Journal of Intelligent & Fuzzy Systems - Volume 38, issue 3

Authors: Najib, Fatma M. | Ismail, Rasha M. | Badr, Nagwa L. | Gharib, Tarek F.

Article Type: Research Article

Abstract: Recent applications such as sensor networks generate continuous and dynamic data streams. Data streams are often gathered from multiple data sources with some incompleteness. Clustering such data is constrained by incompleteness of data, data distribution, and continuous nature of data streams. Ignoring missing values in incomplete data clustering, especially in high missing rates decreases the clustering performance. Traditional clustering is applied on the whole data without dealing with data distribution. This paper presents an efficient framework called Fuzzy c-means clustering for Incomplete Data streams (FID) that works adaptively with incomplete data streams even with high missing rates. The proposed FID …estimates missing values based on the corresponding nearest-neighbors’ intervals. To overcome the previously mentioned data streams clustering problems, the continuous clustering mechanism is adopted and extended to accurately handle the incomplete data streams. Experimental results using two different data sets prove the efficiency of the proposed FID comparing to the alternative approaches. Show more

Keywords: Data streams, incomplete data clustering, fuzzy clustering, nearest neighbor rule

DOI: 10.3233/JIFS-191184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3213-3227, 2020

Authors: Sánchez, Daniela | Melin, Patricia | Castillo, Oscar

Article Type: Research Article

Abstract: In this paper dynamic parameter adjustment in particle swarm optimization (PSO) for modular neural network (MNN) design using granular computing and fuzzy logic (FL) is proposed. Nowadays, there are a plethora of optimization techniques, but their implementations require having knowledge about these techniques in order to establish their parameters, because the performance and final results of a particular technique depend on the optimal parameter values. For this reason, in this paper the fuzzy adjustment of parameters during the execution is proposed, and this proposal allows to adjust the parameters depending on current PSO behavior in each iteration. The proposed method …performs modular neural network optimization applied to human recognition using benchmark ear, iris and face databases. Two fuzzy inference systems are proposed to perform this dynamic adjustment, comparisons against a PSO without this dynamic adjustment (simple PSO) are performed to verify if the proposed adjustment using a fuzzy system is better improving recognition rate and execution time. The PSO variants presented in this paper are aimed at performing MNNs optimization. This optimization consists on finding optimal parameters, such as: the number of modules (or sub granules), percentage of data for the training phase, learning algorithm, goal error, number of hidden layers and their number of neurons. Show more

Keywords: Modular neural networks, granular computing, particle swarm optimization, fuzzy adaptation, human recognition, ear recognition, iris recognition, face recognition, pattern recognition

DOI: 10.3233/JIFS-191198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3229-3252, 2020

Authors: Ko, Jung Mi | Kim, Yong Chan

Article Type: Research Article

Abstract:  We introduce the concepts of fuzzy join complete lattices and Alexandrov L -pretopologies in complete residuated lattices. We show that fuzzy join complete lattices, Alexandrov L -pretopologies, fuzzy meet complete lattices and Alexandrov L -precotopologies are equivalent. Moreover, we define L -preinterior operators (resp. L -preclosure operators) as a viewpoint of fuzzy joins (resp. fuzzy meet) and fuzzy rough sets. Furthermore their properties and examples are investigated.

Keywords: Complete residuated lattices, fuzzy join(meet)-complete lattices Alexandrov L-pre(co)topologies, fuzzy rough sets

DOI: 10.3233/JIFS-191344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3253-3266, 2020

Authors: Gu, Jinheng | Liu, Changqing | Fu, Shenghui | Mao, Enrong | Pang, Changle

Article Type: Research Article

Abstract: Service-oriented design is used in product development to accommodate diverse customer requirements and provide a profit-making strategy. Existing designs encounter difficulties in assessing design alternatives systematically during conceptual design involving customer heterogeneity and cognition vagueness. To evaluate these alternatives, a new systematic service-oriented design is proposed. The fuzzy analytic hierarchy process is used to handle the subjectivity and uncertainty of expert judgments and customer desires. In addition, the structure of service-oriented design within a mapping information flow is illustrated and then associated with technical characteristics via the results of the House of Quality. A consideration of influential design factors is …developed to identify optimal alternative on the basis of the PageRank algorithm. Based on the integrated methods, a priority index is proposed to evaluate these alternatives, which can flexibly handle customer heterogeneity under limited technical conditions. At the same time, a design calculation program of a front axle suspension system was developed based on MATLAB GUI, which shows the design extensibility and robustness of the proposed approach. Overall, the results of the priority index-based method clearly demonstrate the superiority and appropriateness of the technique in selecting the optimal alternative. It also standardizes the design process from the case study of the front axle suspension system, provides rapid reasonable selection of the design scheme, and thereby improving intelligent design capacity from the perspective of product and its services. Show more

Keywords: Service-oriented design, fuzzy analytic hierarchy process, PageRank, MATLAB GUI, influential design factors

DOI: 10.3233/JIFS-191499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3267-3284, 2020

Authors: Hashemi, S. Ahmad | Farrokhi, Hamid

Article Type: Research Article

Abstract: Self-Organization networking (SON) consists of function sets which are responsible for automatically reliable configuring, planning and optimizing next generation mobile networks. Effective self-organization functions improve the level of network key performance indicators by determining optimal network setting and continuously finding efficient solutions that will be very hard for experts to distinguish. Most current self-organization networking functions apply rule-based recommended systems to control network resources in which performance metrics are evaluated and the effective actions are performed in accordance with a set of command sequences which such algorithms are too complicated to design, because rules and command sequences should be derived …for each target index during each possible scenario. This research has proposed cognitive wireless networks as a fully intelligent approach to self-organization networking. We generalize the concept of network automation considering fuzzy-based self-organization networking functions as Q-learning problems in which, a framework is described to find the fuzzy optimal solution of linear programming optimization problem. The achieved results prove that the proposed cognitive approach, provides a prominent cellular framework for developing self-organization solutions, particularly where the relevance of metrics to the control indices is not clearly known. Also, assessment of the scheme in multiple-speed scenarios revealed that Q-learning load balancing obtains more accurate results compared to rule-based adaptive load balancing methods. This is particularly correct in dynamic networks, with high-speed users. Show more

Keywords: Next-generation mobile networks, reinforcement learning, handover optimization, load balancing, network automation

DOI: 10.3233/JIFS-191558

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3285-3300, 2020

Authors: Nilofer, Ms. | Rizwanullah, Mohd.

Article Type: Research Article

Abstract: In this paper a simplification of the CPP is measured, a set of important nodes is given in a linear order and the work is to path all edges at least once in such a technique that the advanced priority nodes are stayed as soon as possible. All roads in the networks are covered by postman tour.postman tour covering all the roads in the network. The solutions found here are valid for the case, where the cost of additional edges traversed is much bigger that the cost of delays and delay for the first priority node is much bigger than …the cost of delay for the second node, and so on. More detailed study of various cost functions may be an interesting topic for future research. Show more

Keywords: Graph, Eulerian tour, road network, higher priority node

DOI: 10.3233/JIFS-190035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3301-3305, 2020

Authors: Peng, Xindong | Ma, Xueling

Article Type: Research Article

Abstract: Pythagorean fuzzy set (PFS), as a generalization of intuitionistic fuzzy set (IFS), is more suitable to capture the indeterminacy of the experts’ decision making information. This paper is designed to build new algorithm for managing multi-criteria decision making (MCDM) issue under Pythagorean fuzzy environment. First, we initiate a novel score function based Pythagorean fuzzy number (PFN). Later, we explore an algorithm for solving MCDM problem based on CODAS (COmbinative Distance-based ASsessment). Ultimately, the availability of method is stated by some numerical examples. The dominating traits of the developed algorithm, compared to some existing Pythagorean fuzzy decision making algorithms, are (1) …derive a ranking without the complex process; (2) achieve the optimal alternative without counterintuitive phenomena; (3) strong ability to differentiate the optimal alternative. Show more

Keywords: Pythagorean fuzzy number, CODAS, score function, MCDM

DOI: 10.3233/JIFS-190043

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3307-3318, 2020

Authors: Jin, Liying | Zhao, Shengdun | Zhang, Congcong | Gao, Wei | Dou, Yao | Lu, Mengkang

Article Type: Research Article

Abstract: For the uncertain problem that between-cluster distance influences clustering in the soft subspace clustering (SSC) process, a novel clustering technique called adaptive soft subspace clustering (ASSC) is proposed by employing both within-cluster and between-cluster information. First, a new objective function is constructed by minimizing the within-cluster compactness and maximizing the between-cluster distance based on the framework of SSC algorithm. Based on this objective function, a new way of computing clusters’ feature weights, centers and membership is then derived by using Lagrange multiplier method. The uniqueness of ASSC is that the objective function does not increase any control parameters, which can …avoid the sensitivity of clustering results to the initial points of the control parameters. The properties of this algorithm are investigated and the performance is evaluated experimentally using UCI datasets. The contrastive experiment results demonstrate that the accuracy and the stability of the proposed algorithm outperform the four existing clustering algorithms, i.e., ESSC, EWKM, FWKM and CIM_QPSO_SSC. Show more

Keywords: Soft subspace clustering, within-cluster compactness, between-cluster distance, not increase any control parameters

DOI: 10.3233/JIFS-190146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3319-3330, 2020

Authors: Ke, Su | Lele, Ren | Xiaohui, Ren

Article Type: Research Article

Abstract: In this paper, a modified nonmonotone QP-free method without penalty function or filter is proposed for inequality constrained optimization. There is only two or three systems of linear equations with the same coefficients are solved at each iteration. We obtain a fundamental direction and the corresponding multiplier by the first equation, and then make full use of Lagrangian function information and multiplier to bend the search direction appropriately and obtain the search direction by the second linear equation. Moreover, the acceptable criterion of trial points is relaxed by the modified nonmonotone linear search technique. Under mild conditions, the global convergence …of the algorithm is proved. Numerical results are given at the end of the paper. Show more

Keywords: Inequality constrained optimization, QP-free method, nonmonotone, working set, global convergence

DOI: 10.3233/JIFS-190475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3331-3342, 2020

Authors: Shao, Songtao | Zhang, Xiaohong

Article Type: Research Article

Abstract:  The probabilistic neutrosophic hesitant fuzzy numbers are considered to be effective tools for dealing with such decision problems when both subjective and objective uncertainties exist simultaneously. However, the existing methods for dealing with real-life decision-making in the context is based on the assumption that the relationships between all criteria are independent and irrelevant. It is worth noting that this assumption is not sufficient. In fact, there may be interrelationships between attributes. In order to consider the correlation between factors from a more global perspective, the generalized Shapley probabilistic neutrosophic hesitant fuzzy Choquet averaging (GS-PNHFCA) operator and the generalized Shapley probabilistic …hesitant fuzzy Choquet geometric (GS-PNHFCG) operator are investigated. Next, in order to find the optimal weight vector about DMs and criteria, a model is constructed by the maximizing score deviation (MSD) method. In addition, based on the integrated operators and built models, an algorithm for solving the MCGDM problem of PNHFN is designed. The effectiveness and practicability of the algorithm is proved by comparison with existingresults. Show more

Keywords: Probabilistic neutrosophic hesitant fuzzy set, multi-criteria group decision-making (MCGDM), Choquet integral, shapley function, aggregation operator

DOI: 10.3233/JIFS-190493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3343-3357, 2020

Authors: Pandey, Prateek | Litoriya, Ratnesh

Article Type: Research Article

Abstract: Irrespective of the nature of the software, the choice of a software development process plays a crucial role. A good choice may help expand the business to heights while a bad choice may wreak havoc for the stakeholders. Therefore, selecting the right software development process is an indispensable element during project planning. Undoubtedly, web-based applications differ from conventional software and are becoming so popular in society due to their distinguishing features and widespread coverage. Web application software is generally found fit candidates to be developed according to agile software processes. This article attempts to provide a model for advising an …agile software process during the planning phase of a web application development project. The proposed model takes into account the underlying web project characteristics and uses the Fuzzy Analytic Hierarchy Process (FAHP) along with TOPSIS techniques for suitable agile candidate selection. The model is also validated on a primary dataset obtained by developing twenty web application projects through four student teams. The results show that the proposed model is successful in suggesting a correct agile process with a probability of the order of 0.8. Show more

Keywords: Agile process, fuzzy AHP, TOPSIS, web application, identification model

DOI: 10.3233/JIFS-190508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3359-3370, 2020

Authors: Fan, Jiashuang | Yu, Suihuai | Yu, Mingjiu | Chu, Jianjie | Tian, Baozhen | Li, Wenhua | Wang, Hui | Hu, Yukun | Chen, Chen

Article Type: Research Article

Abstract: In order to achieve optimal selection of design schemes in cloud environment, this paper proposed a novel hybrid Multi-Criteria Decision-Making (MCDM) model integrated Fuzzy Analytic Network Process (fuzzy-ANP) and Fuzzy Quality Function Development (fuzzy-QFD). There are three steps in the novel approach. In the first step, the evaluation target system of design scheme is identified considering four dimensions: economy, society, environment, and culture. The proper indicators are identified by the integration of multi-intelligent techniques. In the second step, decision-makers are asked to compare the decision indicators, and the weight for each indicator is determined using the intuitionistic fuzzy number and …fuzzy-ANP. In the third step, the decision-making system of design scheme is developed to compare and rank alternatives. The decision-makers are invited over to compare different options and rank them with the aid of fuzzy-QFD in the cloud environment. A case study is provided to validate the proposed approach. Twenty-five sensitivity analysis experiments are conducted to figure out the influence of evaluation indicators on decision making process. The novel approach makes use of the strength of fuzzy set theory in handing vagueness and uncertainty, the fuzzy-ANP in non-independent hierarchy evaluation on the indicator system, the advantage of fuzzy-QFD in multiple-objective decision analysis. Based on the comparative study and assessment, the results show that the proposed approach is more efficient and provided users with multidimensional evaluation. Show more

Keywords: Fuzzy analytic network process, fuzzy quality function development, multi-criteria decision-making, optimal selection, cloud environment

DOI: 10.3233/JIFS-190630

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3371-3388, 2020

Authors: Tao, Liang | Siqi, Qian | Zhaochao, Meng | Gao Feng, Xie

Article Type: Research Article

Abstract: With the construction of large-scale wind turbines, how to reduce the operation and maintenance costs has become an urgent problem to be solved. In this paper, by extracting the actual operation data of the wind turbine in Supervisory Control and Data Acquisition (SCADA) system, the Bidirectional Recurrent Neural Networks (BRNN) is used to establish the wind turbine operation prediction model. By eliminating abnormal data points caused by accidental factors through box diagram, the fault risk threshold of wind turbine components is optimized. Then, based on the residual between the actual value and the measured value of the large sliding window, …the early fault warning is realized according to Wright criterion. Finally, the model proposed in this paper is applied to the actual wind turbine, which proves the reliability and accuracy of the method. Show more

Keywords: BRNN, box-plot, large sliding window, Letts’criterion, early fault warning

DOI: 10.3233/JIFS-190642

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3389-3401, 2020

Authors: Jiang, Bao | Zou, Zezhou | Lio, Waichon | Li, Jian

Article Type: Research Article

Abstract:  Identifying the specific scale status by evaluating scale efficiency of each DMU (decision making unit) is critical for policy makers to make a correct decision on inefficiency improvement. Although some traditional DEA (Data Envelopment Analysis) models can evaluate different scale efficiencies and identify the specific status of each DMU, they are unable to deal with the evaluation indicators with imprecise observation. Therefore, we propose two different uncertain DEA models with imprecise inputs and outputs to recognize two scale status of increasing returns to scale (IRS) and decreasing returns to scale (DRS), which can better help policy makers make a correct …decision on inefficiency improvement without accurate data. Moreover, we analyse the stability of these uncertain DEA models to make effective suggestions for decision makers to further improve the inefficient DMUo or to adjust the inputs and outputs on the premise of remaining the new DMUo efficient. Show more

Keywords: Data envelopment analysis, uncertainty theory, specific scale efficiency identification, stability analysis

DOI: 10.3233/JIFS-190662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3403-3417, 2020

Authors: Choudhury, Hussain Ahmed | Sinha, Nidul | Saikia, Monjul

Article Type: Research Article

Abstract: Video compression is applied for reducing the requirement of hardware, bandwidth, hard drives and power consumption for storing and processing an excessive amount of data generated by videos. The computationally intensive and most time-consuming segment of video compression is known as motion estimation (ME). ME process can be regarded as an optimization problem where search is carried out in a predefined search area of the target frame to locate the identical macroblock (MB) corresponding to each MB in anchor frame by minimizing the objective function cum search criterion as minimum value of search criterion identifies the location of the best …matching MB. Since the efficiency of ME decides the efficiency of Video compression, a rich number of fast block matching algorithms (BMAs) were reported to maintain the tradeoff between the computational complexity and visual experience of video during the ME process. Investigation reveals that most of the pattern-based BMAs are prone to the local optimum and stuck in sub-optimal results. Due to the emergence of various nature-inspired algorithms (NIA) like particle swarm optimization (PSO), genetic algorithm (GA), evolutionary algorithm, etc. and their application in optimizing all types of day to day problems has opened a new era in the field of ME. Our investigation focuses on the application of all types of NIA reported to date for optimizing the ME process in terms of speed, accuracy, and quality. This investigation will analyze all the NIAs and their methodologies through an extensive study of their accompanying publications and will enable us to do a detailed comparison to highlight the competitive advantage of soft computing techniques over existing pattern-based algorithms. Show more

Keywords: Motion estimation, nature inspired algorithms, genetic algorithm, particle swarm optimization, cuckoo search

DOI: 10.3233/JIFS-190308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3419-3443, 2020

Authors: Yang, Yunlei | Hou, Muzhou | Luo, Jianshu | Tian, Zhongchu

Article Type: Research Article

Abstract: In this paper, block neural network (BNN) method is proposed to solve several kinds of differential equations. BNN is used to construct approximating functions and its derivatives, the improved extreme learning machine (IELM) algorithm are designed to train network weights. To evaluate the performance of the proposed method, numerical examples are performed by the presented method. Comparison of the numerical results with exact solutions validate the feasibility of the proposed method in accuracy. Results compared with other recent research works also validate the superiority of the proposed approach. Numerical results show that the proposed BNN with IELM algorithm perform well …in accuracy and requires less hidden neurons. Show more

Keywords: Differential equations, block neural network, IELM algorithm

DOI: 10.3233/JIFS-190406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3445-3461, 2020

Authors: Lin, Zhongqi | Jia, Jingdun | Gao, Wanlin | Huang, Feng

Article Type: Research Article

Abstract: Small-target categorization of butterflies suffers from large-scale search space of candidate target locations, subtler discriminations, camouflaged appearances, and complex backgrounds. Precise localization and domain-specific discrimination extraction are crucial for this issue. In this work, a novel hierarchical coarse-to-fine convolutional neural network (C-t-FCNN) was proposed. It consists of CoarseNet and FineNet, which incorporate object-level and part-level representations into framework. Specifically, the coarse-grained features containing the orientation description are generated by CoarseNet, while the fine-grained discriminations with semantic distinctiveness are captured by FineNet. Next, the correspondences are established to mark the target regions, background regions, and mismatched regions depending on the quantification …of scale-invariant feature transform (SIFT) descriptors. Then, the features are subsampled via spatial pyramid pooling (SPP) for size uniformity and integration. Finally, the irrelevant background and mismatched regions are eliminated by the support vector machine (SVM) with a radial basis function (RBF) kernel, leaving only the target-specific patches for finer-scale extraction. Hence the numeracy can be economized from identifying irrelevant areas and can be rescheduled in feature extraction and final decision, which can suppress time complexity simultaneously. A total of 119,016 augmented butterfly images spanning 47 categories are utilized for model training, while 13,734 images are evaluated for effectiveness verification. The C-t-FCNN delivers impressive performance, i.e., it achieves a validation accuracy of 92.08% and a testing accuracy of 91.6%, which outperforms state-of-the-arts. Show more

Keywords: Convolutional neural network, coarse-to-fine perception, deep learning, small target categorization, butterfly images

DOI: 10.3233/JIFS-190747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3463-3487, 2020

Authors: Wen, Meilin | Chen, Yubing | Yang, Yi | Kang, Rui | Guo, Miaomiao

Article Type: Research Article

Abstract: Stocking strategy for spare parts has a pivotal influence on the productivity and efficiency of industrial plants. Considering the situation of lacking historical statistics causing by uncertainty, this paper proposes a method to optimize spares varieties based on uncertainty theory and uncertain data envelopment analysis (DEA) model. Firstly, a recursive hierarchy structure is established to construct an evaluation system to meet the requirement of avoiding attributes’ redundancy. Then, an uncertain spares optimization model (USOM), which is based on uncertain DEA model, is developed to optimize spares varieties. Furthermore, the uncertainty theory is utilized to convert the USOM into an equivalent …deterministic model for simplification. Finally, a numerical example is given to illustrate the performance of this model. The results show that the stocking strategy obtained from the proposed decision model can satisfy the purpose of saving resources and prompting continuous operation. Show more

Keywords: Spares varieties, uncertain systems, optimization, data envelopment analysis

DOI: 10.3233/JIFS-190838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3489-3499, 2020

Authors: Meng, Fan | Qi, Zhiquan | Chen, Zhensong | Wang, Bo | Shi, Yong

Article Type: Research Article

Abstract: Crack detection has drawn much attention in the last two decades, because of dramatic bloom in monitoring images and the urgent need of corresponding crack detection. However, recent methods have not taken advantage of structure information effectively, resulting in low accuracy when dealing with crack-like noises. In this paper, we propose a novel crack detection framework, which is able to identify cracks from noisy background. The main contributions of this paper are as follows: (1) giving a new edge-based crack detection framework to improve the detection performance; (2) proposing a novel mid-level feature, named Crack Token , which captures the …local structure information of cracks; (3) introducing a new evaluation strategy for crack detection task, which provides a comprehensive system for approach evaluation and comparison in this area. In addition, we provide a novel definition of pavement crack and verify our framework and evaluation strategy in this real world application. Extensive experiments demonstrate the state-of-the-art results of the proposed framework. Show more

Keywords: Crack detection, crack token, machine learning, edge detection, crack recognition

DOI: 10.3233/JIFS-190868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3501-3513, 2020

Authors: Zhao, Youen | Ji, Xiuhua | Liu, Zhaoguang

Article Type: Research Article

Abstract: Blind image quality assessment (BIQA) aims to evaluate the quality of an image without information regarding its reference image. In this paper, we proposed a novel BIQA method, which combines thirty six natural scene statistics (NSS) features, two color statistics features and four perceptual features to construct an image quality assessment model. Support Vector Regression (SVR) is adopted to build the relationship between these features and image quality scores, yielding a measure of image quality. Experimental results in LIVE, TID2013 databases and their cross validations show that the proposed method records a higher correlations with human subjective judgments of visual …quality and delivers highly competitive performance with state-of-the-art BIQA models. Show more

Keywords: Blind image quality assessment, natural scene statistics feature, perceptual feature, color statistics feature, support vector regression

DOI: 10.3233/JIFS-190998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 3515-3526, 2020