Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Hu, Ziyu | Ma, Xuemin | Sun, Hao | Yang, Jingming | Zhao, Zhiwei

Article Type: Research Article

Abstract: When dealing with multi-objective optimization, the proportion of non-dominated solutions increase rapidly with the increase of optimization objective. Pareto-dominance-based algorithms suffer the low selection pressure towards the true Pareto front. Decomposition-based algorithms may fail to solve the problems with highly irregular Pareto front. Based on the analysis of the two selection mechanism, a dynamic reference-vector-based many-objective evolutionary algorithm(RMaEA) is proposed. Adaptive-adjusted reference vector is used to improve the distribution of the algorithm in global area, and the improved non-dominated relationship is used to improve the convergence in a certain local area. Compared with four state-of-art algorithms on DTLZ benchmark with …5-, 10- and 15-objective, the proposed algorithm obtains 13 minimum mean IGD values and 8 minimum standard deviations among 15 test problem. Show more

Keywords: Many-objective optimization, evolutionary algorithm, Gaussian mixture model, selection mechanism

DOI: 10.3233/JIFS-192124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 449-461, 2021

Authors: Li, Juan | Shao, Yabin | Qi, Xiaoding

Article Type: Research Article

Abstract:  With respect to multiple attribute group decision making problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of interval-valued uncertain linguistic variable. In this paper, we introduce the idea of variable precision into the incomplete interval-valued fuzzy information system and propose the theory of variable precision rough sets over incomplete interval-valued fuzzy information systems. Then, we give the properties of rough approximation operators and study the knowledge discovery and attribute reduction in the incomplete interval-valued fuzzy information system under the condition that a certain degree of …misclassification rate is allowed to exist. Furthermore, a decision rule and decision model are given. Finally, an illustrative example is given and compared with the existing methods, the practicability and effectiveness of this method are further verified. Show more

Keywords: Interval-valued fuzzy set, incomplete information systems, variable precision interval-valued rough fuzzy set, attribute reduction, decision rules

DOI: 10.3233/JIFS-192161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 463-475, 2021

Authors: Xu, Yanping | Ye, Tingcong | Wang, Xin | Lai, Yuping | Qiu, Jian | Zhang, Lingjun | Zhang, Xia

Article Type: Research Article

Abstract: In the field of security, the data labels are unknown or the labels are too expensive to label, so that clustering methods are used to detect the threat behavior contained in the big data. The most widely used probabilistic clustering model is Gaussian Mixture Models(GMM), which is flexible and powerful to apply prior knowledge for modelling the uncertainty of the data. Therefore, in this paper, we use GMM to build the threat behavior detection model. Commonly, Expectation Maximization (EM) and Variational Inference (VI) are used to estimate the optimal parameters of GMM. However, both EM and VI are quite sensitive …to the initial values of the parameters. Therefore, we propose to use Singular Value Decomposition (SVD) to initialize the parameters. Firstly, SVD is used to factorize the data set matrix to get the singular value matrix and singular matrices. Then we calculate the number of the components of GMM by the first two singular values in the singular value matrix and the dimension of the data. Next, other parameters of GMM, such as the mixing coefficients, the mean and the covariance, are calculated based on the number of the components. After that, the initialization values of the parameters are input into EM and VI to estimate the optimal parameters of GMM. The experiment results indicate that our proposed method performs well on the parameters initialization of GMM clustering using EM and VI for estimating parameters. Show more

Keywords: Network threat detection, gaussian mixture models, expectation maximization, variational inference, singular value decomposition, parameters initialization

DOI: 10.3233/JIFS-200066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 477-490, 2021

Authors: Shen, Ao | Peng, Shuling | Liu, Gaofei

Article Type: Research Article

Abstract: The probabilistic linguistic term sets (PLTSs) are widely used in decision-making, due to its convenience of evaluation, and allowances of probability information. However, there are still some cases where it is not convenient to give an evaluation using the PLTS gramma. Sometimes the evaluators can only give a comparative relationship between alternatives, sometimes evaluators may have difficulty understanding all the alternatives and cannot give a complete assessment. Therefore, we propose a method to transform the comparative linguistic expressions (CLEs) into PLTSs, and the comparison objects of CLEs are alternatives evaluated by PLTSs. And the probability distribution has been adjusted to …make the transformation more in line with common sense. Then, a method to correct the deviation is proposed, allowing alternatives to be compared in the case of incomplete assessment. Combining the above two methods, we propose a decision-making method when both CLEs and incomplete assessments coexist. With the study in this paper, the limitations of PLTS-based evaluation and decision-making are reduced and the flexibility of using PLTS is improved. Show more

Keywords: Probabilistic linguistic term sets, comparative linguistic expressions, incomplete assessments, transforming, decision-making

DOI: 10.3233/JIFS-200103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 491-506, 2021

Authors: Jiang, Jianming | Wu, Wen-Ze | Li, Qi | Zhang, Yu

Article Type: Research Article

Abstract: The hydropower plays a key role in electricity system owing to its renewability and largest share of clean electricity generation that promotes sustainable development of national economy. Developing a proper forecasting model for the quarterly hydropower generation is crucial for associated energy sectors, which could assist policymakers in adjusting corresponding schemes for facing with sustained demands. For this purpose, this paper presents a fractional nonlinear grey Bernoulli model (abbreviated as FANGBM(1,1)) coupled seasonal factor and Particular Swarm Optimization (PSO) algorithm, namely PSO algorithm-based FASNGBM(1,1) model. In the proposed method, the moving average method that eliminates the seasonal fluctuations is introduced …into FANGBM(1,1), then in which the structure parameters of FASNGBM(1,1) are determined by PSO. Based on hydropower generation of China from the first quarter of 2011 to the final quarter of 2018 (2011Q1-2018Q4), the numerical results show that the proposed model has a better performance than that of other benchmark models. Eventually, the quarterly hydropower generation of China from 2019 to 2020 are forecasted by the proposed model, according to results, the hydropower generation of China will reach 11287.14 × 108 Kwh in 2020. Show more

Keywords: Quarterly hydropower generation, seasonal fluctuation, FASNGBM(1,1), Particle Swarm Optimization (PSO)

DOI: 10.3233/JIFS-200113

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 507-519, 2021

Authors: Zhai, Junhai | Qi, Jiaxing | Zhang, Sufang

Article Type: Research Article

Abstract: The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K -nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K -nearest neighbor. In this paper, we present a condensed fuzzy K -nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T , moving them from T to S . Specifically, CFKNN consists of three steps. First, for each instance x  ∈ T , it finds the K -nearest neighbors in S …and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T . Second it computes the fuzzy membership degrees of x using the fuzzy K -nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms. Show more

Keywords: K-nearest neighbor, fuzzy K-nearest neighbor, fuzzy membership degree, instance selection, information entropy

DOI: 10.3233/JIFS-200124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 521-533, 2021

Authors: Mandal, Ashis Kumar | Sen, Rikta | Chakraborty, Basabi

Article Type: Research Article

Abstract: The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. …The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy. Show more

Keywords: Feature subset selection, binary owl search algorithm, meta-heuristic, optimization, self adaptive mechanism

DOI: 10.3233/JIFS-200258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 535-550, 2021

Authors: Lu, Liqiong | Wu, Dong | Tang, Ziwei | Yi, Yaohua | Huang, Faliang

Article Type: Research Article

Abstract: This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution …weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets. Show more

Keywords: Script identification, score CNN, attention CNN, discriminative patches, scene images

DOI: 10.3233/JIFS-200260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 551-563, 2021

Authors: Zhang, Di | Li, Pi-Yu | An, Shuang

Article Type: Research Article

Abstract: In this paper, we propose a new hybrid model called N -soft rough sets, which can be seen as a combination of rough sets and N -soft sets. Moreover, approximation operators and some useful properties with respect to N -soft rough approximation space are introduced. Furthermore, we propose decision making procedures for N -soft rough sets, the approximation sets are utilized to handle problems involving multi-criteria decision-making(MCDM), aiming at electing the optional objects and the possible optional objects based on their attribute set. The algorithm addresses some limitations of the extended rough sets models in dealing with inconsistent decision problems. …Finally, an application of N -soft rough sets in multi-criteria decision making is illustrated with a real life example. Show more

Keywords: Rough sets, N-soft sets, N-soft rough sets, Decision making analysis

DOI: 10.3233/JIFS-200338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 565-573, 2021

Authors: Manickavasagam, B. | Amutha, B. | Revathi, M. | Karthick, N. | Sree Kumar, K. | Priyanka, K.

Article Type: Research Article

Abstract: Wireless Sensor Node (WSN) helps to track inpatient and remote patient (home/working) health information. Mishandling of the electronic system, patient behaviour and environmental changes which are all lead to incorrect data generation while using WSN for medical purposes. It leads to a false alarm being raised, network resource wastage, a false node priority level and low reliability. We have introduced the Mutual Trust Model (MTM) for Wireless Body Area Network (WBAN) with the help of Fog-Node (FN) to address these issues and to ensure the trustworthiness of the information acquired. In this, First-Hand Trust Method calculates the confidence value of …the individual sensor node. Then, with neighbor node support, the Stigmercy Trust Method (STM) is implemented to reinforce the trust source node. Ultimately, the individual patient’s confidence value for the MTM model is determined. With the assistance of the wireless-mininet network emulator and the RYU controller, the network environment model implement, and the results have been obtained. MTM predicts the confidence level of the collected data significantly and produces an accuracy of 92.3 percentage to prevent the emergency band from being used dispensable. Show more

Keywords: Trust analysis, WBAN, data reliability, direct and indirect trust method, and relative trust approach

DOI: 10.3233/JIFS-200363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 575-589, 2021

Authors: Shiri Daryani, Zahra | Tohidi, Ghasem | Daneshian, Behrouz | Razavyan, Shabnam | Hosseinzadeh Lotfi, Farhad

Article Type: Research Article

Abstract: Inputs and outputs of Decision Making Units (DMUs) are estimated by the Inverse Data Envelopment Analysis (InvDEA) models, while their relative efficiency scores remain unchanged. But, in some cases, cost/price information of the inputs and outputs are available. This paper employs the input and output cost/price information, including the generalized InvDEA concept in two-stage structures. To this end, it proposes a four-stage method to deal with the InvDEA concept, for estimating the inputs and outputs of the DMUs with a two-stage network structure method, while the allocative efficiency scores of all the units remain stable. Eventually, an empirical example is …rendered to illustrate the competence of the method which is presented. Show more

Keywords: Inverse DEA, network DEA, two-stage network, cost efficiency, input/output estimation

DOI: 10.3233/JIFS-200386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 591-603, 2021

Authors: Xu, Lei | Liu, Yi | Liu, Haobin

Article Type: Research Article

Abstract: For the sake of better handle the imprecise and uncertain information in decision making problems(DMPs), linguistic interval-valued intuitionistic fuzzy numbers(LIVIFNs) based aggregation operators (AOS) are proposed by combining extended Copulas (ECs), extended Co-copulas (ECCs), power average operator and linguistic interval-valued intuitionistic fuzzy information (LIVIFI). First of all, ECs and ECCs, some specifics of ECs and ECCs, score and accuracy functions of LIVIFNs are gained. Then, based on ECs and ECCs, several aggregation operators are proposed to aggregate LIVIFI, which can offer decision makers (DMs) desirable generality and flexibility. In addition, the desired properties of proposed AOS are discussed. Last but …not least, a MAGDM approach is constructed based on proposed AOs; Consequently, the effectiveness of the proposed approach is verified by a numerical example, and then the advantages are showed by comparing with other approaches. Show more

Keywords: linguistic interval-valued intuitionistic fuzzy set (LIVIFS), Extended Copulas, Extended Co-copulas, PA operator, multi-attribute group decision making(MAGDM)

DOI: 10.3233/JIFS-200387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 605-624, 2021

Authors: Mahmood, Tahir | Ur Rehman, Ubaid | Ali, Zeeshan | Mahmood, Tariq

Article Type: Research Article

Abstract: Fuzzy set (FS) theory is one of the most important tool to deasl with complicated and difficult information in real-world. Now FS has many extensions and hesitant fuzzy set (HFS) is one of them. Further generalization of FS is complex fuzzy set (CFS), which contains only the membership grade, whose range is unit disc instead of [0, 1]. The aim of this paper is to present the idea of complex hesitant fuzzy set (CHFS) and to introduce its basic properties. Basically, CHFS is the combination of CFS and HFS to deal with two dimension information in a single set. Further, …the vector similarity measures (VSMs) such as Jaccard similarity measures (JSMs), Dice similarity measures (DSMs) and Cosine similarity measures (CSMs) for CHFSs are discussed. The special cases of the proposed measures are also discussed. Then, the notion of complex hesitant fuzzy hybrid vector similarity measures are utilized in the environment of pattern recognition and medical diagnosis. Further, based on these distance measures, a decision-making method has been presented for finding the best alternative under the set of the feasible one. Illustrative examples from the field of pattern recognition as well as medical diagnosis have been taken to validate the approach. Finally, the comparison between proposed approaches with existing approaches are also discussed to find the reliability and proficiency of the elaborated measures for complex hesitant fuzzy elements. Show more

Keywords: Complex fuzzy set, complex hesitant fuzzy sets, similarity measures, hybrid vector similarity measures

DOI: 10.3233/JIFS-200418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 625-646, 2021

Authors: El_Tokhy, Mohamed S.

Article Type: Research Article

Abstract: Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from …zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications. Show more

Keywords: Recognition system, digital signal and image processing, authentication systems

DOI: 10.3233/JIFS-200425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 647-672, 2021

Authors: Wang, Degang | Song, Wenyan | Pedrycz, Witold | Cai, Lili

Article Type: Research Article

Abstract: In this paper, an integrated model combining interval deep belief network (IDBN) and neural network with nonlinear weights, called IDBN-NN, is proposed for interval-valued data modeling. Firstly, the IDBN with variable learning rate is designed to initialize parameters of each sub-model. Based on a modified contrastive divergence algorithm the least square method is adopted to identify the coefficients of nonlinear weights in the output layer. Then, to improve the modeling accuracy, the Fuzzy C-Means (FCM) method and the Particle Swarm Optimization (PSO) algorithm are applied to tune the weights of sub-models. Though each sub-model can capture the nonlinear feature of …the original system, by intersecting cut sets the synthesizing modeling scheme can further improve the performance of the proposed model. Some numerical examples show that the IDBN-NN with nonlinear output structure can achieve higher accuracy than some interval-valued data modeling methods. Show more

Keywords: Interval data, neural network, integrated model, fuzzy clustering

DOI: 10.3233/JIFS-200500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 673-683, 2021

Authors: Wang, Huiru | Zhou, Zhijian

Article Type: Research Article

Abstract:  In Rough margin-based ν -Twin Support Vector Machine (Rν -TSVM) algorithm, the rough theory is introduced. Rν -TSVM gives different penalties to the corresponding misclassified samples according to their positions, so it avoids the overfitting problem to some extent. While the input data is a tensor, Rν -TSVM cannot handle it directly and may not utilize the data information effectively. Therefore, we propose a novel classifier based on tensor data, termed as Rough margin-based ν -Twin Support Tensor Machine (Rν -TSTM). Similar to Rν -TSVM, Rν -TSTM constructs rough lower margin, rough upper margin and rough boundary in tensor space. …Rν -TSTM not only retains the superiority of Rν -TSVM, but also has its unique advantages. Firstly, the data topology is retained more efficiently by the direct use of tensor representation. Secondly, it has better classification performance compared to other classification algorithms. Thirdly, it can avoid overfitting problem to a great extent. Lastly, it is more suitable for high dimensional and small sample size problem. To solve the corresponding optimization problem in Rν -TSTM, we adopt the alternating iteration method in which the parameters corresponding to the hyperplanes are estimated by solving a series of Rν -TSVM optimization problem. The efficiency and superiority of the proposed method are demonstrated by computational experiments. Show more

Keywords: Classification problem, ν-Twin support vector machine, rough margin, tensor learning

DOI: 10.3233/JIFS-200573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 685-702, 2021

Authors: Ali, Aqib | Mashwani, Wali Khan | Tahir, Muhammad H. | Belhaouari, Samir Brahim | Alrabaiah, Hussam | Naeem, Samreen | Nasir, Jamal Abdul | Jamal, Farrukh | Chesneau, Christophe

Article Type: Research Article

Abstract: The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of …(200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying “Correlation-based Feature Selection” (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as “Support Vector Machine” (SVM), “Logistic” (Lg), “Bagging” (B), and “LogitBoost” (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively. Show more

Keywords: Maize seeds, statistical features, discrimination, machine vision, support vector machine

DOI: 10.3233/JIFS-200635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 703-714, 2021

Authors: Gou, Hongyuan | Zhang, Xianyong

Article Type: Research Article

Abstract: The multi-granulation rough sets serve as important hierarchical models for intelligent systems. However, their mainstream optimistic and pessimistic models are respectively too loose and strict, and this defect becomes especially serious in hierarchical processing on an attribute-expansion sequence. Aiming at the attribute-addition chain, compromised multi-granulation rough set models are proposed to systematically complement and balance the optimistic and pessimistic models. According to the knowledge refinement and measure order induced by the attribute-enlargement sequence, the basic measurement positioning and corresponding pointer labeling based on equilibrium statistics are used, and thus we construct four types of compromised models at three levels of …knowledge, approximation, and accuracy. At the knowledge level, the median positioning of ordered granulations derives Compromised-Model 1; at the approximation level, the average positioning of approximation cardinalities is performed, and thus the separation and integration of dual approximations respectively generate Compromised-Models 2 and 3; at the accuracy level, the average positioning of applied accuracies yields Compromised-Model 4. Compromised-Models 1–4 adopt distinctive cognitive levels and statistical perspectives to improve and perfect the multi-granulation rough sets, and their properties and effectiveness are finally verified by information systems and data experiments. Show more

Keywords: Multi-granulation rough set, statistical compromised modeling, attribute-addition chain, granular computing, tri-level analysis

DOI: 10.3233/JIFS-200708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 715-732, 2021

Authors: Gui, Wangyang | Zhang, Xu | Wang, Ai

Article Type: Research Article

Abstract: The construction of high-speed rails is regarded as a major opportunity for urban development by local governments in China, so various grand development plans are actively formulated to promote urban economic development. In this paper, the development of station space is evaluated empirically based on the calculated node and place values of 24 high-speed rail stations along the Beijing-Shanghai line and Bertolini’s “node-place” model. The results show that: (1) The 24 stations along the Beijing-Shanghai line have different development scale, which mostly act as sub-centers of the city, where the real estate industry, modern service industry and cultural industry are …dominated in station space planning. Moreover, local governments are optimistic about the accelerant effect of high-speed rail stations whose functional configuration along the line is relatively repeated, because all 24 stations are basically set with business centers. (2) The size of cities along the Beijing-Shanghai line is related to the node value, the higher the urban function level, the greater the node value, with great differences among cities. The node value of big cities is far higher than that of small and medium-sized cities, hence there are node-oriented station areas in big cities and place-oriented ones in middle-sized and small cities. However, there is no direct relationship between the urban function level of stations along the line and the value of urban places. In some small and medium-sized cities, the planning and development intensity and scale of station areas even exceed that of big cities. (3) Only Wuxi station and Nanjing station are in a balanced development state in the space planning of railway stations along the Beijing-Shanghai line. Therefore, the risk of long-term development of station area should be considered in the planning, and reasonable measures should be formulated to promote the sustainable development of station area, so as to form the overall development of Station City. Show more

Keywords: High speed railway, station area, Beijing-Shanghai line, node-place model

DOI: 10.3233/JIFS-200712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 733-743, 2021

Authors: Chellamani, Ganesh Kumar | Firdouse Ali Khan, M. | Chandramani, Premanand Venkatesh

Article Type: Research Article

Abstract: Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their …comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ...  , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort. Show more

Keywords: Day-ahead tariff, decision tree, home energy management, random forest, support vector machine

DOI: 10.3233/JIFS-200722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 745-757, 2021

Authors: Oner, Tahsin | Katican, Tugce | Saeid, Arsham Borumand

Article Type: Research Article

Abstract: The aim of this study is to introduce fuzzy filters of Sheffer stroke Hilbert algebra. After defining fuzzy filters of Sheffer stroke Hilbert algebra, it is shown that a quotient structure of this algebra is described by its fuzzy filter. In addition to this, the level filter of a Sheffer stroke Hilbert algebra is determined by its fuzzy filter. Some fuzzy filters of a Sheffer stroke Hilbert algebra are defined by a homomorphism. Normal and maximal fuzzy filters of a Sheffer stroke Hilbert algebra and the relation between them are presented. By giving the Cartesian product of fuzzy filters of …a Sheffer stroke Hilbert algebra, various properties are examined. Show more

Keywords: (Sheffer stroke) Hilbert algebra, (normal) fuzzy filter

DOI: 10.3233/JIFS-200760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 759-772, 2021

Authors: Liu, Shuai | Xu, Ying | Guo, Lingming | Shao, Meng | Yue, Guodong | An, Dong

Article Type: Research Article

Abstract: Tens of thousands of work-related injuries and deaths are reported in the construction industry each year, and a high percentage of them are due to construction workers not wearing safety equipment. In order to address this safety issue, it is particularly necessary to automatically identify people and detect the safety characteristics of personnel at the same time in the prefabricated building. Therefore, this paper proposes a depth feature detection algorithm based on the Extended-YOLOv3 model. On the basis of the YOLOv3 network, a security feature recognition network and a feature transmission network are added to achieve the purpose of detecting …security features while identifying personnel. Firstly, a security feature recognition network is added side by side on the basis of the YOLOv3 network to analyze the wearing characteristics of construction workers. Secondly, the S-SPP module is added to the object detection and feature recognition network to broaden the features of the deep network and help the network extract more useful features from the high-resolution input image. Finally, a special feature transmission network is designed to transfer features between the construction worker detection network and the security feature recognition network, so that the two networks can obtain feature information from the other network respectively. Compared with YOLOv3 algorithm, Extended-YOLOv3 in this paper adds security feature recognition and feature transmission functions, and adds S-SPP module to the object detection and feature recognition network. The experimental results show that the Extended-YOLOv3 algorithm is 1.3% better than the YOLOV3 algorithm in AP index. Show more

Keywords: YOLOv3, target detection, depth feature extraction, S-SPP module, deep learning

DOI: 10.3233/JIFS-200778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 773-786, 2021

Authors: Saravanan, G. | Yuvaraj, N.

Article Type: Research Article

Abstract: Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson …Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms. Show more

Keywords: Mobile cloud computing, mobile cloud service providers, mobile cloud users, poisson linear, stochastic, deep neural resource allocation

DOI: 10.3233/JIFS-200799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 787-797, 2021

Authors: Xu, Xiaoyun | Wu, Jingzheng | Yang, Mutian | Luo, Tianyue | Meng, Qianru | Li, Weiheng | Wu, Yanjun

Article Type: Research Article

Abstract: As the scale of software systems continues expanding, software architecture is receiving more and more attention as the blueprint for the complex software system. An outstanding architecture requires a lot of professional experience and expertise. In current practice, architects try to find solutions manually, which is time-consuming and error-prone because of the knowledge barrier between newcomers and experienced architects. The problem can be solved by easing the process of apply experience from prominent architects. To this end, this paper proposes a novel graph-embedding-based method, AI-CTO, to automatically suggest software stack solutions according to the knowledge and experience of prominent architects. …Firstly, AI-CTO converts existing industry experience to knowledge, i.e., knowledge graph. Secondly, the knowledge graph is embedded in a low-dimensional vector space. Then, the entity vectors are used to predict valuable software stack solutions by an SVM model. We evaluate AI-CTO with two case studies and compare its solutions with the software stacks of large companies. The experiment results show that AI-CTO can find effective and correct stack solutions and it outperforms other baseline methods. Show more

Keywords: Knowledge graph, graph embedding, software architecture

DOI: 10.3233/JIFS-200899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 799-812, 2021

Authors: Kazemi, Sajad | Mavi, Reza Kiani | Emrouznejad, Ali | Kiani Mavi, Neda

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study …proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster. Show more

Keywords: Data envelopment analysis, fuzzy DEA, non-homogeneous, clustering, common set of weights (CSW)

DOI: 10.3233/JIFS-200962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 813-832, 2021

Authors: Khan, Y. A. | Chu, Y. M. | Abbas, S. Z.

Article Type: Research Article

Abstract: This paper investigates governments’ performance in the country. We achieved this objective differently. We employed an inverse method of assessment, with the utilization of factor copula modeling technique, to study the dependence relationship of exchange rates returns as auxiliary variables, the performance of political and army government tenures in the country in the last two decades are evaluated. Through factor analysis, common factors for the exchange rate are obtained. The analysis shows that conditioned on the common factors, the dependence amongst the elected currencies are strongly asymmetric in most of the tenures except the term of Pakistan Muslim League-Nawaz, and …condition on common factor Clayton copula demonstrating hypothesis is more suitable. However, we perceive high left tail reliance among foreign currency returns during Pakistan Muslim League-Nawaz tenure, and the condition on common factor Gumbel copula molding assumption is more appropriate. We are signifying the foulest government performance in the country among all occupancies under consideration. Show more

Keywords: Factor analysis, asymmetric, clayton copula, exchange rate, gumbel copula

DOI: 10.3233/JIFS-200996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 833-847, 2021

Authors: Saleem, Nasir | Khattak, Muhammad Irfan | Al-Hasan, Mu’ath | Jan, Atif

Article Type: Research Article

Abstract: Speech enhancement is a very important problem in various speech processing applications. Recently, supervised speech enhancement using deep learning approaches to estimate a time-frequency mask have proved remarkable performance gain. In this paper, we have proposed time-frequency masking-based supervised speech enhancement method for improving intelligibility and quality of the noisy speech. We believe that a large performance gain can be achieved if deep neural networks (DNNs) are layer-wise pre-trained by stacking Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). The proposed DNN is called as Gaussian-Bernoulli Deep Belief Network (GB-DBN) and are optimized by minimizing errors between the estimated and pre-defined masks. Non-linear …Mel-Scale weighted mean square error (LMW-MSE ) loss function is used as training criterion. We have examined the performance of the proposed pre-training scheme using different DNNs which are established on three time-frequency masks comprised of the ideal amplitude mask (IAM), ideal ratio mask (IRM), and phase sensitive mask (PSM). The results in different noisy conditions demonstrated that when DNNs are pre-trained by the proposed scheme provided a persistent performance gain in terms of the perceived speech intelligibility and quality. Also, the proposed pre-training scheme is effective and robust in noisy training data. Show more

Keywords: Supervised speech enhancement, deep learning, deep belief networks, restricted boltzmann machine, intelligibility, quality

DOI: 10.3233/JIFS-201014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 849-864, 2021

Authors: Gong, Zengtai | Wang, Junhu

Article Type: Research Article

Abstract: Up to now, there have been a lot of research results about multi-attribute decision making problems by fuzzy graph theory. However, there are few investigations about multi-attribute decision making problems under the background of indecisiveness. The main reason is that the difference of cognition and the complexity of thinking by decision makers, for the same question have different opinions. In this paper, we proposed a hesitant fuzzy hypergraph model based on hesitant fuzzy sets and fuzzy hypergraphs. At the same time, some basic graph operations of hesitant fuzzy hypergraphs are investigated and several equivalence relationship between hesitant fuzzy hypergraphs, hesitant …fuzzy formal concept analysis and hesitant fuzzy information systems are discussed. Since granular computing can deal with multi-attribute decision-making problems well, we considered the hesitant fuzzy hypergraph model of granular computing, and established an algorithm of multi-attribute decision-making problem based on hesitant fuzzy hypergraph model. Finally an example is given to illustrate the effectiveness of the algorithm. Show more

Keywords: Hesitant fuzzy sets, hesitant fuzzy graph, hesitant fuzzy hypergraph, granular computing, graph decision

DOI: 10.3233/JIFS-201016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 865-875, 2021

Authors: Hourali, Samira | Zahedi, Morteza | Fateh, Mansour

Article Type: Research Article

Abstract: Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from …a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset. Show more

Keywords: Natural language processing, coreference resolution, knowledge management, entity level information, neural network, multi-criteria ranking model

DOI: 10.3233/JIFS-201050

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 877-892, 2021

Authors: Mannar Mannan, J. | Sindhanai Selvan, K. | Mohemmed Yousuf, R.

Article Type: Research Article

Abstract: Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) …semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods. Show more

Keywords: Ontology, semantic ranking, classification, information retrieval

DOI: 10.3233/JIFS-201070

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 893-905, 2021

Authors: Demirkiran, Emin T. | Pak, Muhammet Y. | Cekik, Rasim

Article Type: Research Article

Abstract: Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a …novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance. Show more

Keywords: Accuracy, multi-criteria collaborative filtering, recommender systems, rough sets theory

DOI: 10.3233/JIFS-201073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 907-917, 2021

Authors: Karimzadeh Parizi, Morteza | Keynia, Farshid | Khatibi bardsiri, Amid

Article Type: Research Article

Abstract: Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of …local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems. Show more

Keywords: Optimization, metaheuristic, woodpecker mating algorithm, distance opposition-based learning

DOI: 10.3233/JIFS-201075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 919-946, 2021

Authors: Mostafa, Samih M.

Article Type: Research Article

Abstract: Data preprocessing is a necessary core in data mining. Preprocessing involves handling missing values, outlier and noise removal, data normalization, etc. The problem with existing methods which handle missing values is that they deal with the whole data ignoring the characteristics of the data (e.g., similarities and differences between cases). This paper focuses on handling the missing values using machine learning methods taking into account the characteristics of the data. The proposed preprocessing method clusters the data, then imputes the missing values in each cluster depending on the data belong to this cluster rather than the whole data. The author …performed a comparative study of the proposed method and ten popular imputation methods namely mean, median, mode, KNN, IterativeImputer, IterativeSVD, Softimpute, Mice, Forimp, and Missforest. The experiments were done on four datasets with different number of clusters, sizes, and shapes. The empirical study showed better effectiveness from the point of view of imputation time, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2 score) (i.e., the similarity of the original removed value to the imputed one). Show more

Keywords: Data preprocessing, missing data, imputation, missingness mechanisms

DOI: 10.3233/JIFS-201077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 947-972, 2021

Authors: El-Sharkasy, M. M.

Article Type: Research Article

Abstract: Topological concepts play an important role in applications and solving real-life problems. Among of these concepts are neighbourhood and minimal structure. In this paper, we introduce a new space-based on a generalized system with a binary relation on a nonempty set by using the concept of a minimal structure, which is called a minimal structure approximation space (briefly, MSAS ), and study some of its properties. Also, we compare the advantages of MSAS with neighbourhood approximation space which are based on the same starting point, and apply the concept of MSAS in some examples of chemistry to extraction …and reduct the information. Finally, we investigate the concepts of the separation axioms on MSAS and study some of its properties in the information system as the process of approximation of information. Show more

Keywords: Rough set, approximation space, minimal structure, topological space, T0, T1 and T2, 54A05, 54C55, 54E05

DOI: 10.3233/JIFS-201090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 973-982, 2021

Authors: Li, Huan | Tang, Pengyi | Ma, Yuechao

Article Type: Research Article

Abstract: In this paper, a class of observer-based sliding mode controller is designed, and the finite-time H ∞ control problem of uncertain T-S fuzzy systems with time-varying is studied. Firstly, an integral-type sliding surface function with time-delay is devised based on the state estimator, and sufficient criteria of finite-time bounded and finite-time H ∞ bounded can be obtained for the T-S systems. Moreover, the proposed sliding mode control law is integrated to ensure the dynamics of controlled system into the sliding surface in a finite-time interval. Then, according to the linear matrix inequalities (LMIs), the desired gain matrices of …fuzzy sliding mode controller and state estimator are derived. Finally, effectiveness gives some illustrative examples may be used to display the value of the current proposed method as well as a significant improvement. Show more

Keywords: Finite-time H∞ control, T-S fuzzy system, sliding mode, time-varying delay, linear matrix inequalities (LIMs)

DOI: 10.3233/JIFS-201091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 983-999, 2021

Authors: Chen, Yen-Liang | Chi, Fang-Chi

Article Type: Research Article

Abstract: In the rough set theory proposed by Pawlak, the concept of reduct is very important. The reduct is the minimum attribute set that preserves the partition of the universe. A great deal of research in the past has attempted to reduce the representation of the original table. The advantage of using a reduced representation table is that it can summarize the original table so that it retains the original knowledge without distortion. However, using reduct to summarize tables may encounter the problem of the table still being too large, so users will be overwhelmed by too much information. To solve …this problem, this article considers how to further reduce the size of the table without causing too much distortion to the original knowledge. Therefore, we set an upper limit for information distortion, which represents the maximum degree of information distortion we allow. Under this upper limit of distortion, we seek to find the summary table with the highest compression. This paper proposes two algorithms. The first is to find all summary tables that satisfy the maximum distortion constraint, while the second is to further select the summary table with the greatest degree of compression from these tables. Show more

Keywords: Rough set, reduct, attribute reduction, information system, summarization

DOI: 10.3233/JIFS-201160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1001-1015, 2021

Authors: Wu, Ziheng | Li, Cong | Zhou, Fang | Liu, Lei

Article Type: Research Article

Abstract: Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering …result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance. Show more

Keywords: Fuzzy C-means, data weighting, feature weighting, between-cluster separability

DOI: 10.3233/JIFS-201178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1017-1024, 2021

Authors: Mensah, Patrick Kwabena | Weyori, Benjamin Asubam | Ayidzoe, Mighty Abra

Article Type: Research Article

Abstract: Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test …accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets. Show more

Keywords: Capsule network, convolutional neural network, plant disease, classification, activation maps

DOI: 10.3233/JIFS-201226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1025-1036, 2021

Authors: Wu, Deyin | Li, Yonghong

Article Type: Research Article

Abstract: In this paper, we research a class of axioms in closed G-V fuzzy matroids. The main research method is to transform fuzzy matroids into matroids. First, we study many properties of the basis family of induced matroids, and define a new mapping which can reflect the relationship between bases of induced matroids of a G-V fuzzy matroid. Second, we discuss the new mapping, and reveal the relationship and properties among the fundamental sequence, the induced basis family and the new mapping of a G-V fuzzy matroid. From these relationships and properties, we extract four key attributes: normativity property, inclusion property, …exchange property, and right surjection. Finally, we propose and prove “the induced basis axioms for a closed G-V fuzzy matroid” by these key attributes. With the help of these axioms, a closed G-V fuzzy matroid can be uniquely determined by a finite number sequence, a subset family and a mapping on this subset family when they satisfy above four attributes, and vice versa. Show more

Keywords: Matroids, fuzzy matroids, fundamental sequences, induced matroids, induced basis families, induced basis family mappings

DOI: 10.3233/JIFS-201227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1037-1049, 2021

Authors: Das, Kousik | Naseem, Usman | Samanta, Sovan | Khan, Shah Khalid | De, Kajal

Article Type: Research Article

Abstract: In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, …completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory. Show more

Keywords: Fuzzy mixed graphs, fuzzy mixed degree, adjacency matrices, isomorphism, COVID19

DOI: 10.3233/JIFS-201249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1051-1064, 2021

Authors: Bai, Luyi | Li, Nan | Bai, Huilei

Article Type: Research Article

Abstract: With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based …on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority. Show more

Keywords: RDF, multi-source heterogeneous fuzzy spatiotemporal data, data integration

DOI: 10.3233/JIFS-201258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1065-1082, 2021

Authors: Stefenon, Stéfano Frizzo | Kasburg, Christopher | Freire, Roberto Zanetti | Silva Ferreira, Fernanda Cristina | Bertol, Douglas Wildgrube | Nied, Ademir

Article Type: Research Article

Abstract: The generation of electric energy by photovoltaic (PV) panels depends on many parameters, one of them is the sun’s angle of incidence. By using solar active trackers, it is possible to maximize generation capacity through real-time positioning. However, if the engines that update the position of the panels use more energy than the difference in efficiency, the solar tracker system becomes ineffective. In this way, a time series forecasting method can be assumed to determine the generation capacity in a pre-established horizon prediction to evaluate if a position update would provide efficient results. Among a wide range of algorithms that …can be used in forecasting, this work considered a Neuro-Fuzzy Inference System due to its combined advantages such as smoothness property from Fuzzy systems and adaptability property from neural networks structures. Focusing on time series forecasting, this article presents a model and evaluates the solar prediction capacity using the Wavelet Neuro-Fuzzy algorithm, where Wavelets were included in the model for feature extraction. In this sense, this paper aims to evaluate whether it is possible to obtain reasonable accuracy using a hybrid model for electric power generation forecasting considering solar trackers. The main contributions of this work are related to the efficiency improvement of PV panels. By assuming a hybrid computational model, it is possible to make a forecast and determine if the use of solar tracking is interesting during certain periods. Finally, the proposed model showed promising results when compared to traditional Nonlinear autoregressive model structures. Show more

Keywords: Photovoltaic panels, Neuro-Fuzzy inference system, time series forecasting, wavelets, solar trackers

DOI: 10.3233/JIFS-201279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1083-1096, 2021

Authors: Muhiuddin, G.

Article Type: Research Article

Abstract: In this paper, neutrosophic N -structures are applied to p -ideals of BCI -algebras. In fact, we introduce the notion of neutrosophic N -p -ideal in BCI -algebras, and investigate several properties. Further, we present characterizations of neutrosophic N -p -ideal. Moreover, we consider relations between a neutrosophic N -ideal and a neutrosophic N -p -ideal. Also, we provide conditions for a neutrosophic N -ideal to be a neutrosophic N -p -ideal. …Furthermore, it is proved that the neutrosophic N -structure Q N G over Q is a neutrosophic N p -ideal of Q ⇔ G is a p-ideal of Q where G is a non-empty subset of a BCI -algebras Q . Show more

Keywords: BCI-algebra, p-ideal, neutrosophic set, neutrosophic 𝒩-ideal, neutrosophic 𝒩p-ideal

DOI: 10.3233/JIFS-201309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1097-1105, 2021

Authors: Liu, Peide | Akram, Muhammad | Bashir, Ayesha

Article Type: Research Article

Abstract: This article puts forward an innovative notion of complex picture fuzzy set (CPFS) which is particularly an extension and a generalization of picture fuzzy sets (PFSs) by the addition of phase term in the description of PFSs. The uniqueness of CPFS lies in the capability to manage the uncertainty and periodicity, simultaneously, due to the presence of phase term which broadens the range of CPFS from a real plane to the complex plane of unit disk. We describe and verify the fundamental operations and properties of CPFSs. We introduce the aggregation operators, namely; complex picture fuzzy power averaging and complex …picture fuzzy power geometric operators in CPFSs environment, based on weighted and ordered weighted averaging and geometric operators. We construct multi-criteria decision making (MCDM) problem, using these operators and describe a numerical example to illustrate the validity and competence of this article. Finally, we discuss the advantages of this generalized concept of aggregation technique and analyze a comparative study to demonstrate the superiority and consistency of our model. Show more

Keywords: Complex picture fuzzy set, power aggregation operators, multi-criteria decision making

DOI: 10.3233/JIFS-201385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1107-1128, 2021

Authors: Azhar, Kamran | Zafar, Sohail | Kashif, Agha | Zahid, Zohaib

Article Type: Research Article

Abstract: Fault-tolerant resolving partition is natural extension of resolving partitions which have many applications in different areas of computer sciences for example sensor networking, intelligent systems, optimization and robot navigation. For a nontrivial connected graph G  (V  (G ) , E  (G )), the partition representation of vertex v with respect to an ordered partition Π  = {S i  : 1 ≤ i  ≤ k } of V  (G ) is the k -vector r ( v | Π ) = ( d ( v , S i ) ) i = 1 k , where, d  (v …, S i ) = min {d  (v , x ) |x  ∈ S i }, for i  ∈ {1, 2, …, k }. A partition Π is said to be fault-tolerant partition resolving set of G if r  (u |Π ) and r  (v |Π ) differ by at least two places for all u  ≠ v  ∈ V  (G ). A fault-tolerant partition resolving set of minimum cardinality is called the fault-tolerant partition basis of G and its cardinality the fault-tolerant partition dimension of G denoted by P ( G ) . In this article, we will compute fault-tolerant partition dimension of families of tadpole and necklace graphs. Show more

Keywords: Tadpole graph, necklace graph, partition dimension, fault-tolerant partition dimension

DOI: 10.3233/JIFS-201390

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1129-1135, 2021

Authors: Lu, Xiangyang | Li, Hengyi | Li, Xiaoquan | Xu, Juncai

Article Type: Research Article

Abstract: The security of a train becomes a more critical issue as the train’s speed and the complexity of the railway conditions increases. It is especially true when the train runs on a curved radius rail when the lateral force between the train and the rail is less stable. The rail’s side grinding is a significant problem that affects the train’s safety, especially when the train passes through small radial sections in mountainous areas. The intelligent rail lubrication system is critical to enhancing rails’ safety and efficiency and reducing grease pollution along rail lines. This system is modeled with a force …analysis of train curve motion and numerical simulation of wear power. The lubrication system is constructed with hardware and software. Based on fuzzy group analysis, this system and the adaptive Proportional Integration Differential (PID) controller is presented to improve the lubricative effects. The system test results show that the quality of lubrication control using this system is efficacious; the control convergence is more reliable than the conventional PID controller. Show more

Keywords: Security, curve rail, adaptive PID, lubrication system

DOI: 10.3233/JIFS-201398

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1137-1146, 2021

Authors: Cheng, Yali | Li, Yonghong | Yang, Jie

Article Type: Research Article

Abstract: Linguistic intuitionistic fuzzy sets can qualitatively rather than quantitatively express data in the form of membership degree. But quantitative tools are required to handle qualitative information. Therefore, an improved linguistic scale function, which can more accurately manifest the subjective feelings of decision-makers, is employed to deal with linguistic intuitionistic information. Subsequently, due to some commonly used distance measures do not comprehensively evaluate the information of linguistic intuitionistic fuzzy sets, an improved distance measure of linguistic intuitionistic fuzzy sets is designed. It considers the cross-evaluation information to get more realistic reasoning results. In addition, a new similarity measure defined by nonlinear …Gaussian diffusion model is proposed, which can provide different response scales for different information between various schemes. The properties of these measures are also studied in detail. On this basis, a method in linguistic intuitionistic fuzzy environment is developed to handle multi-attribute decision-making problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method and the influence of the parameters is analyzed. Show more

Keywords: Linguistic intuitionistic fuzzy set, linguistic scale function, distance measure, similarity measure, multi-attribute decision-making

DOI: 10.3233/JIFS-201429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1147-1160, 2021

Authors: Zhang, Yang | Ye, Jianmu

Article Type: Research Article

Abstract: As an important psychological feature that affects the strategic decision-making of top management team (TMT), risk preference exerts important impact on the failure of innovation. Taking Chinese pharmaceutical listed companies from 2011 to 2017 as research samples, this paper uses principal component analysis and probit regression model to empirically test the impact of TMT risk preference on technological innovation failure of firms from the perspective of government intervention. It is found that TMT’s risk preference has a significantly positive impact on the failure of firms’ technological innovation. The regulatory role of government intervention between TMT’s risk preference and failure of …firms’ technological innovation is an inverted U-shaped relationship. Low or high level of government intervention will promote the positive impact of TMT’s risk preference on the failure of technological innovation, and increase the risk of technological innovation failure. The findings of this study contribute to: (1) broaden the research perspective of technological innovation failure. (2) The innovation failure rate can be reduced by constructing or optimizing the TMT and the government intervening in firm innovation through reasonable and perfect technology innovation guidance policies. (3) expand the research means of innovation failure, making the results more convincing. Show more

Keywords: TMT, Risk preference, Failure of technological innovation, Government intervention

DOI: 10.3233/JIFS-201516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1161-1173, 2021

Authors: Shalaby, Raafat | Ammar, Hossam Hassan | Azar, Ahmad Taher | Mahmoud, Mohamed I.

Article Type: Research Article

Abstract: This paper seeks to improve the efficiency of photovoltaic (PV) water pumping system using Fractional-order Fuzzy Maximum Power Point Tracking (FoF-MPPT) control and Gray Wolf Optimization (GWO) technique. The fractional calculus has been used to provide an enhanced model of PV water pumping system to, accurately, describe its nonlinear characteristics. Moreover, three metaheuristic optimizers are applied to tune the parameters of the proposed FoF-MPPT, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the GWO. The FoF-MPPT is intensively tested and compared to the Perturb and Observe (PO), the Incremental Conductance (INC) and the FL-MPPT controllers. A MATLAB-Simscape based physical …model of the PV water pumping system has been developed and simulated for different control techniques with the proposed optimization algorithms. The response of the PV water pumping systems is evaluated under rapidly changing weather conditions to prove the effectiveness of the optimized FoF-MPPT compared to the conventional algorithms. The reliability of the comparative study has been emphasized in terms of several transient tracking and steady- state performance indices under different operating conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FL-MPPT and FoF-MPPT control under different climatic conditions with disturbance rejection and robustness analysis. Show more

Keywords: Optimal Fractional Order Control, Fractional Order Systems, optimal control, Maximum Power Point Tracking

DOI: 10.3233/JIFS-201538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1175-1190, 2021

Authors: Verma, Rajkumar

Article Type: Research Article

Abstract: The development of information measures associated with fuzzy and intuitionistic fuzzy sets is an important research area from the past few decades. Divergence and entropy are two significant information measures in the intuitionistic fuzzy set (IFS) theory, which have gained wider attention from researchers due to their extensive applications in different areas. In the literature, the existing information measures for IFSs have some drawbacks, which make them irrelevant to use in application areas. In order to obtain more robust and flexible information measures for IFSs, the present work develops and studies some parametric information measures under the intuitionistic fuzzy environment. …First, the paper reviews the existing intuitionistic fuzzy divergence measures in detail with their shortcomings and then proposes four new order-α divergence measures between two IFSs. It is worth mentioning that the developed divergence measures satisfy several elegant mathematical properties. Second, we define four new entropy measures called order-α intuitionistic fuzzy entropy measures in order to quantify the fuzziness associated with an IFS. We prove basic and advanced properties of the order-α intuitionistic fuzzy entropy measures for justifying their validity. The paper shows that the introduced measures include various existing fuzzy and intuitionistic fuzzy information measures as their special cases. Further, utilizing the conventional multi-attributive border approximation area comparison (MABAC) model, we develop an intuitionistic fuzzy MABAC method to solve real-life multiple attribute group decision-making problems. Finally, the proposed method is demonstrated by using a practical application of personnel selection. Show more

Keywords: Divergence measure, entropy measure, intuitionistic fuzzy set, MABAC model, personnel selection

DOI: 10.3233/JIFS-201540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1191-1217, 2021