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The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many plans for improving hospital's service quality and investing in potential healthcare services in order to deliver better services with lower costs. Machine learning methods are often opted for prediction in HCA. In this paper, we propose a new method for HCA that uses genetic algorithm (GA) and artificial neural network (ANN). Operators of GA are used to boost up calculation to get optimal weights in the forward propagation of ANN. Experiments on a real database of Hanoi Medical University Hospital (HMUH) including calculus of kidney and ureter inpatients show that the new method achieves better accuracy than the relevant ones including linear regression, K-nearest neighbors (KNN), ANN and deep learning. The mean squared error of the proposed model gets the lowest value (0.00360), compared to those of deep learning, KNN and linear regression which are 0.00901, 0.01205 and 0.01718 respectively. [ABSTRACT FROM AUTHOR]

Fuzzy minimum spanning tree (FMST) has emerged from various real-life applications in different areas by considering uncertainty that exists in arc lengths of a fuzzy graph. In most relevant studies regarding FMST, type 1 fuzzy set was used to represent edge weights. Nonetheless, its membership values are totally crisp which is hard to determine its exact value by human perception. Interval type 2 fuzzy set (IT2FS) increases the number of degrees of freedom to express uncertainty of the edge weight and has more capacity to describe fuzzy information in a logically correct manner. In this paper, we propose the minimum spanning tree problem with undirected connected weighted interval type 2 fuzzy graph (FMST-IT2FS). Herein, the interval type 2 fuzzy set is used to represent the arc lengths of a fuzzy graph. Then, a new genetic algorithm is proposed to solve the FMST-IT2FS problem with the addition, ranking and defuzzification of IT2FSs being used. Illustrative examples are included to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis. [ABSTRACT FROM AUTHOR]

• We proposed a novel approach for image segmentation using neutrosophic sets. • Min-Max normalization was used to reduce uncertain noises. • Activation functions were applied for non-linearity in the images. • Membership functions were computed on different regions to form neutrosophic sets. • The proposed method was illustrated by numerical examples. This paper explores various properties of Neutrosophic sets (NS) and proposes a novel idea on Image Segmentation using NS. A theoretical Neutrosophic model is proposed to reduce uncertainty from missing data. Besides, we also tackle the problem of image segmentation with fewer assumptions. Min-Max Normalization is used to reduce any uncertain noise in an image due to a number of factors during image capturing. Next, we apply activation functions to resolve the non-linearity in the image followed by the computed membership functions. These sets are then transformed and compared with others to find similarities and dissimilarities. Neutrosophic Sets and Dice's Coefficients are fused to ensure proper evaluation of uncertainty of the missing data and their indeterminacy for image segmentation. The proposed method is experimentally validated. [ABSTRACT FROM AUTHOR]

Abstract Telecommunication is described by enormous settled, sunk, and irreversible speculation, which makes it high-hazard undertaking. The circumstance is additionally aggravated in the mobile part by progressive ages of innovation. The introduction of Mobile Virtual Network Operators (MVNOs) added value for both operators and customers; for the operators by using their available excess capacity and for the customers by offering innovative and several niche value-added services that were not offered by Mobile Network Operators (MNOs). However, mobile operators in developing nations are urban and semi-urban based, and very few operators provide services in rural areas. There are many un-served and under-served market segments still available. The major issue for developing nations is the formulation and implementation of policies, rules, and regulations. With an extensive variety of brands, companies are willing to enter the mobile business. An exclusively exploited radio access network by mobile network operators, new concepts and strategies are required to bring the opportunity for both telecom and non-telecom organizations to take part in the mobile sector. In this paper, we aim to develop a regulatory framework for MVNO in developing nations' telecommunication market as follows: first, to carry out a thorough analysis of market and draft a feasibility study for MVNOs in the telecommunications ("telecom") market in developing nations; and second, to develop required regulations for enhanced growth opportunities in the telecom market. This can be regarded as the effort for creating infrastructure for deploying e-Government in the developing countries. Highlights • We examined deployment of Mobile Virtual Network Operator in developing nations. • A new regulatory framework and guidelines for MVNO were proposed. • We analyzed the best global practices on MVNO. • MVNO was discussed in Telecom Market where international practices are zero level. • It is a reference document for the government to put the sector into priority. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on animal migration optimization (AMO), called ARM–AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM–AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated. [ABSTRACT FROM AUTHOR]

Shear strength of the soil is an important engineering parameter used in the design and audit of geo-technical structures. In this research, we aim to investigate and compare the performance of four machine learning methods, Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PANFIS), Genetic Algorithm - Adaptive Network based Fuzzy Inference System (GANFIS), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), for predicting the strength of soft soils. For this purpose, case studies of 188 plastic clay soil samples collected from two major projects, Nhat Tan and Cua Dai bridges in Viet Nam have been used for generating training and testing datasets for constructing and validating the models. Validation and comparison of the models have been carried out using RMSE, and R. The results show that the PANFIS has the highest prediction capability (RMSE = 0.038 and R = 0.601), followed by the GANFIS (RMSE = 0.04 and R = 0.569), SVR (RMSE = 0.044 and R = 0.549), and ANN (RMSE = 0.059 and R = 0.49). It can be concluded that out of four models the PANFIS indicates as a promising technique for prediction of the strength of soft soils. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor's outcome is formed by action of active agents while other agents' activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods' performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration. [ABSTRACT FROM AUTHOR]

Over the last few decades, the advance of new technologies in computer equipment, cameras and medical devices became a starting point for the shape of medical imaging systems. Since then, many new medical devices, e.g. the X-Ray machines, computed tomography scans, magnetic resonance imaging, etc., accompanied with operational algorithms inside has contributed greatly to successful diagnose of clinical cases. Enhancing the accuracy of segmentation, which plays an important role in the recognition of disease patterns, has been the focus of various researches in recent years. Segmentation using advanced fuzzy clustering to handle the problems of common boundaries between clusters would tackle many challenges in medical imaging. In this paper, we propose a new fuzzy clustering algorithm based on the neutrosophic orthogonal matrices for segmentation of dental X-Ray images. This algorithm transforms image data into a neutrosophic set and computes the inner products of the cutting matrix of input. Pixels are then segmented by the orthogonal principle to form clusters. The experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of clustering quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel semi-supervised fuzzy clustering algorithm with spatial constraints for dental segmentation from X-ray images. The detailed contributions include: i) Formulating the spatial features of a dental X-ray image in a dental feature database; ii) Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; iii) Solving the model by the Lagrange multiplier method; iv) Determining the additional information for clustering process by mixing optimal results of Fuzzy C-Means with spatial constraints; v) Proposing a novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation. The new algorithm is validated on a real dataset from Hanoi Medical University, Vietnam including 56 dental images. The experimental results reveal that the proposed work has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods. We also suggest the most appropriate values of parameters that should be opted for the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present an application of soft computing methods for the problem of WiMax Network Planning on 3D Geographical Information Systems (3D GIS) that optimizes both performance of the network (Coverage and Quality-of-Service) and investment costs (the number of base stations and sectors). A pre-processing procedure using latest results of parallel Random Forest classification algorithm to determine valid positions of base stations on a terrain of 3D GIS is proposed. Based upon those positions, we design a generalized mathematical model taking into account 3D obstacles in path loss calculation process. In order to generate optimal solutions of the model, a hybrid algorithm between greedy BTP and improved Particle Swarm Optimization incorporated with parallel computing is presented. Experimental validation of the proposed method in comparison with other relevant ones is performed. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is a useful segmentation tool which has been widely used in many applications in real life problems such as in pattern recognition, recommender systems, forecasting, etc. Fuzzy clustering algorithm on picture fuzzy set (FC-PFS) is an advanced fuzzy clustering algorithm constructed on the basis of picture fuzzy set with the appearance of three membership degrees namely the positive, the neutral and the refusal degrees combined within an entropy component in the objective function to handle the problem of incomplete modeling in fuzzy clustering. A disadvantage of FC-PFS is its capability to handle complex data which include mix data type (categorical and numerical data) and distinct structured data. In this paper, we propose a novel picture fuzzy clustering algorithm for complex data called PFCA-CD that deals with both mix data type and distinct data structures. The idea of this method is the modification of FC-PFS, using a new measurement for categorical attributes, multiple centers of one cluster and an evolutionary strategy – particle swarm optimization. Experiments indicate that the proposed algorithm results in better clustering quality than others through clustering validity indices. [ABSTRACT FROM AUTHOR]

Fuzzy clustering plays an important role in pattern recognition and knowledge discovery. Recently, there has been a great interest of developing fuzzy clustering algorithms on advanced fuzzy sets such as Picture Fuzzy Clustering (FC-PFS) which is an extension of Fuzzy C-Means on Picture Fuzzy Set. A major disadvantage of FC-PFS is how to define a prior number of clusters before clustering. Because each dataset has distinctive features and distributions of patterns, determining such the number for a clustering algorithm would result in good quality. In this paper, we propose a method called Automatic Picture Fuzzy Clustering (AFC-PFS) for determining the most suitable number of clusters for FC-PFS. It is a hybrid method between Particle Swarm Optimization (PSO) and FC-PFS where combined solutions consisting of the number of clusters and equivalent clustering centers and membership matrices are packed and optimized in PSO. A new term namely Picture Composite Cardinality is also given to determine a suitable number of clusters. AFC-PFS is empirically validated on benchmark datasets of UCI Machine Learning Repository by different clustering quality indices. The results show that AFC-PFS has better performance than the relevant methods. [ABSTRACT FROM AUTHOR]

The Recommender System (RS) is an efficient tool for decision makers that assists in the selection of appropriate items according to their preferences and interests. This system has been applied to various domains to personalize applications by recommending items such as books, movies, songs, restaurants, news articles and jokes, among others. An important issue for the RS that has greatly captured the attention of researchers is the new user cold-start problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, we first present a classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format. Next, some typical algorithms of these groups, such as MIPFGWC-CS, NHSM, FARAMS and HU–FCF, are described. Finally, these algorithms are implemented and validated on some benchmark RS datasets under various settings of the new user cold start. The experimental results indicate that NHSM achieves better accuracy and computational time than the relevant methods. [ABSTRACT FROM AUTHOR]

Municipal Solid Waste (MSW) collection is a necessary process in any municipality resulting in the quality-of-life, economic aspects and urban structuralization. The intrinsic nature of MSW collection relates to the development of effective vehicle routing models that optimize the total traveling distances of vehicles, the environmental emission and the investment costs. In this article, we propose a generalized vehicle routing model including multiple transfer stations, gather sites and inhomogeneous vehicles in time windows for MSW collection. It takes into account traveling in one-way routes, the number of vehicles per m 2 and waiting time at traffic stops for reduction of operational time. The proposed model could be used for scenarios having similar node structures and vehicles’ characteristics. A case study at Danang city, Vietnam is given to illustrate the applicability of this model. The experimental results have clearly shown that the new model reduces both total traveling distances and operational hours of vehicles in comparison with those of practical scenarios. Optimal routes of vehicles on streets and markets at Danang are given. Those results are significant to practitioners and local policy makers. [ABSTRACT FROM AUTHOR]

Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications. [ABSTRACT FROM AUTHOR]

The health care support system is a special type of recommender systems that play an important role in medical sciences nowadays. This kind of systems often provides the medical diagnosis function based on the historic clinical symptoms of patients to give a list of possible diseases accompanied with the membership values. The most acquiring disease from that list is then determined by clinicians’ experience expressed through a specific defuzzification method. An important issue in the health care support system is increasing the accuracy of the medical diagnosis function that involves the cooperation of fuzzy systems and recommender systems in the sense that uncertain behaviors of symptoms and the clinicians’ experience are represented by fuzzy memberships whilst the determination of the possible diseases is conducted by the prediction capability of recommender systems. Intuitionistic fuzzy recommender systems (IFRS) are such the combination, which results in better accuracy of prediction than the relevant methods constructed on either the traditional fuzzy sets or recommender system only. Based upon the observation that the calculation of similarity in IFRS could be enhanced by the integration with the information of possibility of patients belonging to clusters specified by a fuzzy clustering method, in this paper we propose a novel hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis so-called HIFCF (Hybrid Intuitionistic Fuzzy Collaborative Filtering). Experimental results reveal that HIFCF obtains better accuracy than IFCF and the standalone methods of intuitionistic fuzzy sets such as De, Biswas & Roy, Szmidt & Kacprzyk, Samuel & Balamurugan and recommender systems, e.g. Davis et al. and Hassan & Syed. The significance and impact of the new method contribute not only the theoretical aspects of recommender systems but also the applicable roles to the health care support systems. [ABSTRACT FROM AUTHOR]

Recommender system (RS) is a special type of information systems that assists decision makers to choose appropriate items according to their preferences and interests. It is utilized in different domains to personalize its applications by recommending items, such as books, movies, songs, restaurants, news articles, jokes, among others. An important issue in RS namely the new user cold-start problem occurring when a new user migrates to the system has grasped a great attraction of researchers in recent years. Existing researches are faced with the limitations of the relied dataset, the determination of the optimal number of clusters, the similarity metric, irrelevant users and the selection of membership values. In this paper, we present a novel hybrid method so-called HU-FCF++ to deal with these drawbacks by considering the integration of existing state-of-the-arts of several groups of methods in order to combine the advantages of different groups and eliminate their disadvantages by some special procedures. A numerical example on a simulated dataset is given to illustrate the activities of the proposed approach. Experimental validation on the benchmark RS datasets show that HU-FCF++ achieves better accuracy than the relevant methods. [ABSTRACT FROM AUTHOR]

Medical diagnosis has been being considered as one of the important processes in clinical medicine that determines acquired diseases from some given symptoms. Enhancing the accuracy of diagnosis is the centralized focuses of researchers involving the uses of computerized techniques such as intuitionistic fuzzy sets (IFS) and recommender systems (RS). Based upon the observation that medical data are often imprecise, incomplete and vague so that using the standalone IFS and RS methods may not improve the accuracy of diagnosis, in this paper we consider the integration of IFS and RS into the proposed methodology and present a novel intuitionistic fuzzy recommender systems (IFRS) including: (i) new definitions of single-criterion and multi-criteria IFRS; (ii) new definitions of intuitionistic fuzzy matrix (IFM) and intuitionistic fuzzy composition matrix (IFCM); (iii) proposing intuitionistic fuzzy similarity matrix (IFSM), intuitionistic fuzzy similarity degree (IFSD) and the formulas to predict values on the basis of IFSD; (iv) a novel intuitionistic fuzzy collaborative filtering method so-called IFCF to predict the possible diseases. Experimental results reveal that IFCF obtains better accuracy than the standalone methods of IFS such as De et al., Szmidt and Kacprzyk, Samuel and Balamurugan and RS, e.g. Davis et al. and Hassan and Syed. [ABSTRACT FROM AUTHOR]

Fuzzy clustering is considered as an important tool in pattern recognition and knowledge discovery from a database; thus has been being applied broadly to various practical problems. Recent advances in data organization and processing such as the cloud computing technology which are suitable for the management, privacy and storing big datasets have made a significant breakthrough to information sciences and to the enhancement of the efficiency of fuzzy clustering. Distributed fuzzy clustering is an efficient mining technique that adapts the traditional fuzzy clustering with a new storage behavior where parts of the dataset are stored in different sites instead of the centralized main site. Some distributed fuzzy clustering algorithms were presented including the most effective one – the CDFCM of Zhou et al. (2013). Based upon the observation that the communication cost and the quality of results in CDFCM could be ameliorated through the integration of a distributed picture fuzzy clustering with the facilitator model, in this paper we will present a novel distributed picture fuzzy clustering method on picture fuzzy sets so-called DPFCM. Experimental results on various datasets show that the clustering quality of DPFCM is better than those of CDFCM and relevant algorithms. [ABSTRACT FROM AUTHOR]

Municipal Solid Waste (MSW) is an increasing concern at any municipality in the world, and is one of the primary factors that contribute greatly to the rising of climate change and global warming. MSW collection and disposal especially in the context of developing countries are indeed the urgent requirements for the sustainable development of environment and landscape, which rule over the quality-of-life and life expectancy of human being. In this paper, we concentrate on MSW collection at Danang city, which is one of four largest municipalities in Vietnam having high quantity of the average waste load per person and is bearing negative impacts of climate change such as severe weather conditions and natural disasters as a result. A novel vehicle routing model for the MSW collection problem at Danang city is presented. A novel hybrid method between Chaotic Particle Swarm Optimization and ArcGIS is proposed to generate optimal solutions from the vehicle routing model of Danang. Experimental results on the real dataset of Danang show that the proposed hybrid method obtains better total collected waste quantity than the relevant ones including the manual MSW collection procedure that is currently applied at this city. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an integrated approach between fuzzy C-means (FCM) and multi-active neuro fuzzy inference system (MANFIS) for the lossless DEM compression for fast retrieval (DCR) problem, aiming to compress digital elevation model (DEM) data with the priority of fast retrieval from the client machine over the Internet environment. Previous researches of this problem either used the float wavelet transforms integrated with the SPIHT coding or constructed a predictor model using statistical correlation of DEM data in local neighborhoods; thus giving large-sized compressed data and slow transferring time of data between the server and the client. Based on the observation that different non-linear transforms for predictive values in the sliding windows may increase the compression ratio, we herein present a novel approach for DCR problem and validated it experimentally on the benchmark DEM datasets. The comparative results show that our method produces better compression ratio than the relevant ones. [Copyright &y& Elsevier]

Highlights: [•] We proposed a novel modification model for geo-demographic analysis. [•] Some important theorems and properties of this model were studied. [•] We integrated the model to fuzzy clustering to form a new algorithm. [•] It was validated through experiments on both small and real datasets. [•] The clustering quality of our method is better than that of relevant ones. [ABSTRACT FROM AUTHOR]

Abstract: Geo-Demographic Analysis (GDA) is an important tool to explore the underlying rules that regulate our world, and therefore, it has been widely applied to the development of effective socio-economic policies through the analysis of data generated from Geographic Information Systems (GIS). In GDA applications, clustering plays a major role however, the current state-of-the-art algorithms, namely the Fuzzy Geographically Weighted Clustering (FGWC), have demonstrated several limitations both in terms of speed and in terms of quality of the achieved results. Accordingly, in this paper, we propose a novel clustering algorithm for GDA application, based on recent results regarding intuitionistic fuzzy sets and the possibilistic fuzzy C-means, that aims at overcoming some of the limitations of the existing methods. [Copyright &y& Elsevier]

There have been various researches regarding routing in wireless sensor networks for many years, but underground wireless communication in complex environments like soil-air or water-air is a new and tough problem for researchers. Replacement of the battery of sensor nodes is already a challenging task under normal conditions. However, the difficulty increases significantly when deploying them in environments like cultivation soils; therefore maintaining the network lifetime as well as the total number of living nodes in such an adverse setting is a challenging task for researchers. In this paper, we propose a new energy-efficient routing model in wireless underground sensor networks (WUSN) that considers the path loss energy of the energy model and the energy efficiency of the total network. The proposed FCM-WUSN model is compared against the well-known fuzzy routing method namely FCM in terms of network lifetime, energy consumption, total node alive, and underground node alive. It has been observed that the network lifetime of the proposed method is 1.5 × longer than that of the FCM, and the number of alive nodes including the underground nodes are 1.6 × higher than that of the FCM. The results are better even when running data sets with more sensors in the network and sensor positioning based on the depth of sensors deployed underground. [ABSTRACT FROM AUTHOR]

In the original article, family name of one of the co-authors (Duong Thi Thu Huyen) has been missed in the online publication. [ABSTRACT FROM AUTHOR]

One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.0 needs dynamic network connectivity with higher amounts of speed and bandwidth. In order to support multiple users for video calling or conferencing with high-speed transmission rates and low packet loss, 4G technology was introduced by the 3G Partnership Program (3GPP). 4G LTE is a type of 4G technology in which LTE stands for Long Term Evolution, followed to achieve 4G speeds. 4G LTE supports multiple users for downlink with higher-order modulation up to 64 quadrature amplitude modulation (QAM). With wide coverage, high reliability and large capacity, LTE networks are widely used in Industry 4.0. However, there are many kinds of equipment with different quality of service (QoS) requirements. In the existing LTE scheduling methods, the scheduler in frequency domain packet scheduling exploits the spatial, frequency, and multi-user diversity to achieve larger MIMO for the required QoS level. On the contrary, time-frequency LTE scheduling pays attention to temporal and utility fairness. It is desirable to have a new solution that combines both the time and frequency domains for real-time applications with fairness among users. In this paper, we propose a channel-aware Integrated Time and Frequency-based Downlink LTE Scheduling (ITFDS) algorithm, which is suitable for both real-time and non-real-time applications. Firstly, it calculates the channel capacity and quality using the channel quality indicator (CQI). Additionally, data broadcasting is maintained by using the dynamic class-based establishment (DCE). In the time domain, we calculate the queue length before transmitting the next packets. In the frequency domain, we use the largest weight delay first (LWDF) scheduling algorithm to allocate resources to all users. All the allocations would be taken placed in the same transmission time interval (TTI). The new method is compared against the largest weighted delay first (LWDF), proportional fair (PF), maximum throughput (MT), and exponential/proportional fair (EXP/PF) methods. Experimental results show that the performance improves by around 12% compared with those other algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders' satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders' evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders' rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features' priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods. [ABSTRACT FROM AUTHOR]

Software defect prediction has been one of the key areas of exploration in the domain of software quality. In this paper, we perform a systematic mapping to analyze all the software defect prediction literature available from 1995 to 2018 using a multi-stage process. A total of 156 studies are selected in the first step, and the final mapping is conducted based on these studies. The ability of a model to learn from data that does not come from the same project or organization will help organizations that do not have sufficient training data or are going to start work on new projects. The findings of this research are useful not only to the software engineering domain, but also to the empirical studies, which mainly focus on symmetry as they provide steps-by-steps solutions for questions raised in the article. [ABSTRACT FROM AUTHOR]

The vague graph has found its importance as a closer approximation to real life situations. A review of the literature in this area reveals that the edge coloring problem for vague graphs has not been studied until now. Therefore, in this paper, we analyse the concept of vertex and edge coloring on simple vague graphs. Specifically, two new definitions for vague graphs related to the concept of the λ-strong-adjacent and ζ-strong-incident of vague graphs are introduced. We consider the color classes to analyze the coloring on the vertices in vague graphs. The proposed method illustrates the concept of coloring on vague graphs, using the definition of color class, which depends only on the truth membership function. Applications of the proposal in solving practical problems related to traffic flow management and the selection of advertisement spots are mainly discussed. [ABSTRACT FROM AUTHOR]

The Neutrosophic set (NS) has grasped concentration by its ability for handling indeterminate, uncertain, incomplete, and inconsistent information encountered in daily life. Recently, there have been various extensions of the NS, such as single valued neutrosophic sets (SVNSs), Interval neutrosophic sets (INSs), bipolar neutrosophic sets (BNSs), Refined Neutrosophic Sets (RNSs), and triangular fuzzy number neutrosophic set (TFNNs). This paper contains an extended overview of the concept of NS as well as several instances and extensions of this model that have been introduced in the last decade, and have had a significant impact in literature. Theoretical and mathematical properties of NS and their counterparts are discussed in this paper as well. Neutrosophic-set-driven decision making algorithms are also overviewed in detail. [ABSTRACT FROM AUTHOR]

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of complex evolutionary artificial intelligence in cognitive digital twinning. [ABSTRACT FROM AUTHOR]

In this study, a new dynamic fuzzy group recommender system (DFGRS) is investigated, which aims to deal with some critical challenges in the group recommender system (GRS), including the user preference uncertainty, item attractiveness tendency, and group members’ interaction and fairness. The proposed approach of the DFGRS applies intuitionistic fuzzy sets (IFSs) to represent user preferences on the considered items by taking into account user hesitancy and inconsistency in ratings. It also incorporates a novel dynamic similarity mechanism for taking into account users’ preference tendency and item attractiveness in predicting unknown user-item preferences. The model’s parameters are optimized using a modified Bayesian method. This combination creates a more realistic and concise prediction of unknown user ratings. Then, to form a balanced solution in the consensus phase of the DFGRS, an intuitionistic fuzzy Choquet integral aggregation operation (IFCAO) is suggested. This approach enables the DFGRS to generate recommendations that balance between optimal average group users’ preferences and fairness among them. Furthermore, the proposed IFCAO includes the group members’ interaction impact due to the proposed capacity function. In addition, several other intuitionistic fuzzy aggregation operations can be utilized that inherit features of common aggregation strategies defined for a traditional GRS. The experimental results in this paper demonstrate the performance of various aggregation approaches for the consensus phase of DFGRS. The obtained performance analysis results highlight the advantage and the potential of the DFGRS based on the proposed Choquet integral aggregation operation. Overall, the proposed DFGRS presents a novel approach for addressing challenges in group recommendation and could have important practical applications in various areas. [ABSTRACT FROM AUTHOR]

Soft set is the power tool to deal with uncertainty in a parametric manner. In applications of soft set, one of the most important steps is to define mappings on soft sets. In this study, we model theory of game under theory of soft set which is an effective tool for handling uncertainties events and problems that may exist in a game. To this end, we first define some expected impact functions of players in soft games. Then, we propose three new decision making algorithms to solve the 2.2 × p, 2. n × p and m. 2 × p soft matrix games, which cannot be settled by the relevant soft methods such as saddle points, lover and upper values, dominated strategies and Nash equilibrium. The proposed soft game algorithms are illustrated by examples. [ABSTRACT FROM AUTHOR]

Picture fuzzy graph (PFG) is an extended version of intuitionistic fuzzy graph (IFG) to model the uncertain real world problems, in which IFG may fail to model those problems properly. PFG is more precise, flexible and compatible than IFG to deal the real-life scenarios which consists of information these types: yes, abstain, no and refusal. The main focus of our study is to present the concept of isomorphic PFG, regular PFG (RPFG) and picture fuzzy multigraph. In this paper, we present the notation of RPFG. Many different types of RPFGs such as regular strong PFG, regular complete PFG, complete bipartite PFG and regular complement PFG are introduced. We also describe the concepts of dn and tdn-degree of a vertex in a RPFG. Based on those two types of degrees, we classify the regularity of PFG into 3 type's namely, dn- RPFG, tdn-RPFG and n- highly irregular PFG. Several theorems of those RPFG are presented here. We define the busy vertex and free vertex in a RPFG. We present the notations of μ-complement, homomorphism, isomorphism, weak isomorphism and co weak isomorphism of RPFG. Some significant theorems on isomorphism and μ- complement of RPFG are derived here. We also introduce the notation of picture fuzzy multigraph. We present a mathematical model of communication network and transportation network by using picture fuzzy multigraph and real time data are collected so that the transportation network/communication network can work efficiently. [ABSTRACT FROM AUTHOR]

The uncertainty principle is one of the most important features in modeling and solving linear time-invariant (LTI) systems. The neutrality phenomena of some factors in real models have been widely recognized by engineers and scientists. The convenience and flexibility of neutrosophic theory in the description and differentiation of uncertainty terms make it take advantage of modeling and designing of control systems. This paper deals with the controllability and stabilizability of LTI systems containing neutrosophic uncertainty in the sense of both indeterminacy parameters and functional relationships. We define some properties and operators between neutrosophic numbers via horizontal membership function of a relative-distance-measure variable. Results on exponential matrices of neutrosophic numbers are well-defined with the notion e tA deployed in a series of neutrosophic matrices. Moreover, we introduce the concepts of controllability and stabilizability of neutrosophic systems in the sense of Granular derivatives. Sufficient conditions to guarantee the controllability of neutrosophic LTI systems are established. Some numerical examples, related to RLC circuit and DC motor systems, are exhibited to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms. [ABSTRACT FROM AUTHOR]

• An enhanced Ripplet transform based compression technique is proposed in this work. • Singular Value Decomposition is used in this work for prediction. • An approximate 5%–25% improvement in PSNR is achieved with this proposed work. Ripplet transforms provide efficient representation of images with all the spatial details. Since more details are extracted, the compression ratio is usually less. In this work, lossless prediction and lossy singular value decomposition of the ripplet coefficients are carried out to improve the performance of conventional ripplet transform based system. Initially, lossless prediction exploits the correlation between the image pixels and eases the process of transformation. Fast and computationally efficient randomized singular value decomposition technique is used to capture the essential information of the high frequency ripplet coefficients. The low frequency and the high frequency components are then encoded using entropy method. Experimental results show promising results for the proposed approached in terms of performance measure. [ABSTRACT FROM AUTHOR]

In cloud computing environment, cross-cloud live migration of virtual machines (VMs) is a major concern in these days. Cloud computing provides the users with huge, versatile and on-demand access to a bulk of customizable and configurable registered physical devices or things. It helps organizations or enterprises to share data efficiently by privately owned cloud or by the third-party servers. This type of sharing of bulky data through cloud is more efficient and reliable. In an enterprise environment, one of the essential capabilities of cloud infrastructure is VM migration. VM live migration basically involves the transference of instances that includes the operating system, runtime memory pages and active CPU states from source hub to the destination hub. In this paper, we have discussed on resource allocation algorithm which performs better in utilization of CPU, time and memory. Our proposed algorithm deals with the effective utilization of unoccupied memory, and we have also measured VM memory stack flow of total memory for cloud computing architecture. [ABSTRACT FROM AUTHOR]

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to distinguish power quality events based on the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). We examine energy quality events such as sag, interruptions, swell, harmonic, transient, notch and flicker. The proposed method calculates numerous power quality disturbances such as flickering with harmonics, intrusion with harmonics, and sagging with harmonics. It has less processing time than the previous methods due to multiple events occurring at same time. Numerical experiments performed on a real database of power quality disturbances show that there is less calculation in the proposal in comparison with the wavelet change, S-transform and Hilbert change. Recognition with the assistance of HOG gives better and precise outcome in time area with faster reaction. [ABSTRACT FROM AUTHOR]

Human emotion analysis is one of the challenging tasks in today's scenario. The success rate of human emotion recognition has high implication in practical applications such as Human Machine Interaction, anomaly detection, surveillance, etc. Artificial Neural Networks (ANN) is one of the highly favored computational intelligence techniques for human emotion recognition. However, the performance of traditional ANN is not satisfactory in case of applications such as human emotion analysis. This leads to the necessity of modified ANN with better performance than the conventional systems. In this paper, we propose Circular Back Propagation Neural Network (CBPN) and Deep Kohonen Neural Network (DKNN) to overcome drawbacks of the traditional neural networks regarding computational complexity and accuracy. Performance of the proposals is explored in classifying different emotions of humans using Electroencephalography (EEG) signals. It has been validated that the proposals have better performance than the related methods. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

Identification and repairing holes in ireless sensor networks (WSNs) are challenging with full and low complexity. A local healing approach based on distributed virtual forces will make the healing area of the hole possible. The construction of repairing design is a locally allocated and constructed methodology that will enhance the coverage area in WSNs. In this paper, an enhanced border and hole detection (EBHD) algorithm is proposed for the energy containment of WSN. The performance of the proposed EBHD methodology is compared with those of the existing methods through Network Simulator 2 (NS2). The simulation results prove that the proposed EBHD achieves a high packet reception ratio while improving the residual energy and reducing end-to-end delay. [ABSTRACT FROM AUTHOR]

Periapical Inflammation (PI) is one of the most popular diseases in adults due to complication of endodontitis or dental trauma with corresponding consequences to quality-of-life like tiredness and signs of infection. Specifically, patients having severe PI are often tiredness and high fever accompanied by signs of infection such as dry lips, dirty tongue, lymph node reaction in the area under the jaw. In X-Ray images, PI could be recognized by vague boundaries with signs of periapical ligament extensions. It is necessary to design a computerized diagnosis system based on the Deep Learning models for supporting clinicians in diagnosis of PI from X-Ray images. In this paper, we propose a new medical system called VNU for diagnosis of PI from X-Rays images. The VNU system uses Deep Learning to classify whether X-Ray images being PI or not. The Residual Neural Network (ResNet) with 34 layers is utilized with proper data augmentation and learning algorithms. The system is designed based on 7-layer enterprise architecture (User, Business, Application, Data, Technology, Infrastructure, and Security). It is used by both the clinicians and IT operators. The system has been validated on real data from Hanoi Medical University, Vietnam consisting of 900 images with PI and 500 normal images. Two scenarios of validation namely hyperparameter selection and performance comparison with other CNN-based Deep Learning models have been performed. It has been found from the experiments that the proposed system has better performance than the others in terms of sensitivity and specificity with the corresponding values of 96.70% and 93.87%. The system is deployed on the web interface that offers flexibility for clinicians in diagnosis and training. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply. [ABSTRACT FROM AUTHOR]