Journal of Intelligent & Fuzzy Systems - Volume 39, issue 3

Authors: Araújo Júnior, José M. | Linhares, Leandro L.S. | Araújo, Fábio M.U. | Almeida, Otacílio M.

Article Type: Research Article

Abstract: Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of …the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators. Show more

Keywords: Fuzzy wavelet neural networks, inferential sensors, neonatal incubators, system identification

DOI: 10.3233/JIFS-190129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2567-2579, 2020

Authors: Yang, Yu | Wang, Jian-Qiang | Wang, Jing

Article Type: Research Article

Abstract: In this study, a multi-criteria group decision making (MCGDM) framework is constructed for electric vehicle fast-charging station (EVFCS) selection using a proportional hesitant fuzzy set (PHFS) that can describe two aspects of information: the possible membership degrees in the hesitant fuzzy elements and associated proportion representing statistical information from different groups. A newly extended distance measure for PHFSs is introduced and an extended maximizing deviation method is constructed to obtain criteria weights objectively. Accordingly, an integrated PHFS-VIKOR (VlseKriterijum-ska Optimizacija I Kompromisno Resenje) method embedded with a new distance measure and extended maximizing deviation method is presented. With increasing concerns about …range anxiety, it is essential to seek an optimal location for EVFCS considering efficient utilization of resources and long-term development of socio-economy under proportional hesitant fuzzy environment. Lastly, an illustration with sensitivity analysis and comparative analyses is provided to demonstrate the validity and robustness of our proposal. Show more

Keywords: Multi-criteria group decision making, proportional hesitant fuzzy set, distance measure, VIKOR, maximizing deviation method

DOI: 10.3233/JIFS-190156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2581-2596, 2020

Authors: Kim Son, Nguyen Thi | Long, Hoang Viet

Article Type: Research Article

Abstract:  In this paper, we consider Cauchy problems for second order fuzzy functional differential equations (DEs) with generalized Hukuhara (gH) derivatives. We study the solvability of the problem by using Perov fixed point theorem in ordered partial metric spaces. The data monotony, continuity, diferentiability dependence of mild solutions with respect to parameters are investigated via weak Picard operators. Moreover, the stability of mild solutions is addressed in sense of Ulam-Hyers stability related to the technique of coefficient matrix converges to zero. Some examples are presented to demonstrate for theoretical results.

Keywords: Fuzzy functional DEs, gH-derivatives, ordered partial metric spaces

DOI: 10.3233/JIFS-190222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2597-2610, 2020

Authors: Lee, Pin-Chan | Lo, Tzu-Ping | Sun, Haoqing | Wen, I-Jyh

Article Type: Research Article

Abstract: Structure of convolutional neural network (CNN) applied for image recognition requires large numbers of tuning for designated datasets in practice. It is a time-consuming process to finally come up with a feasible structure for specific requirement. This paper proposes a method based on Taguchi method which can efficiently determine the optimal structure of hyperparameters combination. Five hyperparameters with four levels are defined as control factors and two indicators are chosen to measure the performance of CNN structure. L 16  (45 ) orthogonal array is used to arrange the experiment. S/N ratio and main effect plot are used to identify the …optimal structure (hyperparameter combination) of CNN. The classic case of MNIST is employed to verify the practicability of the proposed method. Results show that the proposed method can identify the optimal CNN structure efficiently and also rank the significance priority of hyperparameters. Show more

Keywords: Convolutional neural network, hyperparameter combination, optimization algorithm, Taguchi method

DOI: 10.3233/JIFS-190275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2611-2625, 2020

Authors: Xian, Sidong | Guo, Hailin | Chai, Jiahui | Wan, Wenhua

Article Type: Research Article

Abstract: Hesitant fuzzy linguistic term set (HFLTS) can handle the qualitative and hesitant information in multiple attribute decision making (MADM) problems which are widely used in various fields. However, the experts’ evaluation of information is not completely reliable in the situation where their own knowledge background is insufficient. In order to deal with deviations due to incomplete reliability of the evaluation, this paper first proposes the interval probability hesitant fuzzy linguistic variable (IPHFLV), which takes the HFLTS as the evaluation part and adds a novel element-reliability of evaluation, thus can describe the different credibility of information evaluation due to the familiarity …of experts with schemes and the differences in knowledge cognition. The operation rules and comparison methods are also illustrated. Particularly, under the inspiration of probability theory, we propose the possibility degree of the IPHFLVs. Then we propose IPHFL-AHP based on the AHP and interval probability hesitant fuzzy linguistic variable. Especially, the general geometric consistency index (G-GCI) based on the unbiased estimator of the variance is presented to measure the consistency and the iterative algorithm is constructed to improve the consistency. We use the possibility degree to calculate the priority vector to acquire the total ranking and introduce the process of IPHFL-AHP. Finally, case study of talent selection is given to illustrate the effectiveness and feasibility of the proposed method. Show more

Keywords: Interval probability hesitant fuzzy linguistic variable, interval probability hesitant fuzzy linguistic analytic hierarchy processe, general geometric consistency index, possibility degreee, reliability

DOI: 10.3233/JIFS-190427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2627-2645, 2020

Authors: Feng, Naidan | Liang, Yongquan

Article Type: Research Article

Abstract:  Aiming at the imprecise and uncertain data and knowledge, this paper proposes a novel prior assumption by the rough set theory. The performance of the classical Bayesian classifier is improved through this study. We applied the operations of approximations to represent the imprecise knowledge accurately, and the concept of approximation quality is first applied in this method. Thus, this paper provides a novel rough set theory based prior probability in classical Bayesian classifier and the corresponding rough set prior Bayesian classifier. And we chose 18 public datasets to evaluate the performance of the proposed model compared with the classical Bayesian …classifier and Bayesian classifier with Dirichlet prior assumption. Sufficient experimental results verified the effectiveness of the proposed method. The mainly impacts of our proposed method are: firstly, it provides a novel methodology which combines the rough set theory with the classical probability theory; secondly, it improves the accuracy of prior assumptions; thirdly, it provides an appropriate prior probability to the classical Bayesian classifier which can improve its performance only by improving the accuracy of prior assumption and without any effect to the likelihood probability; fourthly, the proposed method provides a novel and effective method to deal with the imprecise and uncertain data; last but not least, this methodology can be extended and applied to other concepts of classical probability theory, which providing a novel methodology to the probability theory. Show more

Keywords: Rough set theory, prior assumption, Bayesian classifier, approximation quality, probability theory

DOI: 10.3233/JIFS-190517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2647-2655, 2020

Authors: Pham, Ngoc Thuy

Article Type: Research Article

Abstract: This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance …of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer. Show more

Keywords: Neural networks, sensorless vector control, six phase induction motor drive, stator current MRAS based on speed observer, backstepping control, port controlled hamiltonian

DOI: 10.3233/JIFS-190540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2657-2677, 2020

Authors: Rao, G. Madhukar | Ramesh, Dharavath

Article Type: Research Article

Abstract: In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific …region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD. Show more

Keywords: Convolutional neural network, deep learning, traffic data visualization, traffic prediction

DOI: 10.3233/JIFS-190601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2679-2691, 2020

Authors: Farag, Wael

Article Type: Research Article

Abstract: In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT ) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen …each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions. Show more

Keywords: Computer vision, self-driving car, autonomous driving, ADAS, vehicle detection, vehicle tracking

DOI: 10.3233/JIFS-190634

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2693-2710, 2020

Authors: Hu, Yangguang | Xiao, Mingqing | Li, Shaoyi | Yang, Yao | Wu, Sijie

Article Type: Research Article

Abstract: Infrared target tracking is increasingly becoming important for various applications in recent years. However, it is still a challenging task as limited information can be obtained from the infrared image. Inspired by the excellent performance of deep tracker, a novel tracker based on MDNet is proposed. As the prior information has great value for target tracking, a modified Back-Propagation network is used for predicting the scale of target during tracking. The result of the prediction is used for generating candidate windows for online learning, which can improve the performance of tracker. To evaluate the proposed tracking algorithm, we performed experiments …on the VOT-TIR2016 and AMCOM infrared data. The experimental results demonstrate that our algorithm provides a 1.94% relative gain in accuracy and 21.4% in robustness on VOT-TIR2016 when compared with MDNet. Show more

Keywords: Artificial intelligence, infrared target tracking, convolutional neural network, scale prediction

DOI: 10.3233/JIFS-190787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2711-2723, 2020

Authors: Ni, Xun-You | Lu, Weite | Zhang, Chunqin | Liu, Yong | Zhao, Jing

Article Type: Research Article

Abstract: Parking spaces are insufficient and are plagued by over-consumption in hot areas. To assist drivers easily in identifying available parking spaces, parking variable message signs are commonly adopted to display information on space availability. This paper analyzes the performance of various information provision strategies. To achieve this objective, we first present the mechanisms of the information provision strategies. Then, the information provision strategies are classified into three categories: regular, symmetric, and discriminative. The regular strategies provide the collected parking information directly to drivers; the symmetric schemes employ the equal threshold values for all parking lots; and the discriminative schedules adopt …an independent threshold value for each parking lot. The threshold value provides an upper limit for the Space Occupancy Percentage (SOP): when the SOP is larger than the threshold value, the parking lot status becomes FULL; otherwise, it is displayed having available spaces. Finally, an agent-based simulation model is introduced to describe the parking and traffic conditions. The results indicate that both the symmetric and discriminative strategies significantly decrease the highest failure rate and average travel time, whereas the latter performs better. The results of this comparative analysis can assist in the configuration and operation of an urban parking guidance and information system. Show more

Keywords: Intelligent transportation system, parking VMS, display problem, provision strategies, agent-based simulation

DOI: 10.3233/JIFS-190962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2725-2735, 2020

Authors: Zhang, Xia | Sun, Hao | Jin, Xuanzhu | Esangbedo, Moses Olabhele

Article Type: Research Article

Abstract: This paper focuses on a new model to reach the existence of equilibrium in a pure exchange economy with fuzzy preferences (PXE-FP). The proposed model integrates exchange, consumption and the agent’s fuzzy preference in the consumption set. We set up a new fuzzy binary relation on the consumption set to evaluate the fuzzy preferences. Also, we prove that there exists a continuous fuzzy order-preserving function in the consumption set under certain conditions. The existence of a fuzzy competitive equilibrium for the PXE-FP is confirmed through a new result on the existence of fuzzy Nash equilibrium for fuzzy non-cooperative games. The …payoffs of all strategy profiles for any agent are fuzzy numbers in fuzzy non-cooperative games. Finally, we show that the fuzzy competitive equilibrium could be characterized as a solution to an associated quasi-variational inequality, giving rise to an equilibrium solution. Show more

Keywords: Pure exchange economy, fuzzy preference, fuzzy utility function, fuzzy competitive equilibrium, quasi-variational inequality

DOI: 10.3233/JIFS-191011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2737-2752, 2020

Authors: Zhou, Hui | Ren, Haiping

Article Type: Research Article

Abstract: In reliability field, the probabilities of basic events are often treated as exact values in conventional fault tree analysis. However, for many practical systems, because the concept of events may be ambiguous, the factors affecting the occurrence of events are complex and changeable, so it is difficult to obtain accurate values of the occurrence probability of events. Fuzzy sets can well deal with these situations. Thus this paper will develop a novel fault tree analysis method in the assumption of the values of probability of basic events expressed with triangular intuitionistic fuzzy numbers. First, a new ranking function of triangular …intuitionistic numbers is established, which can reflect the behavior factors of the decision maker. Then a novel fault tree analysis method is put forward on the basis of operational laws and the proposed ranking function of triangular intuitionistic numbers. Finally, an example of weapon system “automatic gun” is employed to show that the proposed fault tree analysis method is feasible and effective. Show more

Keywords: Fault tree analysis, triangular intuitionistic numbers, ranking function, bottom event

DOI: 10.3233/JIFS-191018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2753-2761, 2020

Authors: Zeng, Biqing | Zeng, Feng | Yang, Heng | Zhou, Wu | Xu, Ruyang

Article Type: Research Article

Abstract: Aspect-based sentiment analysis (ABSA) is a hot and significant task of natural language processing, which is composed of two subtasks, the aspect term extraction (ATE) and aspect polarity classification (APC). Previous researches generally studied two subtasks independently and designed neural network models for ATE and APC respectively. However, it integrates various manual features into the model, which will consume plenty of computing resources and labor. Moreover, the quality of the ATE results will affect the performance of APC. This paper proposes a multi-task learning model based on dual auxiliary labels for ATE and APC. In this paper, general IOB labels, …and sentimental IOB labels are equipped to efficiently solve both ATE and APC tasks without manual features adopted. Experiments are conducted on two general ABSA benchmark datasets of SemEval-2014. The experimental results reveal that the proposed model is of great performance and efficient for both ATE and APC tasks compared to the main baseline models. Show more

Keywords: Multi-task learning, aspect term extraction, aspect polarity classification, sentiment classification

DOI: 10.3233/JIFS-191047

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2763-2774, 2020

Authors: Onasanya, B.O. | Atamewoue, T.S. | Hoskova-Mayerova, S.

Article Type: Research Article

Abstract: Fuzzy set theory and also the hypergroups in the sense of Marty are both generalizations of some existing mathematical concepts which are used for modeling many real life situations. The main purpose of this paper is the study of the link between fuzzy sets and fuzzy hypergroups and fuzzy semihypergroups. As a matter of fact, some commutative fuzzy hypergroups and fuzzy semihypergroups have been constructed from fuzzy set and some of their properties were investigated.

Keywords: Hypergroup, fuzzy sets, fuzzy hypergroup

DOI: 10.3233/JIFS-191054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2775-2782, 2020

Authors: Hu, Qian | Qin, Ke-Yun

Article Type: Research Article

Abstract: The construction of concept lattices is an important research topic in formal concept analysis. Inspired by multi-granularity rough sets, multi-granularity formal concept analysis has become a new hot research issue. This paper mainly studies the construction methods of concept lattices in multi-granularity formal context. The relationships between concept forming operators under different granularity are discussed. The mutual transformation methods of formal concepts under different granularity are presented. In addition, the approaches of obtaining coarse-granularity concept lattice by fine-granularity concept lattice and fine-granularity concept lattice by coarse-granularity concept lattice are examined. The related algorithms for generating concept lattices are proposed. The …practicability of the method is illustrated by an example. Show more

Keywords: Multi-granularity, formal concept analysis, formal concept, formal concept lattice

DOI: 10.3233/JIFS-191090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2783-2790, 2020

Authors: Li, Xingguang | Song, Wenjun | Liang, Zonglin

Article Type: Research Article

Abstract: In speech emotion recognition, most emotional corpora generally have problems such as inconsistent sample length and imbalance of sample categories. Considering these problems, in this paper, a variable length input CRNN deep learning model based on Focal Loss is proposed for speech emotion recognition of anger, happiness, neutrality and sadness in IEMOCAP emotional corpus. In this model, Firstly, a variable-length strategy is introduced to input the speech spectra of the filled speech samples into CNN. Then the effective part of the input sequence is preserved and output by masking matrix and convolution layer. Thirdly, the effective output of input sequence …is input into BiGRU network for learning. Finally, the focal loss is used for network training to control and adjust the contribution of various samples to the total loss. Compared with the traditional speech emotion recognition model, simulations show that our method can effectively improve the accuracy and performance of emotion recognition. Show more

Keywords: Speech emotion recognition, spectrograms, CRNN, focal loss

DOI: 10.3233/JIFS-191129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2791-2796, 2020

Authors: Akram, Muhammad | Luqman, Anam | Al-Kenani, Ahmad N.

Article Type: Research Article

Abstract: An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine …certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks. Show more

Keywords: Rough fuzzy approximations, rough fuzzy digraphs, information granulation, algorithms

DOI: 10.3233/JIFS-191165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2797-2816, 2020

Authors: Javan Bakht, Ahmad | Motameni, Homayun | Mohamadi, Hosein

Article Type: Research Article

Abstract: One of the most important problems in directional sensor networks is k -coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k -coverage problem). Due to the significance of solving the imbalanced k -coverage problem, this paper proposes a learning …automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k -coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem. Show more

Keywords: Visual sensor networks, balanced coverage, k-coverage, learning automata

DOI: 10.3233/JIFS-191170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2817-2829, 2020

Authors: He, Peng | Wang, Xue-Ping

Article Type: Research Article

Abstract: Let D be a finite distributive lattice with n join-irreducible elements. It is well-known that D can be represented as the congruence lattice of a rectangular lattice L which is a special planer semimodular lattice. In this paper, we shall give a better upper bound for the size of L by a function of n , improving a 2009 result of G. Grätzer and E. Knapp.

Keywords: Distributive lattice, Congruence lattice, Rectangular lattice AMS classification: 06C10; 06B10, 06C10, 06B10

DOI: 10.3233/JIFS-191220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2831-2843, 2020

Authors: Hamidi, Mohammad | Rahmati, Marzieh | Rezaei, Akbar

Article Type: Research Article

Abstract: According to Boolean logic, a disjunctive normal form (DNF) is a canonical normal form of a logical formula consisting of a disjunction of conjunctions (it can also be described as an OR of AND’s). For each table an arbitrary T.B.T is given (total binary truth table) Boolean expression can be written as a disjunctive normal form. This paper considers a notation of a T.B.T, introduces a new concept of the hypergraphable Boolean functions and the Boolean functionable hypergraphs with respect to any given T.B.T. This study defines a notation of unitors set on switching functions and proves that every T.B.T …corresponds to a minimum Boolean expression via unitors set and presents some conditions on a T.B.T to obtain a minimum irreducible Boolean expression from switching functions. Indeed, we generate a switching function in different way via the concept of hypergraphs in terms of Boolean expression in such a way that it has a minimum irreducible Boolean expression, for every given T.B.T. Finally, an algorithm is presented. Therefore, a Python programming(with complete and original codes) such that for any given T.B.T, introduces a minimum irreducible switching expression. Show more

Keywords: Switching function, hypergraphable Boolean function, Boolean functionable hypergraph, Boolean function–based hypergraph, Unitor, T.B.T.

DOI: 10.3233/JIFS-191230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2845-2859, 2020

Authors: Sun, Qiong | Tan, Zhiyong | Zhou, Xiaolu

Article Type: Research Article

Abstract: In this study, support vector machine (SVM) and back-propagation (BP) neural networks were combined to predict the workload of cloud computing physical machine, so as to improve the work efficiency of physical machine and service quality of cloud computing. Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, …the prediction accuracy of SVM, BP, RBF and SVM + BP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively. Show more

Keywords: Back propagation neural network, support vector machine, cloud computing, workload prediction

DOI: 10.3233/JIFS-191266

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2861-2867, 2020

Authors: Chang, Shih-Jui | Hsu, Chi-I | Lin, Chin-Tsai

Article Type: Research Article

Abstract: This research combines the Fuzzy Analytic Hierarchy Process (FAHP) with Case-Based Reasoning (CBR) to evaluate the intention of adoption of web ATM services. Compared with physical ATM service, web ATM allows users to perform financial transactions over the internet conveniently. Based on literature and considering the characteristics of web ATM, this study constructs a model for web ATM adoption that comprises three dimensions: The knowledge, the potential value, and the security. 222 valid user questionnaires are collected, and factor analysis is used to verify the factor structure of the decision hierarchy. FAHP is then used to calculate the weights of …criteria with six experts through pairwise comparisons. Finally, FAHP weights are integrated into a CBR prediction mechanism for evaluating a user’s adoption intention toward web ATM. The results are helpful for financial institutions to understand and to evaluate the user behavior toward internet banking service adoption. Show more

Keywords: Fuzzy analytic hierarchy process, case-based reasoning, web ATM, innovation adoption

DOI: 10.3233/JIFS-191408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2869-2879, 2020

Authors: Dong, Hongwei | Yang, Liming

Article Type: Research Article

Abstract:  Symmetric loss functions are widely used in regression algorithms to focus on estimating the means. Huber loss, a symmetric smooth loss function, has been proved that it can be optimized with high efficiency and certain robustness. However, mean estimators may be poor when the noise distribution is asymmetric (even outliers caused heavy-tailed distribution noise) and estimators beyond the means are necessary. Under the circumstances, quantile regression is a natural choice which estimates quantiles instead of means through asymmetric loss functions. In this paper, an asymmetric Huber loss function is proposed to implement different penalty for overestimation and underestimation so as …to deal with more general noise. Moreover, a smooth truncated version of the proposed loss is introduced to enhance stronger robustness to outliers. Concave-convex procedure is developed in the primal space with the proof of convergence to handle the non-convexity of the involved truncated objective. Experiments are carried out on both artificial and benchmark datasets and robustness of the proposed methods are verified. Show more

Keywords: Support vector regression, training in the primal, robustness, asymmetric Huber loss, concave-convex procedure

DOI: 10.3233/JIFS-191429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2881-2892, 2020

Authors: Wu, Huaiguang | Xie, Pengjie | Zhang, Huiyi | Li, Daiyi | Cheng, Ming

Article Type: Research Article

Abstract: The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for …detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system. Show more

Keywords: Chest X-ray, CNN, adaptive median filter, RF, image classification

DOI: 10.3233/JIFS-191438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2893-2907, 2020

Authors: Lei, Fan | Lu, Jianping | Wei, Guiwu | Wu, Jiang | Wei, Cun | Guo, Yanfeng

Article Type: Research Article

Abstract: In this paper, we provide the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In such method, the linguistic information firstly is shifted into probabilistic linguistic information. For obtaining the weight information of the attribute, two optimization models are built on the basis of the basic idea of grey relational analysis (GRA), by which the attribute weights can be obtained. Then, the optimal alternative is obtained through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational coefficient (GRC) from the PLPIS and the smallest GRC …form probabilistic linguistic negative ideal solution (PLNIS). Finally, a case study for waste incineration plants location problem is given to demonstrate the advantages of the developed methods. Show more

Keywords: multiple attribute group decision making (MAGDM), probabilistic linguistic term sets (PLTSs), GRA method, incomplete weight information, waste incineration plants

DOI: 10.3233/JIFS-191443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2909-2920, 2020

Authors: Pirayesh, Pardis | Motameni, Homayun | Akbari, Ebrahim

Article Type: Research Article

Abstract: Fuzzy logic is a multi-valued concept, whose emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of [0,1]. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. For that purpose, we first obtained the best classification for each defuzzifier, based on which a classified fuzzy was implemented. Nonetheless, the fuzzy system used in this research was classified based on statistical computations. To achieve the best classification, five defuzzification methods (Mean Of Max, Max …Of Membership, Largest Of Max, Smallest Of Max, and Central Average) competed in 16 roles each in five classes (different matrices). Finally, Mean of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among 5 different defuzzification methods. Show more

Keywords: Fuzzy role, defuzzifier, terminology

DOI: 10.3233/JIFS-191447

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2921-2934, 2020

Authors: Shang, Bo | Du, Xingyu

Article Type: Research Article

Abstract: An intelligent decision analytic framework for dealing with complex decision-making risk system is presented and Bayesian network (BN) approach is utilized to evaluate the influence of multilevel uncertainty in various risks (e.g., social, natural, economic, intracompany risks) on decision-making deviation of Chinese hydropower corporations. The technique of fuzzy probability is approached to calculate intricate parameters to the question of inference learning through the sensitivity and influence power analysis, the results of back inference show that there exists the risk transformation mechanism from external uncertain risks (e.g., social risks, ecological environment factors) to hydropower corporations’ internal uncertainties closely relating to economic …uncertainties through strategic planning. The study concerning identification and intelligent analysis of uncertain risks in decision-making process illustrates the feasibility and validity of applying BN and its pragmatic implications on hydropower corporations strategic planning and guidance in operational management. Show more

Keywords: Chinese hydropower corporations, decision makers, decision-making risks, Bayesian network, triangular fuzzy numbers

DOI: 10.3233/JIFS-191469

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2935-2945, 2020

Authors: Shang, Bo | Huang, Taozhen | Du, Xingyu

Article Type: Research Article

Abstract: In a significant period of Chinese energy reformation to the point of expediting revolution in energy production and consumption, and promoting green low-carbon upgrading transformation of energy electricity, Chinese government has to implement the obligatory policy of renewable portfolio standards (RPS) with specific institutional provisions sternly. The renewable energy quotas in thermal power industry with carbon emission abatement constraints particularly have a latent impact on the behavior of thermal electricity producers, which is ineluctably involved in electricity connection of grid companies. To make clear the positive role in boosting investment in renewable energy generation in thermal power industry under mandatory …quotas requirements, we will utilize evolutionary game based on system dynamics (SD) to tackle with the sophisticated nexus among the government, thermal power producers and grid companies. We begin analysis of general evolutionary strategy stability in scenario ll by dynamically adjusting values of external variables of SD model to uncover pivotal variables affecting evolutionary strategy stability. Then in scenario I, the dynamic punishment structure consisting of cost elements to spark off the emulation of optimal manoeuvre selections of tripartite game agents is amended based on the simulation of vital variables affecting evolutionary strategy stability in scenario II. The significant conclusions provide decision-making support and management enlightenment for Chinese government to edge renewable energy generation capacity of thermal power producers and constrained degree of dynamic penalty for grid companies. Show more

Keywords: Renewable portfolio standards (RPS), China’s power market, Carbon abatement, Government regulation, evolutionary strategy stability, system dynamics

DOI: 10.3233/JIFS-191470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2947-2975, 2020

Authors: Anushiadevi, R. | Praveenkumar, Padmapriya | Rayappan, John Bosco Balaguru | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Digital image steganography algorithms usually suffer from a lossy restoration of the cover content after extraction of a secret message. When a cover object and confidential information are both utilised, the reversible property of the cover is inevitable. With this objective, several reversible data hiding (RDH) algorithms are available in the literature. Conversely, because both are diametrically related parameters, existing RDH algorithms focus on either a good embedding capacity (EC) or better stego-image quality. In this paper, a pixel expansion reversible data hiding (PE-RDH) method with a high EC and good stego-image quality are proposed. The proposed PE-RDH method was …based on three typical RDH schemes, namely difference expansion, histogram shifting, and pixel value ordering. The PE-RDH method has an average EC of 0.75 bpp, with an average peak signal-to-noise ratio (PSNR) of 30.89 dB. It offers 100% recovery of the original image and confidential hidden messages. To protect secret as well as cover the proposed PE-RDH is also implemented on the encrypted image by using homomorphic encryption. The strength of the proposed method on the encrypted image was verified based on a comparison with several existing methods, and the approach achieved better results than these methods in terms of its EC, location map size and imperceptibility of directly decrypted images. Show more

Keywords: Reversible data hiding, pixel expansion, reversible data hiding in encrypted image, imperceptibility, homomorphic encryption

DOI: 10.3233/JIFS-191478

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2977-2990, 2020

Authors: Kaul, Sonam Devgan | Hatzinakos, Dimitrios

Article Type: Research Article

Abstract: In this work, we will be investigating, developing and implementing an intelligent RFID system in conjunction with a fuzzy data classification system, to greatly enhance and secure financial transactions and improve operational efficiency in the banking environment. The innovative part of this research is to provide an efficient solution to the challenge that may arise from the need to expertly and automatically match the profile of customer and banker and solve the vagueness in customer/banking profiling. Our proposal offers an expert, secure, efficient and comprehensive framework, methodology and its application in financial environments to develop customer to banker profile matching …and availability via an expert agent multi level fuzzy data classification system. Foremost, according to clients and banking staff members weighted attributes, exact match has been established according to highest degree of relevance by utilizing Matlab fuzzy inference system. Then, to communicate output of a match profile engine from one party to another, to show profiling effectiveness and to do implementation; secure, privacy preserving, and comprehensive intelligent RFID profiling authentication system has been designed and verified by Scyther tool. Show more

Keywords: Authentication system, fuzzy inference system, intelligent system, matlab, profiling system, RFID, Scyther

DOI: 10.3233/JIFS-191480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 2991-3010, 2020

Authors: Munirathinam, T. | Ganapathy, Sannasi | Kannan, Arputharaj

Article Type: Research Article

Abstract: Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data storage and communication. …For this purpose, we propose a new e-healthcare system for monitoring the dead disease level by using the technologies such as IoT and Cloud with the help of deep learning approach and fuzzy rules with temporal features. In this system, the medical data is retrieved from various located patients who are utilizing the e-healthcare assisting devices. First, the retrieved and encrypted data is stored in cloud by applying a newly proposed secured cloud storage algorithm. Second, the stored data can be retrieved the data as original data by applying the decryption process. Third, a new cloud framework is introduced for predicting the status of heart beat rates and diabetes levels by using the medical data that is created by applying the UCI Repository dataset. In addition, a new deep learning approach which applies the Convolutional Neural Network for predicting the disease severity. The experimental results are obtained by conducting various experiments for the proposed model by using the dataset and the hospital patient records. The proposed model results outperforms the available disease prediction systems in terms of prediction accuracy. Show more

Keywords: Internet of things (IoT), CNN, cryptography, encryption, decryption, elliptic curve cryptography and e-healthcare.

DOI: 10.3233/JIFS-191490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3011-3023, 2020

Authors: Işık, Hüseyin | Sezen, Muzeyyen Sangurlu

Article Type: Research Article

Abstract: In this work, we prove a new fixed point theorem in the setting fuzzy metric spaces. The fuzzy metric space considered here is assumed to have two partial orders defined on it. We introduce a new approach to the existence of a fixed point of a function satisfying the two constraint inequalities. An example is included which illustrates new results of this paper. Moreover, an application of our result to the study of integral equations is provided.

Keywords: Common fixed points, constraint inequalities, G-complete fuzzy metric spaces, partial order

DOI: 10.3233/JIFS-191521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3025-3032, 2020

Authors: Wang, Zecong | Parvin, Hamid | Qasem, Sultan Noman | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: A bad partition in an ensemble will be removed by a cluster ensemble selection framework from the final ensemble. It is the main idea in cluster ensemble selection to remove these partitions (bad partitions) from the selected ensemble. But still, it is likely that one of them contains some reliable clusters. Therefore, it may be reasonable to apply the selection phase on cluster level. To do this, a cluster evaluation metric is needed. Some of these metrics have been recently introduced; each of them has its limitations. The weak points of each method have been addressed in the paper. Subsequently, …a new metric for cluster assessment has been introduced. The new measure is named Balanced Normalized Mutual Information (BNMI) criterion. It balances the deficiency of the traditional NMI-based criteria. Additionally, an innovative cluster ensemble approach has been proposed. To create the consensus partition considering the elected clusters, a set of different aggregation-functions (called also consensus-functions) have been utilized: the ones which are based upon the co-association matrix (CAM), the ones which are based on hyper graph partitioning algorithms, and the ones which are based upon intermediate space. The experimental study indicates that the state-of-the-art cluster ensemble methods are outperformed by the proposed cluster ensemble approach. Show more

Keywords: Cluster ensembles, enhanced stability, extended-EAC, CAM, cluster evaluation

DOI: 10.3233/JIFS-191531

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3033-3055, 2020

Authors: Jamwal, Prashant K. | Hussain, Shahid

Article Type: Research Article

Abstract: Globalization of business around the world has turned individual firms into groups of collaborating business units whereby companies do not operate in isolation but function as integral part of big supply chain networks (SCN). Organization of SCN is quite complex as they operate with uncertainty in demands and operations. However, supply chain networks are required to be optimized in order to reduce the overall supply chain cost and increase service levels. Since these objectives are normally conflicting and incommensurable, instead of a singular solution, it is preferred to obtain a set of equitable solutions which is commonly referred to as …set of Pareto optimal solutions. Subsequently, a suitable solution can be chosen by the user from the set of equitable solutions. In the present research, a multi-echelon SCN problem is formulated and two important objectives are identified. It is desired to minimize the total cost of supply chain network and at the same time maximize customer service level in terms of supply to demand ratio. Simultaneous optimization of these objectives has been carried out using an evolutionary algorithm (EA) called NSGA-II, which works with population of SCN solutions and is more likely to provide set of globally optimized solutions. However, at the conclusion of optimization, user needs to select a final solution from the Pareto optimal set of solutions after careful analysis. Existing approaches to carry out such analysis are complex and time consuming. We propose a novel method involving fuzzy logic in this research by which fuzzy indices corresponding to each of the solutions in the Pareto Front (PF) are obtained. Fuzzy indices of all the Pareto optimal SCN solutions are later compared to reach to a final solution from the Pareto optimal set. Show more

Keywords: Evolutionary algorithms, fuzzy logic, pareto optimal solutions, supply chain networks

DOI: 10.3233/JIFS-191534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3057-3066, 2020

Authors: Yang, Hai-Long | Zhou, Jia-Jia

Article Type: Research Article

Abstract: By combining interval-valued Pythagorean fuzzy sets with rough sets, the interval-valued Pythagorean fuzzy rough set model is first constructed in this paper. The connections between special interval-valued Pythagorean fuzzy relations and interval-valued Pythagorean fuzzy approximation operators are established subsequently. Then, we study the axiomatic characterizations of interval-valued Pythagorean fuzzy lower and upper approximation operators. Different axiom sets of interval-valued Pythagorean fuzzy set-theoretic operators ensure the existence of different types of interval-valued Pythagorean fuzzy relations producing the same operators. Finally, we give an example to illustrate the practical application of the newly proposed model.

Keywords: Interval-valued pythagorean fuzzy sets, interval-valued pythagorean fuzzy relations, interval-valued pythagorean fuzzy rough sets, axiomatic characterizations

DOI: 10.3233/JIFS-191539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3067-3084, 2020

Authors: Nawar, A.S. | El-Bably, M.K. | El-Atik, Abd El Fattah

Article Type: Research Article

Abstract: Covering-based rough sets are important generalizations of the classical rough sets of Pawlak. In this paper, by means of j -neighborhoods, complementary j -neighborhoods and j -adhesions, we build some new different types of j -covering approximations based rough sets and study related properties. Also, we explore the relationships between the considered j -covering approximations and investigate the properties of them. Using different neighborhoods, some different general topologies are generated as topologies induced from a binary relation. Finally, an interesting application of the new types of covering-based rough sets to the rheumatic fever is given.

Keywords: Covering-based rough sets, approximation space, topology, neighborhood, complementary neighborhood

DOI: 10.3233/JIFS-191542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3085-3098, 2020

Authors: Bagheri, M. | Ebrahimnejad, A. | Razavyan, S. | Hosseinzadeh Lotfi, F. | Malekmohammadi, N.

Article Type: Research Article

Abstract: A transportation problem basically deals with the problem which aims to minimize the total transportation cost or maximize the total transportation profit of distributing a product from a number of sources or origins to a number of destinations. While, in general, most of the real life applications are modeled as a transportation problem (TP) with the multiple, conflicting and incommensurate objective functions. On the other hand, for some reason such as shortage of information, insufficient data or lack of evidence, the data of the mentioned problem are not always exact but can be fuzzy. This type of problem is called …fuzzy multi-objective transportation problem (FMOTP). There are a few approaches to solve the FMOTPs. In this paper, a new fuzzy DEA based approach is developed to solve the Fully Fuzzy MOTPs (FFMOTPs) in which, in addition to parameters of the MOTPs, all of the variables are considered fuzzy. This approach considers each arc in a FFMOTP as a decision making unit which produces multiple fuzzy outputs using the multiple fuzzy inputs. Then, by using the concept of the common set of weights (CSW) in DEA, a unique fuzzy relative efficiency is defined for each arc. In the following, the unique fuzzy relative efficiency is considered as the only attribute for the arcs. In this way, a single objective fully fuzzy TP (FFTP) is obtained that can be solved using the existing standard algorithms for solving this kind of TPs. A numerical example is provided to illustrate the developed approach. Show more

Keywords: Fuzzy multi-objective transportation problem, data envelopment analysis, fuzzy arithmetic, common set of weights

DOI: 10.3233/JIFS-191560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3099-3124, 2020

Authors: Wei, Guiwu | He, Yan | Lei, Fan | Wu, Jiang | Wei, Cun | Guo, Yanfeng

Article Type: Research Article

Abstract: In recent years, with the increased voice for protecting the environment by the people all over the world, the governments also have actively adopted more and more measures to further promote environmental conservation and sustainable development. Traditional procurement approaches have not well updated to the current needs of the society, especially for the retail industry which is in relation to the national economy due to numerous products and different suppliers being involved. Therefore, the need for green procurement is more important. The qualified green supplier selection is the core of green procurement, which is the utmost importance in the business …competition throughout the supply chain in today’s strong business competition. Thus, in order to obtain the optimal green supplier, integration of Entropy weights and multi-attributive border approximation area comparison (MABAC) under uncertain probabilistic linguistic sets (UPLTSs) has offered a novel integrated model, in which information Entropy is utilized for calculating objective weights with UPLTSs to acquire the final ranking result of green supplier. Besides, so as to indicate the applicability of devised method, it is confirmed by a numerical case for green supplier selection. Some comparative studies are made with some existing methods. The proposed method can also serve for selecting suitable alternative successfully in other selection problems. Show more

Keywords: Multiple attribute group decision making (MAGDM), uncertain probabilistic linguistic term sets (UPLTSs), MABAC method, entropy method, green supplier selection

DOI: 10.3233/JIFS-191584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3125-3136, 2020

Authors: Bera, Sanchari | Pal, Madhumangal

Article Type: Research Article

Abstract: In this paper, an unprecedented kind of fuzzy graph designated as m -polar interval valued fuzzy graph (m -PIVFG) is defined. Complement of the m -PIVFG open and closed neighborhood degrees of m -PIVFG are discussed. The other algebraic properties such as density, regularity, irregularity of the m -PIVFG are investigated. Moreover, some basic results on regularity and irregularity of m -PIVFG are proved. Free nodes and busy nodes of m -PIVFG is explored with some basic theorems and examples. Lastly, an application of m -PIVFG is described.

Keywords: m-polar interval-valued fuzzy graph, balanced m-PIVFG, regularity and irregularity in m-PIVFG, density of m-PIVFG, free and busy nodes in m-PIVFG

DOI: 10.3233/JIFS-191587

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3137-3150, 2020

Authors: Li, Xiaoping | Tao, Yujie | Li, Yanhong

Article Type: Research Article

Abstract: A polygonal fuzzy numbers can describe fuzzy information by means of finite ordered real numbers. It not only overcomes the complexity of traditional fuzzy number operations, but also keeps some good properties of trapezoidal fuzzy numbers, and it can approximate general fuzzy numbers with arbitrary precision. In this paper, a weighted arithmetic average operator is defined by the ordered representation and its operations of the polygonal fuzzy numbers, and a new Euclidean distance for measuring the polygonal fuzzy numbers is given. Secondly, in view of cost attribute and benefit attributes, the polygonal fuzzy decision matrix is normalized, and the weighted …Euclidean distance is used to solve the positive (negative) ideal solution and the relative closeness of the decision matrix, and then a new decision method is given. Finally, the effectiveness of the proposed decision-making method is illustrated by an example of the evaluation of logistics companies by shopping websites. Show more

Keywords: Polygonal fuzzy number, ordered representation, Euclidean distance, positive (negative) ideal solution, multiple attribute decision making

DOI: 10.3233/JIFS-191588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3151-3166, 2020

Authors: Sinha, Bam Bahadur | Dhanalakshmi, R.

Article Type: Research Article

Abstract: In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is …improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy. Show more

Keywords: Collaborative filtering, similarity, crow search algorithm, optimization, movielens, jester

DOI: 10.3233/JIFS-191594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3167-3182, 2020

Authors: Li, Jieya | Yang, Liming

Article Type: Research Article

Abstract: The classical principal component analysis (PCA) is not sparse enough since it is based on the L 2 -norm that is also prone to be adversely affected by the presence of outliers and noises. In order to address the problem, a sparse robust PCA framework is proposed based on the min of zero-norm regularization and the max of L p -norm (0 < p  ≤ 2) PCA. Furthermore, we developed a continuous optimization method, DC (difference of convex functions) programming algorithm (DCA), to solve the proposed problem. The resulting algorithm (called DC-LpZSPCA) is convergent linearly. In addition, when choosing different p …values, the model can keep robust and is applicable to different data types. Numerical simulations are simulated in artificial data sets and Yale face data sets. Experiment results show that the proposed method can maintain good sparsity and anti-outlier ability. Show more

Keywords: Principal component analysis, sparseness, robustness, zero-norm, DC programming, face reconstruction

DOI: 10.3233/JIFS-191617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3183-3193, 2020

Authors: Mehrani, Mohammad | Attarzadeh, Iman | Hosseinzadeh, Mehdi

Article Type: Research Article

Abstract: Wireless Body Area Networks (WBANs) have been introduced as a useful way in controlling health status of the monitored patients, during recent years. Each WBAN includes a number of biosensors attached to the patient’s body, collecting his vital sign features and communicating them to the coordinator to make appropriate decisions. Managing energy consumption of biosensors and continuous monitoring of the patients are two main issues in WBANs. Hence, denoting efficient sampling frequency of biosensors is very important in WBANs. In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in WBANs. In …this regard, from the first round until a certain round, the sampling rate of biosensors would be determined. Accordingly, we introduce our modified Fisher test, develop spline interpolation method and introduce three main parameters. These parameters are information of patient’s activity, patient’s risk and pivot biosensor’s value. Then, by employing mentioned parameters in addition to the introduced statistical and mathematical based strategies, the sampling rate of active biosensors in the next round would be determined at the end of each entire round. By reaching a pre-denoted round, the sampling rate of biosensors would be predicted through forecasting methods. For this purpose, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM). For estimation our approaches we simulate them in MATLAB R2018b software. Simulation results demonstrate that our methods can decrease the number of communicated data by 81%, reduce energy expenditure of biosensors by 73% and forecast the sampling rate of biosensors in the future rounds with 97% accuracy and 2.2753 RMSE. Show more

Keywords: WBANs, sampling rate determining, sampling rate forecasting, energy efficiency, overhead data, modified fisher test, spline, ANFIS, LSTM.

DOI: 10.3233/JIFS-191622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3195-3227, 2020

Authors: Bai, Nan | Chen, Guangzhu | Hou, Rui | Ying, Feng

Article Type: Research Article

Abstract: With the sustainable development of mobile communication technology and the increasing demand for indoor services, Location-based Service (LBS) is attracting more and more attention. Determining the mobile target’s location is a core problem of LBS. The traditional WiFi signal fingerprint-based positioning technology mainly determines the location information of the mobile target by received RSS, which has high real-time positioning but low positioning accuracy. The fingerprint-based positioning technology using image mainly determines the location information of the mobile target by matching the features of the foreground images, which has the high positioning accuracy but low real-time positioning. This paper presents an …indoor positioning method fusing information of the WiFi signal and RGB image to improve the positioning performance. The WiFi signal is transformed into the W-image according to indoor space and correction radius parameters, then the W-image and RGB image information are fused with LBP feature by the uniform-LBP algorithm. A fusion positioning model based on the sparse representation is established and solved using Lasso and BPDN positioning method. The positioning methods are tested in manufacturing workshop, and the experimental results show that the proposed method can reduce the complexity of the positioning method and achieve the higher positioning accuracy under same conditions. Show more

Keywords: Indoor positioning, WiFi signal, RGB image, manufacturing workshop, sparse representation

DOI: 10.3233/JIFS-191647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3229-3240, 2020

Authors: Xu, Xinggui | Yang, Ping | Ran, Bing | Xian, Hao | Liu, Yong

Article Type: Research Article

Abstract: The tough challenges of object recognition in long-distance scene involves contour shape deformation invariant features construction. In this work, an effective contour shape descriptor integrating critical points structure and Scale-invariant Heat Kernel Signature (SI-HKS) is proposed for long-distance object recognition. We firstly propose a general feature fusion model. Then, we capture the object contour structure feature with Critical-points Inner-distance Shape Context (CP-IDSC). Meanwhile, we pull-in the SI-HKS for capturing the local deformation-invariant properties of 2D shape. Based on the integration of the above two feature descriptors, the fusion descriptor is compacted by mapping into a low dimensional subspace using the …bags-of-features, allowing for an efficient Bayesian classifier recognition. The extensive experiments on synthetic turbulence-degraded shapes and real-life infrared image show that the proposed method outperformed other compared approaches in terms of the recognition precision and robustness. Show more

Keywords: Imaging through turbulent media, shape invariant descriptor, heat kernel signature, shape context, contour shape recognition

DOI: 10.3233/JIFS-191649

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3241-3257, 2020

Authors: Shahsavari-Pour, Nasser | Bahram-Pour, Najmeh | Kazemi, Mojde

Article Type: Research Article

Abstract: The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective …firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems. Show more

Keywords: Fuzzy optimization, location and routing, firefly algorithm, NSGAII algorithm, reliability, failure

DOI: 10.3233/JIFS-191654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3259-3273, 2020

Authors: Tianhe, Yin | Mahmoudi, Mohammad Reza | Qasem, Sultan Noman | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: A lot of research has been directed to the new optimizers that can find a suboptimal solution for any optimization problem named as heuristic black-box optimizers. They can find the suboptimal solutions of an optimization problem much faster than the mathematical programming methods (if they find them at all). Particle swarm optimization (PSO) is an example of this type. In this paper, a new modified PSO has been proposed. The proposed PSO incorporates conditional learning behavior among birds into the PSO algorithm. Indeed, the particles, little by little, learn how they should behave in some similar conditions. The proposed method …is named Conditionalized Particle Swarm Optimization (CoPSO). The problem space is first divided into a set of subspaces in CoPSO. In CoPSO, any particle inside a subspace will be inclined towards its best experienced location if the particles in its subspace have low diversity; otherwise, it will be inclined towards the global best location. The particles also learn to speed-up in the non-valuable subspaces and to speed-down in the valuable subspaces. The performance of CoPSO has been compared with the state-of-the-art methods on a set of standard benchmark functions. Show more

Keywords: Swarm intelligence, black-box optimizer, particle swarm optimization, adaptive conditionalized particle swarm optimization

DOI: 10.3233/JIFS-191685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3275-3295, 2020

Authors: Sinha, Keshav | Priya, Annu | Paul, Partha

Article Type: Research Article

Abstract: Cloud has become one of the most demanding services for data storage. On another hand, the security of data is one of the challenging tasks for Cloud Service Provider (CSP). Cryptography is one of the ways for securing the storage data. Cryptography is not a new approach instead of the efficient utilization of cryptographical algorithms is greatly needed. In this work, we proposed a Secure Hidden Layer (SHL) and Application Programming Interface (API) for data encryption. The SHL is consisting of two major modules (i) Key Management Server (KMS) and (ii) Share Holder Server (SHS) which is used for storing …and sharing of cryptographic key. For this purpose, we proposed a server-side encryption algorithm, which is based on the asymmetric algorithm (RSA and CRT) for providing end-to-end security of multimedia data. The experimental results of text and video are evidence that the size of file is not much affected after the encryption and effectively stored at Cloud Storage Server (CSS). The parameters like ciphertext size, encryption time and throughput are considered for performance evaluation of the proposed encryption technique. Show more

Keywords: Secure hidden layer (SHL), key management server (KMS), share holder server (SHS), cloud service provider (CSP), chinese remainder theorem (CRT), cloud storage server (CSS)

DOI: 10.3233/JIFS-191687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3297-3314, 2020

Authors: Wei, Guiwu | He, Yan | Lei, Fan | Wu, Jiang | Wei, Cun

Article Type: Research Article

Abstract: In recent years, ecological problems have become increasingly serious which are forcing people to give up the past high investment, high consumption and high emission development to promote green growth, implement the green new deal and pay attention to green supply chain research and practice. Therefore, in order to attach great importance to the economic and environmental benefits, enterprises should implement green supply chain and “green” change which has become the trend and urgent. Thus, in order to obtain an optimal green supplier, integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic uncertain linguistic sets (PULTSs) …has offered a novel integrated model, in which information entropy is utilized for calculating objective weights with PULTSs to acquire the final ranking result of green supplier. Besides, so as to indicate the applicability of devised method, it is confirmed by a numerical case for green supplier selection. Some comparative studies are made with some existing methods. The proposed method can also serve for selecting suitable alternative successfully in other selection problems. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic uncertain linguistic term sets (PULTSs), MABAC method, entropy method, combined weights, green supplier selection

DOI: 10.3233/JIFS-191688

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3315-3327, 2020