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A recommender system is a model that automatically recommends some meaningful cases (such as clips/films/goods/items) to the clients/people/consumers/users according to their (previous) interests. These systems are expected to recommend the items according to the users' interests. There are two traditional general recommender system models, i.e., Collaborative Filtering Recommender System (ColFRS) and Content-based Filtering Recommender System (ConFRS). Also, there is another model that is a hybrid of those two traditional recommender systems; it is called Hybrid Recommender System (HRS). An HRS usually outperforms simple traditional recommender systems. The problems such as scalability, cold start, and sparsity belong to the main problems that any recommender system should solve. The memory-based (modeless) recommender systems benefit from good accuracies. But they suffer from a lack of admissible scalability. The model-based recommender systems suffer from a lack of admissible accuracies. But they benefit from good scalability. In this paper, it is tried to propose a hybrid model based on an automatically improved ontology to solve the scalability, cold start, and sparsity problems. Our proposed HRS also uses an innovative approach of clustering as an augmented section. When there are enough ratings, it uses a collaborative filtering approach to predict the missing ratings. When there are not enough ratings, it uses a content-based filtering approach to predict the missing ratings. In the content-based filtering section of our proposed HRS, ontology concepts are used to improve the accuracy of ratings' prediction. If our target client is severely sparse, we cannot trust even the ratings predicted by the content-based filtering section of our proposed HRS. Therefore, our proposed HRS uses additive clustering to improve the prediction of the missing ratings if the target client is severely sparse. It is experimentally shown that our model outperforms many of the newly developed recommender systems. [ABSTRACT FROM AUTHOR]

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20–30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs. [ABSTRACT FROM AUTHOR]

Streaming data has been evolved in a dynamically changing and evolving environment. Therefore, concept drift or changing the underlying distribution of data over time is considered as an important challenge in processing this type of data. Moreover, concept drift affects the performance of anomaly detection process. The problem of anomaly detection in streaming data is applied to many important applications, for instance, intrusion detection in computer networks or traffic management in the road networks. In recent years, some tensor decomposition based approaches have been presented that track the main pattern or subspace of data in an online manner and adapt the learner with probabilistic changes continuously in all time-intervals by using an implicit strategy. We propose an online approach that detects the concept drift in an explicit manner. Moreover, the learner has been adapted with drift and changes only in their occurrences using informed strategy. Evaluation of the proposed method is performed with real datasets. Analysis of the obtained results confirms the promising performance of the proposed method in terms of learning and detection. [ABSTRACT FROM AUTHOR]

The social network embodies real-life social graphs. Detecting communities or clusters from these graphs is an ill-posed difficult task. The communities are identified using the adjacent nodes that have shared edges and similar features. One of the principal concerns after community detection is to identify the active nodes in a network who attend several communities. So, finding communities that are overlapped in a social network is an important topic in social network analysis. This paper introduces an algorithm based on the multi-agent particle swarm optimization. The proposed algorithm detects overlapping as well as non-overlapping communities. Following the detection of overlapping communities, this algorithm can identify those nodes leading to overlapping, and ultimately it can determine the affiliation ratio of each node to the given community. The algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In this method, the modularity measure is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm which is called Fuzzy Overlapping Community Detection based on Multi-Agent Particle Swarm Optimization (FOCDMAPSO), is superior compared with four other competitive algorithms. This algorithm is implemented over six well-known datasets and five LFR datasets in the literature. Our algorithm is capable of detecting nodes in overlapping communities with high accuracy. Furthermore, the proposed algorithm can detect the affiliation percentage of each node that leads to overlapping communities which is a novel feature in the area of the social network. [ABSTRACT FROM AUTHOR]

Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods. [ABSTRACT FROM AUTHOR]

Feature selection is an important preprocessing task in classification that eliminates the irrelevant, redundant, and noisy features. Improving the performance of model, decreasing the computational cost, and adjusting the “curse of dimensionality” are the key advantages of feature selection task. The evolution process of the existing multi-objective based feature selection algorithms is relied on the objective space while the problem space contains useful information. This paper proposes a multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set. Then, these ranks are used to refine the archive set and guide the particles. The proposed method is compared with three PSO based and one genetic based multi-objective methods on 9 Benchmark datasets. Qualitative and quantitative analyses of the results are performed by visual analysis of the Pareto fronts and three performance metrics respectively. Finally, remarkable performance in datasets with more than hundred features and satisfactory performance in datasets with less than hundred features are obtained. [ABSTRACT FROM AUTHOR]

A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs. [ABSTRACT FROM AUTHOR]

Frequent itemset mining is a basic data mining task and has numerous applications in other data mining tasks. In recent years, some data structures based on sets of nodes in a prefix tree have been presented. These data structures store essential information about frequent itemsets. In this paper, we propose another efficient data structure, NegNodeset . Similar to other such data structures, the basis of NegNodeset is sets of nodes in a prefix tree. NegNodeset employs a novel encoding model for nodes in a prefix tree based on the bitmap representation of sets. Based on the NegNodeset data structure, we propose negFIN, which is an efficient algorithm for frequent itemset mining. The efficiency of the negFIN algorithm is confirmed by the following three reasons: (1) the NegNodeset s of itemsets are extracted using bitwise operators, (2) the complexity of calculating NegNodeset s and counting supports is reduced to O ( n ), where n is the cardinality of NegNodeset , and (3) it employs a set-enumeration tree to generate frequent itemsets and uses a promotion method to prune the search space in this tree. Our extensive performance study on a variety of benchmark datasets indicates that negFIN is the fastest algorithm, compared with previous state-of-the-art algorithms. However, our algorithm runs with the same speed as dFIN on some datasets. [ABSTRACT FROM AUTHOR]

Nowadays, text is one prevalent forms of data and text classification is a widely used data mining task, which has various application fields. One mass-produced instance of text is email. As a communication medium, despite having a lot of advantages, email suffers from a serious problem. The number of spam emails has steadily increased in the recent years, leading to considerable irritation. Therefore, spam detection has emerged as a separate field of text classification. A primary challenge of text classification, which is more severe in spam detection and impedes the process, is high-dimensionality of feature space. Various dimension reduction methods have been proposed that produce a lower dimensional space compared to the original. These methods are divided mainly into two groups: feature selection and feature extraction. This research deals with dimension reduction in the text classification task and especially performs experiments in the spam detection field. We employ Information Gain (IG) and Chi-square Statistic (CHI) as well-known feature selection methods. Also, we propose a new feature extraction method called Sprinkled Semantic Feature Space (SSFS). Furthermore, this paper presents a new hybrid method called IG_SSFS. In IG_SSFS, we combine the selection and extraction processes to reap the benefits from both. To evaluate the mentioned methods in the spam detection field, experiments are conducted on some well-known email datasets. According to the results, SSFS demonstrated superior effectiveness over the basic selection methods in terms of improving classifiers' performance, and IG_SSFS further enhanced the performance despite consuming less processing time. [ABSTRACT FROM AUTHOR]

• A multi-criteria collaborative recommender system is proposed for MOOCs. • Text mining and fuzzy logic approaches are used for method development. • Data collection is performed in Udemy.com. • Numerical And textual data are analyzed for method evaluation. • The method is effective for MOOCs websites for course recommendations. Massive Open Online Courses (MOOCs) provide learners with high-quality and flexible online courses with no limitations regarding time and location. Detecting users' behavior in MOOCs is an important task for course recommendations. Collaborative Filtering (CF) is considered the widely approach in recommender systems to provide a online learner courses according to similar learners' preferences in an e-learning environment. The current research provides a novel framework through machine learning techniques to propose course recommendations in MOOCs according to the uses' preferences and behavior. The method is developed using multi-criteria ratings extracted from users' online reviews. We use Latent Dirichlet Allocation (LDA) for text mining, Decision Trees for decision rule generation, Self-Organizing Map (SOM) for users' reviews on courses and the fuzzy rule-based system for users' preferences prediction. We also adopt a feature selection method to select the most important criteria for users' preferences prediction. The method is evaluated using the data collected from an online learning platform, Udemy. The results showed that the method is able to accurately provide relevant courses to the users tailored to their preferences. The method has the potential to be implemented as a recommendation agent in the MOOC websites for course recommendations. [ABSTRACT FROM AUTHOR]

• A new method for big social data analysis to reveal traveller' behavior is presented. • EM clustering approach is used for customers' segmentation. • Neural network combined with fuzzy logic is used for preference prediction. • The method uses HOSVD for data dimensionality reduction. • The method is evaluated on eco-friendly hotels data. Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers' online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers' preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers' segmentation, and preference learning and prediction in eco-friendly hotels. [ABSTRACT FROM AUTHOR]

Abstract: Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets. [Copyright &y& Elsevier]

In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model defines a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. Despite the crisp primary partitions, the suggested model employs fuzzy logic in the mentioned objective function. Each row in a candidate solution of the model includes membership degrees indicating how much data point belongs to each cluster. The defined nonlinear model can be solved by every nonlinear optimizer; however; we used genetic algorithm to solve it. Accordingly, three suitable crossover and mutation operators satisfying the constraints of the problem are devised. The proposed crossover operators exchange information between two clusters. They use a novel relabeling method to find corresponding clusters between two partitions. The algorithm is applied on multiple standard datasets. The obtained results show that the modified genetic algorithm operators are desirable in exploration and exploitation of the big search space. [ABSTRACT FROM AUTHOR]

Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems. [ABSTRACT FROM AUTHOR]

• NEclatClosed is a fast algorithm for mining frequent closed itemsets. • Some concepts and thermos based on vertical database format is represented. • NEclatClosed employs these concepts and thermos in the mining process. • NEclatClosed is adopted from the Eclat algorithm. Frequent closed itemsets provide a lossless and concise collection of all frequent itemsets to reduce the runtime and memory requirement of frequent itemsets mining tasks. This study presents an algorithm named NEclatClosed for fast mining of frequent closed itemsets. We introduce concepts and techniques based on the vertical database format and employ them in the mining process. The experimental results show that NEclatClosed outperforms the leading algorithms in terms of runtime and memory usage, especially runtime, in most cases. [ABSTRACT FROM AUTHOR]

As one of the significant issues in social networks analysis, the influence maximization problem aims to fetch a minimal set of the most influential individuals in the network to maximize the number of influenced nodes under a diffusion model. Several approaches have been proposed to tackle this NP-hard problem. The traditional approaches failed to develop an efficient and effective solution due to the exponential growth of the size of social networks (due to massive computational overhead). In this paper, a three-stage framework based on the community detection approach is devised, namely LGFIM. In the first stage, the search space was controlled by partitioning the network into communities. Simultaneously, three heuristic methods were presented for modifying the community detection algorithm to extract the optimal communities: core nodes selection, capacity constraint on communities, and communities combination. These extracted communities were highly compatible with the information propagation mechanism. The next stages apply a scalable and robust algorithm at two different levels of the network: 1. Exploring the local scope of communities to select the most influential nodes of each community and construct the potential influential nodes set 2. Exploring the global scope of the network to select the target influential nodes among potential influential nodes set. Experimental results on various real datasets proved that LGFIM could achieve remarkable results compared with the state-of-the-art algorithms, especially acceptable influence spread, much better running time, and more applicable to massive social networks. [ABSTRACT FROM AUTHOR]

Anomaly detection in time-evolving networks has many applications, for instance, traffic analysis in transportation networks and intrusion detection in computer networks. One group of popular methods for anomaly detection from evolving networks are robust online subspace trackers. However, these methods suffer from problem of insensitivity to drastic changes in the evolving subspace. In order to solve this problem, we propose a new robust online subspace and anomaly tracker, which is more adaptive and robust against sudden drastic changes in the subspace. More accurate estimation of low rank and sparse components by this tracker leads to more accurate anomaly detection. We evaluate the accuracy of our method with real-world dynamic network data sets with varying sparsity levels. The result is promising and our method outperforms the state-of-the-art. • An online tracker that is more adaptive and robust against sudden drastic changes in the subspace. • A framework for learning, monitoring and detecting anomalies in time-evolving networks. • The more accurate estimation of low rank and sparse, the higher performance for anomaly detection. • Evaluations on real-world dynamic networks with varying sparsity levels. [ABSTRACT FROM AUTHOR]

In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods. In the first step, a new definition of data indeterminacy (indeterminacy set) is proposed in NS domain based on density properties of data. Lower indeterminacy is assigned to data points in dense regions and vice versa. In the second step, indeterminacy set is presented for a proposed cost function in NS domain by considering a set of main clusters and a noisy cluster. In the proposed cost function, two conditions based on distance from cluster centers and value of indeterminacy, are considered for each data point. In the third step, the proposed cost function is minimized by gradient descend methods. Data points are clustered based on their membership degrees. Outlier points are assigned to noise cluster; and boundary points are assigned to main clusters with almost same membership degrees. To show the effectiveness of the proposed method, three types of datasets including diamond, UCI and image datasets are used. Results demonstrate that the proposed cost function handles boundary and outlier points with more accurate membership degrees and outperforms existing state of the art clustering methods in all datasets. [ABSTRACT FROM AUTHOR]

In recent years, various studies have been conducted on SVMs and their applications in different area. They have been developed significantly in many areas. SVM is one of the most robust classification and regression algorithms that plays a significant role in pattern recognition. However, SVM has not been developed significantly in some areas like large-scale datasets, unbalanced datasets, and multiclass classification. Efficient SVM training in large-scale datasets is of great importance in the big data era. However, as the number of samples increases, the time and memory required to train SVM increase, making SVM impractical even for a medium-sized problem. With the emergence of big data, this problem becomes more significant. This paper presents a novel distributed method for SVM training in which a very small subset of training samples is used for classification, which reduces the problem size and thus the required memory and computational resources. The solution of this problem almost converges to standard SVM. This method includes three steps: first, detecting a subset of distributed training samples, second, creating local models of SVM and obtaining partial vectors, and finally combining the partial vectors and obtaining the global vector and the final model. In addition, the datasets which suffer from unbalanced number of samples and tend to the majority class, the proposed method balances the samples of the two classes and it can be used in unbalanced datasets. The empirical results show that using this method is efficient for large-scale problems. [ABSTRACT FROM AUTHOR]

Introduction: Video games affect the stress system and cognitive abilities in different ways. Here, we evaluated electrophysiological and biochemical indicators of stress and assessed their effects on cognition and behavioral indexes after playing a scary video game. Methods: Thirty volunteers were recruited into two groups as control and experimental. The saliva and blood samples were collected before and after intervention (watching/playing the scary game for control and experimental groups respectively). To measure cortisol and salivary alpha-amylase (sAA) levels, oxytocin (OT), and brain-derived neurotrophic factor (BDNF) plasma levels, dedicated ELISA kits were used. Electroencephalography recording was done before and after interventions for electroencephalogram (EEG)-based emotion and stress recognition. Then, the feature extraction (for mental stress, arousal, and valence) was done. Matrix laboratory (MATLAB) software, version 7.0.1 was used for processing EEG-acquired data. The repeated measures were applied to determine the intragroup significance level of difference. Results: Scary gameplay increases mental stress (P<0.001) and arousal (P<0.001) features and decreases the valence (P<0.001) one. The salivary cortisol and alpha-amylase levels were significantly higher after the gameplay (P<0.001 for both). OT and BDNF plasma levels decreased after playing the scary game (P<0.05 for both). Conclusion: We conclude that perceived stress considerably elevates among players of scary video games, which adversely affects the emotional and cognitive capabilities, possibly via the strength of synaptic connections, and dendritic thorn construction of the brain neurons among players. [ABSTRACT FROM AUTHOR]

• Introducing combined fuzzy firefly algorithm and random forest (named FFA-RF). • Collecting a dataset by utilizing FFA for offline clustering in different applications. • Performing RF to learn the behavioral pattern of FFA in proper cluster-head selection. • Applying the trained FFA-RF model for online clustering in new unseen WSNs. Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application-specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

Today, with the expansion of online social networks and their impact on various aspects of human life, investigating the interactions between users and identifying influential users for various advertising applications and accelerating or preventing the dissemination of information has been the focus of researchers. One of the fundamental researches is investigating the fact that the similarity of users’ characteristics along with their interests leads to new relationships in the friendship network, a concept known as homophily. The study of homophily can provide significant insight into the flow of information and behaviors in a community to analyze the formation of online communities. In recent years, the emergence of location-based social networks (LBSNs) has created massive datasets by sharing spatial and temporal information better than ever before. This issue enables researchers to analyze the behavioral patterns of users and their impact on their social connections and friends. Throughout the present paper, a framework is being defined to examine the effect of combining structural similarity and homophily in determining users’ social influence under two scenarios. The experiments simulate performance of nodes on three LBSNs: Gowalla, Foursquare, and Brightkite. By calculating the correlation coefficient for the similarity methods applied, it can be displayed that with the increase in homophily, the correlation of the proposed method and the social influence increases. A new measure of centrality is also introduced by using the topological structure of the user's communication network, such as the eigenvector centrality along with the values of friendship influence and the number of spatial movements of the user. The results show that our proposed centrality matches up to 85% with baseline methods. [ABSTRACT FROM AUTHOR]

Abstract: Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods. [Copyright &y& Elsevier]

Introduction: Computer games as an interactive media play a significant role in the cognitive and behavioral health of the players. Computer games have either positive or negative effects on cognitive indices among players. They also directly influence the lifestyle and quality of life of children, adolescents, and young adults. The present study aimed to evaluate the short-term effects of the brain teaser game on players. Methods: Among 45 male volunteers, 40 subjects with an average age of 20 years were recruited and divided into two groups: the experimental group and the control group. All required tests were conducted before and after the intervention (playing the game) on the experimental group. Also, the same tests were performed on the control group, in which the participants were not allowed to play the game. All participants completed a questionnaire comprised demographic characteristics and specific information regarding the game (e.g., game style and hours spent on playing the game). The saliva samples were collected to measure levels of cortisol and α-amylase. The salivary α-amylase (sAA) and cortisol levels were analyzed using the relevant ELISA kits. The cognitive tests were performed using PASAT software before and after the game to assess the perceptual-cognitive abilities of the players. The brain waveforms were acquired by a 14-channel Emotiv brain signal recording device before and after the game. Data analysis was conducted in R and MATLAB software. Results: PASAT test suggested that mental health and sustained attention were significantly improved after the intervention. In addition, the sAA and salivary cortisol levels were significantly higher before the intervention. The results of the brainwave analysis revealed that stress index and attention were significantly higher before the intervention. Conclusion: Findings of the present study suggest that brain teaser games positively influence the central nervous system and activate stress path, leading to changes in brain signals and subsequently improved cognitive elements, such as attention among players. [ABSTRACT FROM AUTHOR]

Introduction: This research investigated the effects of violent and football video games on cognitive functions, cortisol levels, and brain waves. Methods: A total of 64 participants competed in a single-elimination tournament. Saliva samples of all players were obtained before and after the games for the assessment of cortisol levels. The cognitive performances of the players were also assessed by paced auditory serial addition test. Moreover, the electroencephalogram recording was conducted during the games. Results: The results showed that salivary cortisol levels significantly decreased after playing both games. Also, playing the football game increased reaction time, whereas decreased sustained attention and mental fatigue. Conclusion: Conversely, following playing a violent game, the reaction time decreased, and sustained attention and mental fatigue increased. Furthermore, the results of the EEG recording revealed that playing a violent game engaged more brain regions than the football game. In conclusion, playing violent game more effectively improved cognitive performances in the players than the football game. [ABSTRACT FROM AUTHOR]

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced through a process that dissociates it from its compounds. Several methods are used for hydrogen production, which first of all differ in the energy used in this process. Investigating the viability and exact applicability of a method in a specific context requires accurate knowledge of the parameters involved in the method and the interaction between these parameters. This can be done using top-down models relying on complex mathematically driven equations. However, with the raise of computational intelligence (CI) and machine learning techniques, researchers in hydrology have increasingly been using these methods for this complex task and report promising results. The contribution of this study is to investigate the state of the art CI methods employed in hydrogen production, and to identify the CI method(s) that perform better in the prediction, assessment and optimization tasks related to different types of Hydrogen production methods. The resulting analysis provides in-depth insight into the different hydrogen production methods, modeling technique and the obtained results from various scenarios, integrating them within the framework of a common discussion and evaluation paper. The identified methods were benchmarked by a qualitative analysis of the accuracy of CI in modeling hydrogen production, providing extensive overview of its usage to empower renewable energy utilization. [ABSTRACT FROM AUTHOR]

Introduction: Video games are common cultural issues with great influence in all societies. One of the important cognitive effects of video games is on creating stress on video players. The present research objective was to study different types of stress in players based on video game styles. Methods: A total of 80 players, aged 18 to 30 years, played four types of video games; Runner game, Excitement game, Fear game, and Puzzle game. In the beginning, the players filled in the form of personal information as well as some general and specialized information on the games. Before starting each game, the saliva samples of the players were collected to measure their level of cortisol and a-amylase. At the end of each game, the same samples were collected again. The concentrations of cortisol and a-amylase were measured using a specialized kit and an ELISA device. In addition, the variations of brain waves were recorded by an Emotiv system. Finally, the data were analyzed in SPSS and Matlab system (after and before playing video game). Results: The research findings revealed that the salivary a-amylase concentration increased significantly after playing the Fear game, Runner game, and Excitement game and decreased significantly after playing the Puzzle game. Moreover, the concentration of salivary cortisol increased significantly after playing the Runner game, Excitement game, and Fear game and decreased significantly after playing the Puzzle game. The brain wave analysis also revealed that the level of stress experienced by playing Fear game was higher than the Excitement game. Conclusion: According to the research findings, video games can affect the stress system as well as the cognitive system of humans depending on the game style. In addition, the type and level of stress triggered in the players depend on the game style. [ABSTRACT FROM AUTHOR]

This research introduces a new strategy in cluster ensemble selection by using Independency and Diversity metrics. In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection. Although quality can improve the final results in cluster ensemble, it cannot control the procedures of generating basic results, which causes a gap in prediction of the generated basic results’ accuracy. Instead of quality, this paper introduces Independency as a supplementary method to be used in conjunction with Diversity. Therefore, this paper uses a heuristic metric, which is based on the procedure of converting code to graph in Software Testing, in order to calculate the Independency of two basic clustering algorithms. Moreover, a new modeling language, which we called as “Clustering Algorithms Independency Language” (CAIL), is introduced in order to generate graphs which depict Independency of algorithms. Also, Uniformity, which is a new similarity metric, has been introduced for evaluating the diversity of basic results. As a credential, our experimental results on varied different standard data sets show that the proposed framework improves the accuracy of final results dramatically in comparison with other cluster ensemble methods. [ABSTRACT FROM AUTHOR]

Introduction: Computer games have attracted remarkable attentions in general publics with different cultures and their effects are subject of research by cognitive neuroscientists. In the present study, possible effects of the game Fifa 2015 on cognitive performance, hormonal levels, and electroencephalographic (EEG) signals were evaluated in young male volunteers. Methods: Thirty two subjects aged 20 years on average participated mutually in playing computer game Fifa 2015. Identification information and general knowledge about the game were collected. Saliva samples from the contestants were obtained before and after the competition. Perceptive and cognitive performance including the general cognitive health, response delay, attention maintenance, and mental fatigue were measured using PASAT test. EEG were recorded during the play using EEG device and analyzed later using QEEG. Simultaneously, the players' behavior were recorded using a video camera. Saliva cortisol levels were assessed by ELISA kit. Data were analyzed by SPSS program. Results: The impact of playing computer games on cortisol concentration of saliva before and after the game showed that the amount of saliva plasma after playing the game has dropped significantly. Also the impact of playing computer games on mental health, before and after the game indicated that the number of correct answers has not changed significantly. This indicates that sustained attention has increased in participants after the game in comparison with before that. Also it is shown that mental fatigue measured by PASAT test, did not changed significantly after the game in comparison to before that. The impact of game on changes in brain waves showed that the subjects in high activity state during playing the game had higher power of the EEG signals in most of the channels in lower frequency bands in compared to normal state. Discussion: The present study showed that computer games can positively affect the stress system and the perceptual-cognitive system. Even though this impact was not significant in most cases, the changes in cognitive and hormonal test and also in brain waves were visible. Hence, due to the importance of this matter, it is necessary to create control systems in selecting the types of games for playing. [ABSTRACT FROM AUTHOR]

One of the important aspects of decision making and management in distributed systems is collecting accurate information about the available resources of the peers. The previously proposed approaches for collecting such information completely depend on the system's architecture. In the server-oriented architecture, servers assume the main role of collecting comprehensive information from the peers and the system. Next, based on the information about the features of the basic activities and the system, an exact description of the peers' status is produced. Accurate decisions are then made using this description. However, the amount of information gathered in this architecture is too large, and it requires massive processing. On the other hand, updating the information takes time, causing delays and undermining the validity of the information. In addition, due to the limitations imposed by the servers, such architecture is not scalable and dynamic enough. The peer-to-peer architecture was introduced to address these concerns. However, due to a lack of complete knowledge of the peers and the system, the decisions are made without a precise description of the peers' status and are only based on the hardware data collected from the peers. Such an abstract and general image of the peers is not adequate for the purpose of decision making. In this paper, a 4-dimensional model is presented for the purpose of information collection and the exact description of the peer's status, including the features of the peer, the basic activity, the time, and the specifications of the system. The proposed model is for a server-oriented architecture, but it also adapts to the peer-to-peer serverless architecture. Based on this model, a new approach is introduced for information collection and an exact description of the peers' status in a peer-to-peer system based on the Latin square concept. We evaluate the model in the server-oriented and serverless situations. The workload is considered as the basic activity in our evaluation. Our evaluation demonstrates that in a server-oriented situation, increasing the size of the system has a direct relation with time. However, a serverless situation does not follow this behavior. [ABSTRACT FROM AUTHOR]