tweets. In addition, in the proposed method, unlike most methods in this field, the number of clusters is calculated automatically. In fact, in this method, the tweets are connected with a probability proportional to their similarity, and a collection of these connections constitutes a topic. To speed up the implementation of the algorithm, we use a cluster-based summarization method. The method is evaluated on a real data set collected over two and a half months from the Twitter social network. Evaluation is performed by clustering the texts and comparing the clusters. The results of the evaluations show that the proposed method has a better coherence compared to other methods, and can be effectively used for topic detection from social media short texts. Manuscript profile

Open Access Article

17 - Improving Energy Consumption in Wireless Sensor Networks Using Shuffled Frog Leaping Algorithm and Fuzzy Logic
Shayesteh Tabatabaey

20.1001.1.16823745.1400.19.1.11.8

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the w More

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled Frog Leaping algorithm, which acts based on the Frog food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802.15.4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802.15.4 Protocol and NODIC Protocol due to the use of the criteria listed. Manuscript profile

Open Access Article

18 - A Semi-Central Method to Improve Energy Saving in Real Wireless Sensor Networks Using Clustering and Mobile Sinks
Fatemeh Sadeghi Sepideh Adabi Sahar Adabi

20.1001.1.16823745.1400.19.2.14.3

Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to criti More

Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to critical regions (i.e., regions those have low remaining energy and thus, high risk of energy hole problem). The limited number of mobile sinks should be utilized due to a high cost. Therefore, allocating the limited number of mobile sinks to the high amount of requests received from the critical regions is categorized as a NP-hard problem. Most of the previous studies address this problem by using heuristic methods which are carried out by sensor nodes. However, this type of solutions cannot be implemented in real WSN due to the sensors’ current technology and their limited processing capability. In other words, these are just theoretical solutions. Consequently, a semi-central genetic algorithm based method using mobile sink and clustering technique is proposed in order to find a trade-off between reduction of computation load on the sensors and increasing accuracy. In our method, lightweight computations are separated from heavyweight computations. While, the former computations are carried out by sensors, the latter are carried out by base station. Following activities are done by the authors: 1) cluster head selection by using effective environmental parameters and defining cost function of cluster membership, 2) mathematical modeling of a region’s chance to achieve mobile sink, and 3) designing a fitness function to evaluate the fitness of each allocation of mobile sinks to the critical regions in genetic algorithm. Furthermore, in our activities minimizing the number and length of messages are focused. In summary, the main distinguishing feature of the proposed method is that it can be implemented in real WSN (due to separation of lightweight computations from heavyweight computations) with respect to early mentioned objectives. The simulation results show the better performance of the proposed method compared to comparison bases. Manuscript profile

Open Access Article

19 - A New Data Clustering Method Using 4-Gray Wolf Algorithm
Laleh Ajami Bakhtiarvand Zahra Beheshti

20.1001.1.16823745.1400.19.4.12.5

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in More

Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets. Manuscript profile

Open Access Article

20 - Energy-Aware Data Gathering in Rechargeable Wireless Sensor Networks Using Particle Swarm Optimization Algorithm
Vahideh Farahani Leili Farzinvash Mina Zolfy Lighvan Rahim Abri Lighvan

20.1001.1.16823745.1400.19.4.13.6

This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine t More

This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware Data Gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5.6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero Manuscript profile

Open Access Article

21 - A POI Recommendation Model According to the Behavior Pattern of Users Based on Friends List Using Deep Learning
sadaf safavi mehrdad jalali

20.1001.1.16823745.1400.19.2.16.5

The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is More

The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is to investigate numerous significant traits of users and POIs. In this work, a novel method is presented for POI recommendation to develop the accurate sequence of top-k POIs to users, which is a combination of convolutional neural network, clustering and friendship. To discover the likeness, we use the mean-shift clustering method and only consider the influence of the most similarities in pattern’s friendship, which has the greatest psychological and behavioral impact rather than all user’s friendship. The new framework of a convolutional neural network with 10 layers can predict the next suitable venues and then select the accurate places based on the shortest distance from the similar friend behavior pattern. This approach is appraised on two LBSN datasets, and the experimental results represent that our strategy has significant improvements over the state-of-the-art techniques for POI recommendation. Manuscript profile

Open Access Article

22 - A Novel Method Based on Non-Negative Matrix Factorization for Dimensions Reduction
Mehdi Hosseinzadeh Aghdam مرتضی آنالویی Jafar Tanha

20.1001.1.16823745.1401.20.2.4.0

Machine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative mat More

Machine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative matrix factorization reduces data dimensions by extracting latent features from large dimensional data. Non-negative matrix factorization only considers how to model each feature vector in the decomposed matrices and ignores the relationships between feature vectors. The relationships between feature vectors provide better factorization for machine learning applications. In this paper, a new method based on non-negative matrix factorization is proposed to reduce the dimensions of the data, which sets constraints on each feature vector pair using distance-based criteria. The proposed method uses the Frobenius norm as a cost function to create update rules. The results of experiments on the data sets show that the proposed multiplicative update rules converge rapidly and give better results than other algorithms. Manuscript profile

Open Access Article

23 - Improving Precision of Recommender Systems using Time-, Location- and Context-aware Trust Estimation Based on Clustering and Beta Distribution
Samaneh Sheibani Hassan Shakeri Reza Sheybani

20.1001.1.16823745.1402.21.1.16.7

Calculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimat More

Calculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimating trust among users of recommender systems is introduced. In the proposed scheme, first, users of the system are clustered based on their similarities in demographics information and history of ratings. To predict the rating of the active user into a specific item, the value of trust between him and the other users in his cluster is calculated considering the factors i.e. time, location, and context of their rating. To this end, we propose an algorithm based on beta distribution. A novel tree-based measure for computing the semantic similarity between the contexts is utilized. Finally, the rating of the active user is predicted using weighted averaging where trust values are considered as weights. The proposed scheme was performed on three datasets, and the obtained results indicated that it outperforms existing methods in terms of accuracy and other efficiency metrics. Manuscript profile

Open Access Article

24 - Video Summarization Using a Clustering Graph Neural Networks
Mahsa RahimiResketi Homayun Motameni Ebrahim Akbari Hossein Nematzadeh

20.1001.1.16823745.1402.21.2.7.0

The increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summar More

The increase of cameras nowadays, and the power of the media in people's lives lead to a staggering amount of video data. It is certain that a method to process this large volume of videos quickly and optimally becomes especially important. With the help of video summarization, this task is achieved and the film is summarized into a series of short but meaningful frames or clips. This study tried to cluster the data by an algorithm (K-Medoids) and then with the help of a convolutional graph attention network, temporal and graph separation is done, then in the next step with the connection rejection method, noises and duplicates are removed, and finally summarization is done by merging the results obtained from two different graphical and temporal steps. The results were analyzed qualitatively and quantitatively on three datasets SumMe, TVSum, and OpenCv. In the qualitative method, an average of 88% accuracy rate in summarization and 31% error rate was achieved, which is one of the highest accuracy rates compared to other methods. In quantitative evaluation, the proposed method has a higher efficiency than the existing methods. Manuscript profile

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