Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Wang, Weizhong | Ma, Yilin | Liu, Shuli

Article Type: Research Article

Abstract: Current risk prioritization approaches for FMEA models are insufficient to cope with risk analysis problem in which the self-confidence of expert’s judgment and the deviation among risk evaluation information are considered, simultaneously. Therefore, to remedy this limitation, this paper reports an extended risk prioritization approach by integrating the MULTIMOORA approach, Z-numbers and power weighted average (PWA) operator. Firstly, the Z-numbers with triangular fuzzy numbers are applied to reflect the self-confidence and uncertainty of expert’s judgment. Next, the PWA operator for Z-numbers (Z-PWA) with similarity measure is proposed to obtain the group risk evaluation matrix by considering the influence of the …deviation among risk evaluation information. Then, an extended version of MULTIMOORA method with developed entropy method is presented to calculate risk priority ranking order of each failure. Finally, the equipment failures in a certain oil and gas plant is utilized to test the extended risk prioritization approach for FMEA model. After that, the sensitivity and comparison studies are led to illustrate the availability and reliability of the proposed risk prioritization approach for FMEA based risk analysis problem. Show more

Keywords: Failure model and effect analysis, risk prioritization, Z-numbers, MULTIMOORA

DOI: 10.3233/JIFS-200678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2523-2537, 2021

Authors: Zhang, Qiao | Cheng, Xiaoliang | Liao, Shaoyi

Article Type: Research Article

Abstract: Hybrid energy storage system supplies a feasible solution to battery peak current reduction by introducing supercapacitor as auxiliary energy source. Energy management control strategy is a key technology for guaranteeing performance. In this paper, we describe a predictive energy management strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles. To utilize the supercapacitor reasonably, Markov chain model is proposed to predict the future load power during a driving cycle. The predictive results are subsequently used by power distribution strategy, which is designed using a low-pass filter and a fuzzy logic controller. The strategy model is developed …under MATLAB/Simulink software environment. To validate the performance of the proposed control strategy, a comparison test is implemented based on a 72 V rated voltage hybrid energy storage system experimental platform. The results indicate that the battery peak currents by proposed predictive control strategy are reduced by 26.32%, 28.21% and 27.12% under the UDDS, SC03 and NEDC three driving cycles respectively. Show more

Keywords: Electric vehicle, hybrid energy storage system, predictive energy management strategy, markov chain

DOI: 10.3233/JIFS-200934

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2539-2549, 2021

Authors: Wang, Zuli | Chiong, Raymond | Fan, Zongwen

Article Type: Research Article

Abstract: Malicious web domains represent a serious threat to online users’ privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzy-weighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve …the model’s robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared. Show more

Keywords: Malicious web domain identification, support vector machines, fuzzy weights, online attacks

DOI: 10.3233/JIFS-200943

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2551-2559, 2021

Authors: Liu, Peide | Hendalianpour, Ayad

Article Type: Research Article

Abstract: Financial flows are one of the three majors in a Supply Chain (SC). Ignoring financial flows, regardless of the quality of freight transport and information, could lead the organization to a state of bankruptcy, which is a situation directly resulting from a lack of control over financial inputs/outputs. This study proposes a multi-product mathematical model, which makes it possible to choose among suppliers, manufacturing sites, distribution centres, retailers, and transportation vehicles. The purpose of the model is to integrate physical and material dimensions to maximize net corporate profits through inbound and outbound financial flows; it involves payment mechanisms between the …financial and physical flows through maximizing the cash flows of manufacturing sites and suppliers, as two conflicting objectives that must consider the reciprocal effects of their decisions. These objectives are calculated by subtracting costs from the revenue; this process, of course, will ultimately result in an optimization of the organization’s financial flow. To solve the proposed mathematical model, the study relies on two algorithms, namely Particle Swarm Optimization (PSO) and Imperialist Competition Algorithm (ICA). The sample under investigation is solved separately using the three algorithms, and results are then compared. The observations of the study reveal the better performance of PSO. Show more

Keywords: Supply chain financial flow, inbound and outbound flow, mathematical modeling, particle swarm optimization, imperialist competitive algorithm

DOI: 10.3233/JIFS-201068

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2561-2579, 2021

Authors: Kişi, Ömer

Article Type: Research Article

Abstract: Statistical convergence of sequences has been studied in neutrosophic normed spaces (NNS) by Kirişci and Şimşek [39 ]. Ideal convergence is more general than statistical convergence for sequences. This has motivated us to study the ideal convergence in NNS. In this paper, we study the concept of ideal convergence and ideal Cauchy for sequences in NNS.

Keywords: Neutrosophic normed spaced, ideal convergence, ideal Cauchy sequence, t-norm, t-conorm

DOI: 10.3233/JIFS-201568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2581-2590, 2021

Authors: Yılmaz, Melike | Atan, Tankut

Article Type: Research Article

Abstract: As the Covid-19 pandemic also proved, access to health care plays a crucial role in our lives. Public officials, managers and investors should consider many criteria such as public infrastructure, environment, accessibility and demand for selecting the most appropriate site for a new hospital. Thus, the hospital site selection problem is a critical multi-criteria decision-making (MCDM) problem. This paper is a case study for İstanbul where a recent MCDM methodology, the fuzzy Evaluation based on Distance from Average Solution (EDAS) method, is applied to this problem for the first time. We used a comprehensive set of five main criteria and …17 sub-criteria found in the relevant literature regarding hospital site selection. These criteria were evaluated by three decision-makers to choose the hospital site from three districts. The recommendation of the fuzzy EDAS method was then compared to the outcome of a frequently used fuzzy MCDM method. The methods resulted in different site recommendations. Show more

Keywords: Hospital site selection, health care planning, multi-criteria decision making, fuzzy EDAS, fuzzy TOPSIS

DOI: 10.3233/JIFS-201757

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2591-2602, 2021

Authors: Liu, Xiang-Min | Hu, Jian | Mwakapesa, Deborah Simon | Nanehkaran, Y.A. | Mao, Yi-Min | Zhang, Rui-Peng | Chen, Zhi-Gang

Article Type: Research Article

Abstract: Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-rival challenges in a big data environment: excessive network parameters, slow convergence, and inefficient parallelism. To tackle these three problems, this paper develops a deep convolutional neural networks optimization algorithm (PDCNNO) in the MapReduce framework. The proposed method first pruned the network to obtain a compressed network in order to effectively reduce redundant …parameters. Next, a conjugate gradient method based on modified secant equation (CGMSE) is developed in the Map phase to further accelerate the convergence of the network. Finally, a load balancing strategy based on regulate load rate (LBRLA) is proposed in the Reduce phase to quickly achieve equal grouping of data and thus improving the parallel performance of the system. We compared the PDCNNO algorithm with other algorithms on three datasets, including SVHN, EMNIST Digits, and ISLVRC2012. The experimental results show that our algorithm not only reduces the space and time overhead of network training but also obtains a well-performing speed-up ratio in a big data environment. Show more

Keywords: DCNN, MapReduce, network compression, conjugate gradient method, load balancing

DOI: 10.3233/JIFS-201790

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2603-2615, 2021

Authors: Kurt, Mahmut Mahir | Yazıcıoğlu, Osman

Article Type: Research Article

Abstract: Retail e-commerce has been growing over the years by attracting entrepreneurs’ attention. Increasing interest in retail e-commerce has affected competition and made it necessary to choose the right competition strategy. This study aims at selecting the right competition strategy to be successful in retail e-commerce under vague and imprecise conditions. AHP and TOPSIS methods are used under intuitionistic fuzzy environment, which allows decision-makers to reflect their hesitation in their judgements. After a literature review on retail, e-commerce, and competition strategy, the main criteria and sub-criteria of multicriteria retail e-commerce alternative selection are presented. Cost leadership, differentiation, and focusing strategies are …evaluated as competitive strategies, based on the opinions of experts collected through a questionnaire. The results of the study show that the strategies are ranked as differentiation, focus, and cost leadership strategies, respectively. Show more

Keywords: Decision making, intuitionistic fuzzy, competitive strategy, retail, e-commerce

DOI: 10.3233/JIFS-202010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2617-2637, 2021

Authors: Fu, Wenqing | Khalil, Ahmed Mostafa

Article Type: Research Article

Abstract: In this paper, we will propose the novel notion of neighborhood rough sets on a universe set and study some of their basic properties. Then, the relationships between the neighborhood rough sets and covering rough sets are established. Further, the several related notions of probabilistic neighborhood rough sets are investigated and their basic theoretical are discussed. In addition, the notion of neighborhood rough sets over two different universes is defined, and interesting in their properties are explained. Depend on the neighborhood rough sets over two different universes, two algorithms are designed to solve the rough decision-making problems and clarify their …applicability by two illustrative examples, respectively. Finally, a comparison between Liu et al.’s approach and our approach is given. Show more

Keywords: Rough sets, covering rough sets, neighborhood operator, probabilistic neighborhood rough sets, gradedn-neighborhood rough sets, decision-making

DOI: 10.3233/JIFS-202081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2639-2664, 2021

Authors: Namarta, | Kaur, Parmpreet | Gupta, Umesh Chandra

Article Type: Research Article

Abstract: In the word of uncertain competitive situations everything is in the state of flux. Under such situations knowing the exact outcomes of mixed strategies adopted by a player is nearly impossible. It is highly rational to assume that no two experts will project the similar fuzzy payoffs for mix of strategies used. Aggregation of expert’s judgement becomes utmost important before solving such competitive situations. Considering this the present paper proposes a method to solve intuitionistic fuzzy game problems by using aggregation operators on payoff judgments of more than one expert. The proposed method significantly adds to the existing literature by …overcoming the limitation of Li’s existing method that considers only one expert’s opinion for solving intuitionistic fuzzy game problems. Illustrative example has been given for showing the superiority of the proposed method. Show more

Keywords: Aggregation operators, intuitionistic fuzzy sets, fuzzy matrix game

DOI: 10.3233/JIFS-202232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2665-2674, 2021

Authors: Zhang, Feng | Wu, Mingying | Hou, Xinting | Wang, Xinhe | Han, Cheng | Xu, Xiayu | Zhang, Leilei

Article Type: Research Article

Abstract: In order to improve the reliability and performance of landing gear retraction systems, this paper presents two importance analysis methods based on universal grey operation. According to the system principle and fault mechanisms, the fault tree of the retraction system was first established. The uncertainties of the bottom events were then described using the universal grey number to obtain the universal grey representation of the system failure probability. And compared with the traditional interval operation, the results show that universal grey operation can solve the problem of interval expansion with uncertainty. Importance analyses of the bottom events were then conducted …based on the probability importance and the key importance. By comparing the two important indices of the bottom events, the larger the value is, the higher the importance is. It was found that the occurrence of the bottom events “pipeline oil leakage,” “pump motor damage,” and “oil pollution” had the greatest impact on system failure probability, thus determining the key weak links affecting system failure and indicating the most effective targets for improvement. Show more

Keywords: Universal grey operation, interval operation, importance measure, fault tree, landing gear retraction system

DOI: 10.3233/JIFS-202248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2675-2685, 2021

Authors: Jingfei, Chang | Yang, Lu | Ping, Xue | Xing, Wei | Zhen, Wei

Article Type: Research Article

Abstract: Deep convolutional neural network (CNN) is difficult to deploy to mobile and portable devices due to its large number of parameters and floating-point operations (FLOPs). To tackle this problem, we propose a novel channel pruning method. We use the modified squeeze-and-excitation blocks (MSEB) to measure the importance of the channels in the convolutional layers. The unimportant channels, including convolutional kernels related to them, are pruned directly, which greatly reduces the storage cost and the number of calculations. For ResNet with basic blocks, we propose an approach to consistently prune all residual blocks in the same stage to ensure that the …compact network structure is dimensionally correct. After pruning we retrain the compact network from scratch to restore the accuracy. Finally, we verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012. The results indicate that the performance of the compact network is better than the original network when the pruning rate is small. Even when the pruning amplitude is large, the accuracy can also be maintained or decreased slightly. On the CIFAR-100, when reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy of VGG-19 even improve by 0.54% after retraining. The source code is available at https://github.com/JingfeiChang/UCP . Show more

Keywords: Deep learning, convolutional neural network, network pruning, image classification

DOI: 10.3233/JIFS-202290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2687-2699, 2021

Authors: Chakhrit, Ammar | Chennoufi, Mohammed

Article Type: Research Article

Abstract: Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA …by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively. Show more

Keywords: Fuzzy logic, FMECA, criticality assessment, multi-criteria, AHP, decision-making

DOI: 10.3233/JIFS-202362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2701-2716, 2021

Authors: Amiri Shahmirani, Mohammad Reza | Akbarpour Nikghalb Rashti, Abbas | Adib Ramezani, Mohammad Reza | Golafshani, Emadaldin Mohammadi

Article Type: Research Article

Abstract: Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective …parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data. Show more

Keywords: Earthquake, damage degree, fuzzy model, membership function, rules of fuzzy model

DOI: 10.3233/JIFS-202424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2717-2730, 2021

Authors: Jerisha Liby, J. | Jaya, T

Article Type: Research Article

Abstract: This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R 1 , G 1 , B 1 ), (R 2 , G 2 , B 2 ) are converted to HSV (Hue, saturation, values) Coefficients (H 1 , S 1 , V 1 ) and (H 2 , S 2 , V 2 ). The ‘Value’ Coefficients V 1 and V …2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC). Show more

Keywords: Video watermarking, data hiding, RGB model, HSV model, embedding rate, PSNR, SSIM, normalized cross-correlation, attacks

DOI: 10.3233/JIFS-202468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2731-2742, 2021

Authors: Kaliraman, Bhawna | Duhan, Manoj

Article Type: Research Article

Abstract: Electroencephalogram (EEG) signals are essential in brain-computer interface systems. Nowadays, these signals are employed in various medical applications. In the past few years, EEG signals gain more attention in security systems to identify users, as these signals are unique for each individual. The current study explores deep learning frameworks for EEG-based user identification. Data from 107 users were considered for the study, which is acquired using 64 channels. Several experimental tests are performed over both convolutional neural network (CNN) and recurrent neural networks (RNN) using a 10-fold cross-validation process to check system effectiveness. In CNN, 1-D Convolutional layer is employed …for the processing of EEG signals. In RNN, LSTM and GRU are used to check system accuracy. For performance measure various metrices were considered such as accuracy, precision, recall and kappa score. Acquired results suggest that gated recurrent unit (GRU) outperforms other models in terms of accuracy and complexity both. GRU model has 91.2% accuracy and has three layers only, which reduces the model’s complexity. The training cost is also decreasing due to the low complexity of the model. Show more

Keywords: Convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), feed forward neural networks (FFNN), rectified linear units (ReLU), gated recurrent unit (GRU)

DOI: 10.3233/JIFS-202490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2743-2753, 2021

Authors: Zhang, Jing | Sheng, Yuhong | Wang, Xiaoli

Article Type: Research Article

Abstract: Parameter estimation of high-order uncertain differential equations is an inevitable problem in practice. In this paper, the equivalent equations of high-order uncertain differential equations are obtained by transformation, and the parameters of the first-order uncertain differential equation including Liu process are estimated. Based on the least squares estimation method, this paper proposes a means to minimize the residual sum of squares to obtain an estimate of the parameters in the drift term, and make the noise sum of squares equal to the residual sum of squares to obtain an estimate of the parameters in the diffusion term. In addition, some …numerical examples are given to illustrate the proposed method. Finally, applications of the high-order uncertain spring vibration equations verify the viability of our method. Show more

Keywords: Uncertainty theory, high-order uncertain differential equation, parameter estimation, least squares estimation

DOI: 10.3233/JIFS-202522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2755-2764, 2021

Authors: Yi, Kui | Mao, Xiulan | Cheng, Honglei | Zhang, Ligang | Zhang, Dian

Article Type: Research Article

Abstract: Evaluating the capability of smart information service and exploring the smart information service elements in scenic areas can accelerate the smart tourism industry progress to a highly effective administrative and high quality of smart tourism service. This study aims to exploring the connections of a variety of smart information service elements in scenic areas, analyzing and evaluating the capability of smart information service, mapping a positive resolution to improve the capability of information service in scenic spots. Based on a synthetic method that combining Structural Equation Modeling (SEM) and Analytic Network Process (ANP), explicitly using the SEM to extracting the …key factors and mapping each factors’ co-relations, and in further step, using the ANP to carry a fuzzy evaluation of weighing the information service capability and each element in the case of Jiangxi Province in China, the result shows that the method of “SEM-ANP” is better fit than single ”SEM” or ”ANP”, the evaluating system for smart information service in scenic areas is significantly innovative and scientific to supply effective suggestions for policy makers. Show more

Keywords: Smart scenic areas, information service construction, “SEM-ANP” synthetic evaluation

DOI: 10.3233/JIFS-202536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2765-2777, 2021

Authors: Gao, Ting | Ma, Zhengming | Gao, Wenxu | Liu, Shuyu

Article Type: Research Article

Abstract: There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be …determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals. Show more

Keywords: Tensor decompostion, local linear embedding, mode product, dimensionality reduction

DOI: 10.3233/JIFS-202588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2779-2796, 2021

Authors: Yang, Meng | Ni, Yaodong | Yang, Xiangfeng | Ralescu, Dan A.

Article Type: Research Article

Abstract: In the increasingly competitive logistics industry, much more emphasis has been put on customer satisfaction by firms to distinguish themselves from their competitors. Motivated by the practices, the consistent vehicle routing problem (ConVRP) incorporates service consistency into the vehicle routing problem to improve customer satisfaction. However, the majority of the existing research considers the ConVRP in a deterministic environment while the uncertainties are not fully studied. Therefore, this paper solves the consistent vehicle routing problem under uncertain environment (UnConVRP) taking into account uncertain customer demands, travel times, and service times. Over a multi-day planning horizon, customers may have multi-day or …single-day service requirements. The same driver is assigned to each customer almost at the same time each day when customers require service. The objective is to design the routes for vehicles over the planning horizon under uncertain environment while maintaining service consistency. Two uncertain programming models are established based on different decision criteria and the crisp equivalents are proposed using uncertainty theory. An efficient template-based solution framework is designed to solve the models where the artificial bee colony algorithm is embedded. Initially, the template route is constructed to guarantee the service consistency of frequent customers. Then the final daily routes can be derived from the template route. Finally, numerical experiments are performed to show the effectiveness of the proposed algorithm. Show more

Keywords: Vehicle routing, consistent service, uncertain programming, artificial bee colony

DOI: 10.3233/JIFS-202593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2797-2812, 2021

Authors: Farwa, Shabieh | Kamran, Muhammad | Sarwar, Sundas | Kazmi, Maedah | Ahmad, Hijaz | Gepreel, Khaled A.

Article Type: Research Article

Abstract:  In this work, bipolar fuzzy parameterized sets in conjunction with soft sets are studied. This research focuses on the application of bipolar fuzzy parameterized soft sets (BFPSS ), arising from association of bipolar fuzzy parameterized sets with soft sets. Some useful operations and fundamental properties of BFPSS are presented. Mainly, we aim to design a novel and comparatively labour-saving algorithm to see the efficacy of BFPSS . We discuss an application of our algorithm in pharmaceutical decision making problem based on effectiveness and harmfulness of certain drugs. However, the algorithm is equally applicable in other decision making environments as …well where BFPSS arise. By preserving the structural implication of BFPSS , we compare our technique with a most recent algorithm to prove the significance of our method. Show more

Keywords: Fuzzy sets, bipolar fuzzy sets, soft sets, bipolar fuzzy parameterized soft sets, decision making

DOI: 10.3233/JIFS-202685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2813-2821, 2021

Authors: Zhao, Yifan | Tian, Shuicheng

Article Type: Research Article

Abstract: In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on Naive Bayes algorithm was proposed. The posterior probability of Bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise Naive Bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors …is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s. Show more

Keywords: Naive bayes algorithm, coal mine, hidden disaster factors, identification

DOI: 10.3233/JIFS-202726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2823-2831, 2021

Authors: Fan, Jianping | Wang, Shuting | Wu, Meiqin

Article Type: Research Article

Abstract: Failure modes and effects analysis (FMEA) is a useful reliability analysis technique to identify potential failure modes in a wide range of industries. However, the conventional FMEA method is deficient in dealing with the risk evaluation and prioritization method. To overcome the shortcomings, this paper presents a new risk priority model using Best-Worst Method based on D numbers (D-BWM) and the Measurement of Alternatives and Ranking according to COmpromise Solution based on D numbers (D-MARCOS). First, D numbers are used to deal with the uncertainty of FMEA team members’ subjective judgment. Second, the distance-based method is proposed to determine the …objective weight of each team member. Then, the D-BWM was used to determine the weight of risk factors. The combination rule of D number theory combined the evaluation information of multiple members into group opinions. Finally, D-MARCOS method is proposed to obtain the risk priority of the failure modes. An example and the results of comparative analysis show the method is effective. Show more

Keywords: Failure modes and effects analysis, D numbers, BWM, MARCOS

DOI: 10.3233/JIFS-202765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2833-2846, 2021

Authors: Luo, Damei | Li, Zhaowen | Qu, Liangdong

Article Type: Research Article

Abstract: An information system (IS) is an important mathematical tool for artificial intelligence. A fuzzy probabilistic information system (FPIS), the combination of some fuzzy relations in the same universe which satisfies the probability distribution, can be seen as an IS with fuzzy relations. A FPIS overcomes the shortcoming that rough set theory assumes elements in the universe with equal probability and leads to lose some useful information. This paper integrates the probability distribution into the fuzzy relations in a FPIS and studies its reduction. Firstly, the concept of a FPIS is introduced and its reduction is proposed. Then, the fuzzy relations …in a FPIS are divided into three categories (P -necessary, P -relatively necessary and P -unnecessary fuzzy relations) according to their importance. Next, entropy measurement for a FPIS is investigated. Moreover, some reduction algorithms are constructed. Finally, an example is given to verify the effectiveness of these proposed algorithms. Show more

Keywords: Fuzzy relation, FPIS, reduction, core, uncertainty, entropy, algorithm

DOI: 10.3233/JIFS-202783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2847-2863, 2021

Authors: Zhang, Nan | Sheng, Yuhong | Zhang, Jing | Wang, Xiaoli

Article Type: Research Article

Abstract: In uncertainty theory, parameter estimation of uncertain differential equation is a very important research direction. The parameter estimation of multifactor uncertain differential equation needs to be solved. Multifactor uncertain differential equation is a differential equation driven by multiple Liu processes. The paper introduces two methods to solve the unknown parameters of the multifactor uncertain differential equation, they are the method of moment estimation and the method of least squares estimation. Several numerical examples are used to illustrate the proposed parameter estimation methods.

Keywords: Uncertainty theory, multifactor uncertain differential equation, parameter estimation

DOI: 10.3233/JIFS-202891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2865-2878, 2021

Authors: Moradi Zirkohi, Majid | Lin, Tsung-Chih

Article Type: Research Article

Abstract: Interval type-2 fuzzy logic systems (IT2FLSs) have better abilities to cope with uncertainties in many applications. One major drawback of IT2FLSs is the high computational cost of the iterative Karnik-Mendel (KM) algorithms in type-reduction (TR). From the practical point of view, this prevents using IT2FLS in real-world applications. To address this issue, a novel non-iterative method called Moradi-Zirkohi-Lin (MZL) TR method is proposed for computing the centroid of an IT2FLS. This makes the practical implementation of the IT2FLSs simpler. Comparative simulation results show that the proposed method outperforms the KM TR method in terms of computational burden. Besides, closer results, …in terms of accuracy, to the KM TR method among the existing non-iterative TR methods are also achieved by the proposed TR method. Show more

Keywords: Karnik-Mendel (KM) algorithms, type-reduction, non-iterative, the centroid of an interval type-2 Fuzzy Set

DOI: 10.3233/JIFS-202913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2879-2889, 2021

Authors: Venkataramanan, C. | Ramalingam, S. | Manikandan, A.

Article Type: Research Article

Abstract: Smart farming is one of the immense applications of Wireless Sensor Networks (WSN). Still, most of the researches have been focusing on precision agriculture using WSNs. In general, the nodes within the wireless sensor systems are self-configured. Based on the application requirement, gadgets within the region of interest collect data, prepare it, and send it to the recipient. The biggest impediments to these sensor systems are collision, restricted battery, and transmission capacity. Due to these characteristics, the node battery depletes earlier, when it starts working. Currently, agriculture depends on rain due to the lack of water resources and irrigation services. …The crop development depends totally on the factors of water, the climatic conditions of the soil, etc. In large-scale agriculture, it is exceptionally problematic to analyze all the parameters accurately throughout the growing field. In this article, high-precision architecture for large-scale agriculture has been proposed. An IoT (Internet of Things) enabled WSN has been built and installed in the respective areas to measure the physical quantities regularly. In addition, Lévy-Walk Bat (LWBA) algorithm has been proposed to optimize the collected data. The prediction accuracy of the collected data is evaluated by LWBA and then, it is compared with the existing optimization algorithms with different error solvers. It has provided the exact information regarding the whole landscape and it will help the farmers to irrigate precisely. Show more

Keywords: Data prediction, error minimization, IoT, regression, machine learning, optimization, smart agriculture, SVM, WSNs

DOI: 10.3233/JIFS-202953

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2891-2904, 2021

Authors: Cen, Shixin | Yu, Yang | Yan, Gang | Yu, Ming | Kong, Yanlei

Article Type: Research Article

Abstract: As a spontaneous facial expression, micro-expression reveals the psychological responses of human beings. However, micro-expression recognition (MER) is highly susceptible to noise interference due to the short existing time and low-intensity of facial actions. Research on facial action coding systems explores the correlation between emotional states and facial actions, which provides more discriminative features. Therefore, based on the exploration of correlation information, the goal of our work is to propose a spatiotemporal network that is robust to low-intensity muscle movements for the MER task. Firstly, a multi-scale weighted module is proposed to encode the spatial global context, which is obtained …by merging features of different resolutions preserved from the backbone network. Secondly, we propose a multi-task-based facial action learning module using the constraints of the correlation between muscle movement and micro-expressions to encode local action features. Besides, a clustering constraint term is introduced to restrict the feature distribution of similar actions to improve categories’ separability in feature space. Finally, the global context and local action features are stacked as high-quality spatial descriptions to predict micro-expressions by passing through the Convolutional Long Short-Term Memory (ConvLSTM) network. The proposed method is proved to outperform other mainstream methods through comparative experiments on the SMIC, CASME-I, and CASME-II datasets. Show more

Keywords: Micro-expression recognition, multi-scale weighted module, facial action learning module, spatiotemporal network

DOI: 10.3233/JIFS-202962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2905-2921, 2021

Authors: Liu, Liming | Li, Ping | Chu, Maoxiang | Gao, Chuang

Article Type: Research Article

Abstract: Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K -nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall …into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10°C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields. Show more

Keywords: Basic oxygen furnace, end-point information prediction, nonparallel support vector regression, weight information, whale optimization algorithm

DOI: 10.3233/JIFS-210007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2923-2937, 2021

Authors: Jyothi, R.L. | Rahiman, M. Abdul

Article Type: Research Article

Abstract: Binarization is the most important stage in historical document image processing. Efficient working of character and word recognition algorithms depend on effective segmentation methods. Segmentation algorithms in turn depend on images free of noises and degradations. Most of these historical documents are illegible with degradations like bleeding through degradation, faded ink or faint characters, uneven illumination, contrast variation, etc. For effective processing of these document images, efficient binarization algorithms should be devised. Here a simple modified version of the Convolutional Neural Network (CNN) is proposed for historical document binarization. AOD-Net architecture for generating dehazed images from hazed images is modified …to create the proposed network.The new CNN model is created by incorporating Difference of Concatenation layer (DOC), Enhancement layer (EN) and Thresholding layer into AOD-Net to make it suitable for binarization of highly degraded document images. The DOC layer and EN layer work effectively in solving degradation that exists in the form of low pass noises. The complexity of working of the proposed model is reduced by decreasing the number of layers and by introducing filters in convolution layers that work with low inter-pixel dependency. This modified version of CNN works effectively with a variety of highly degraded documents when tested with the benchmark historical datasets. The main highlight of the proposed network is that it works efficiently in a generalized manner for any type of document images without further parameter tuning. Another important highlight of this method is that it can handle most of the degradation categories present in document images. In this work, the performance of the proposed model is compared with Otsu, Sauvola, and three recent Deep Learning-based models. Show more

Keywords: Binarization, historical document images, degradation, difference of concatenated convolutions, enhancement layer

DOI: 10.3233/JIFS-210015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2939-2952, 2021

Authors: Zhang, Lingyun | Zhang, Pingjian

Article Type: Research Article

Abstract: Computational aesthetics, which uses computers to learn human aesthetic habits and ultimately replace humans in scoring images, has become a hot topic in recent years due to its wide application. Most of the initial research is to manually extract features and use classifiers such as support vector machines to score images. With the development of deep learning, traditional manual feature extraction methods are gradually replaced by convolutional neural networks to extract more comprehensive features. However, it is a huge challenge to artificially design an aesthetic neural network. Recently, Neural Architecture Search has upsurged to find suitable neural networks for many …tasks in deep learning. In this paper, we first attempt to combine Neural Architecture Search with computational aesthetics. We design and apply a customized progressive differentiable architecture search strategy to obtain a light-weighted and efficient aesthetic baseline model. In addition, we simulate the multi-person rating mechanism by outputting the distribution of the aesthetic value of the image, replacing the previous classification scheme of judging the beauty and unbeauty of the image by the threshold value, and propose a self-weighted Earth Mover’s Distance loss to better fit human subjective scoring. Based on the baseline model, we further introduce several strategies including an attention mechanism, the dilated convolution, and adaptive pooling, to enhance the performance. Finally, we design several groups of comparative experiments to demonstrate the effectiveness of our baseline aesthetic model and the introduced improvement strategies. Show more

Keywords: Artificial intelligence, deep learning, convolutional neural networks, computational aesthetics, neural architecture search

DOI: 10.3233/JIFS-210026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2953-2967, 2021

Authors: Mohan, Prakash | Sundaram, Manikandan | Satpathy, Sambit | Das, Sanchali

Article Type: Research Article

Abstract: Techniques of data compression involve de-duplication of data that plays an important role in eliminating duplicate copies of information and has been widely employed in cloud storage to scale back the storage capacity and save information measure. A secure AES encryption de-duplication system for finding duplication with the meaning and store up it in the cloud. To protect the privacy of sensitive information whereas supporting de-duplication, The AES encryption technique and SHA-256 hashing algorithm have been utilized to encrypt the information before outsourcing. Pre-processing is completed and documents are compared and verified with the use of wordnet. Cosine similarity is …employed to see the similarity between both the documents and to perform this, a far economical VSM data structure is used. Wordnet hierarchical corpus is used to see syntax and semantics so that the identification of duplicates is done. NLTK provides a large vary of libraries and programs for symbolic and statistical natural language process (NLP) for the Python programming language that is used here for the unidentified words by cosine similarity. Within the previous strategies, cloud storage was used abundantly since similar files were allowed to store. By implementing our system, space for storing is reduced up to 85%. Since AES and SHA-256 are employed, it provides high security and efficiency. Show more

Keywords: Vector space Model, Wordnet, deduplication, cosine similarity, NLTK

DOI: 10.3233/JIFS-210038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2969-2980, 2021

Authors: Zhang, Jin | Gu, Fu | Ji, Yangjian | Guo, Jianfeng

Article Type: Research Article

Abstract: To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view …history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress. Show more

Keywords: Scientific and technological literature resources, personalized recommendation, deep learning, recommendation algorithm

DOI: 10.3233/JIFS-210043

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2981-2996, 2021

Authors: Gao, Yin | Jia, Lifen

Article Type: Research Article

Abstract: Uncertain delay differential equations (UDDEs) charactered by Liu process can be employed to model an uncertain control system with a delay time. The stability of its solution always be a significant matter. At present, the stability in measure for UDDEs has been proposed and investigated based on the strong Lipschitz condition. In reality, the strong Lipschitz condition is so strictly and hardly applied to judge the stability in measure for UDDEs. For the sake of solving the above issue, the stability in measure based on new Lipschitz condition as a larger scale of applications is verified in this paper. In …other words, if it satisfies the strong Lipschitz condition, it must satisfy the new Lipschitz conditions. Conversely, it may not be established. An example is provided to show that it is stable in measure based on the new Lipschitz conditions, but it becomes invalid based on the strong Lipschitz condition. Moreover, a special class of UDDEs is verified to be stable in measure without any limited condition. Besides, some examples are investigated in this paper. Show more

Keywords: Stability in measure, Liu process, uncertain process, uncertain delay differential equations

DOI: 10.3233/JIFS-210089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2997-3009, 2021

Authors: Senniappan, Gomathi | Umapathy, Prabha

Article Type: Research Article

Abstract:  Renewable energy presently occupies a prominent position in India’s overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) …configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. Show more

Keywords: Photovoltaic (PV) systems, MPPT, perturb and observation, artificial bee colony, human psychology optimization (HPO), hybrid HPO-PO

DOI: 10.3233/JIFS-210114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3011-3030, 2021

Authors: Xiao, Yanjun | Zhang, Zhenpeng | Liu, Zhenhao | Zhang, Zonghua | Zhou, Wei | Liu, Weiling

Article Type: Research Article

Abstract: In textile machines, the stability of warp tension is one of the decisive factors for the reliability, stability and product quality of weaving process. In order to meet the improving requirement for weaving efficiency and fabric quality, it is proposed that a fuzzy optimization integral separation PID warp tension control scheme based on process sampling to improve the warp tension control level of loom. Aiming at the problems of time-varying, nonlinear and variable coupling in the warp tension control system of loom, the forming mechanism of warp tension is modeled and analyzed, and the sampling scheme of warp tension based …on process is proposed. Based on the periodic change of warp tension at macro level and continuous fluctuation at micro level, the integral separation control and fuzzy optimization theory are introduced to optimize the control effect of the control system on the basis of classical PID control algorithm. Finally, the simulation and experiment show that the scheme can improve the tension controls performance and effectively reduce the tension error fluctuation. Show more

Keywords: Rapier loom, tension control, closed loop control, fuzzy optimization, integral separation control

DOI: 10.3233/JIFS-210124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3031-3044, 2021

Authors: El-Bably, M. K. | Abo-Tabl, E. A.

Article Type: Research Article

Abstract: The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak’s rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of “nano-topology”, which have introduced by Thivagar and Richard [49 ], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical …application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods. Show more

Keywords: Neighborhoods, topology, rough sets, generalized nano-topology, attributes reduction and lung cancer disease

DOI: 10.3233/JIFS-210167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3045-3060, 2021

Authors: Wu, Hsien-Chung

Article Type: Research Article

Abstract: The main purpose of this paper is to establish a mechanical procedure to determine the membership functions using the data collected from the economic and engineering problems. Determining the membership functions from the collected data may depend on the subjective viewpoint of decision makers. The mechanical procedure proposed in this paper can get rid of the subjective bias of decision makers. The concept of solid families is also proposed by regarding the sets in a family to be continuously varied. The desired fuzzy sets will be generated in the sense that its α -level sets will be identical to the …sets of the original family. In order to achieve this purpose, any arbitrary families will be rearranged as the nested families by applying some suitable functions to the original families that are formulated from the collected data. Show more

Keywords: Nested families, non-normal fuzzy sets, solid families

DOI: 10.3233/JIFS-210201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3061-3082, 2021

Authors: Ling, Jie | Xiong, Su | Luo, Yu

Article Type: Research Article

Abstract: Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the …rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods. Show more

Keywords: Malicious URL detection, network security, deep learning, semi-supervised learning

DOI: 10.3233/JIFS-210212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3083-3092, 2021

Authors: Angelini, Pierpaolo | Maturo, Fabrizio

Article Type: Research Article

Abstract: This paper focuses on logical aspects of choices being made by the consumer under conditions of uncertainty or certainty. Such logical aspects are found out to be the same. Choices being made by the consumer that should maximize her subjective utility are decisions studied by revealed preference theory. A finite number of possible alternatives is considered. They are mutually exclusive propositions identifying all quantitative states of nature of a consumption plan. Each proposition of it is expressed by a real number. This research work distinguishes it from its temporary truth value depending on the state of information and knowledge of …the consumer. Since each point of the consumption space of the consumer belongs to a two-dimensional convex set, this article focuses on conjoint distributions of mass. Indeed, the consumption space of the consumer is generated by all coherent summaries of a conjoint distribution of mass. Each point of her consumption space is connected with a weighted average of states of nature of two consumption plans jointly studied. They give rise to a conjoint distribution of mass. The consumer chooses a point of a two-dimensional convex set representing that bundle of goods actually demanded by her inside of her consumption space. This paper innovatively shows that it is nothing but a bilinear and disaggregate measure. It is decomposed into two real numbers, where each real number is a linear measure. In this paper, different measures are obtained. They can be disaggregate or aggregate measures, where the latter are independent of the notion of ordered pair of consumption plans. Show more

Keywords: 2-parallelepiped, α-product, temporary truth value, antisymmetric tensor, non-linear metric, linear metric

DOI: 10.3233/JIFS-210234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3093-3105, 2021

Authors: Wu, Meiqin | Hou, Xiaoqing | Fan, Jianping

Article Type: Research Article

Abstract: As an extended model of data envelopment analysis (DEA), cross-efficiency is utilized in various fields. It applies the relative evaluation values of homogeneous decision-making units (DMUs) in ranking completely. Due to the non-uniqueness of DEA optimal weights, and the cross-efficiency methods focus on the aggregation of cross-efficiency matrix values. There is a low correction between the final efficiency evaluation value and the value assigned to the criteria weight or the value of the criteria. This paper uses the modified aggressive cross-efficiency model to calculate the weights of the decision-making units (DMUs). Then according to the consistency of peer-evaluation criteria, the …entropy weight method is used to aggregate the index weights to generate a set of common weights, and a new Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to calculate the utility score of DMUs that can rank them completely. Finally, a numerical case of supplier selection is offered to illustrate the feasibility and effectiveness of the proposed method. Show more

Keywords: Cross-efficiency, group decision-making, criteria utility, MARCOS method

DOI: 10.3233/JIFS-210279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3107-3119, 2021

Authors: Liu, Donghai | Luo, Yan

Article Type: Research Article

Abstract: Although some correlation measure of intuitionistic fuzzy sets(IFSs) have been proposed, some of them cannot express the consistency of information or satisfy the axioms of similarity measure. In this paper, we present a consensus reaching process based on the concordance correlation measure of IFSs in multi-criteria decision making problems. Firstly, we define an innovative concordance correlation measure of IFSs, which not only takes the average information deviation of IFSs into account but also overcomes the disadvantages of previous correlation measures. In addition, its properties and the relationship between the defined new concordance correlation measure and Pearson correlation coefficient of IFSs …are discussed. Secondly, considering that the classical TOPSIS method cannot be applied to the correlation measure with negative values, we continue to introduce the concept of relative concordance correlation measure and propose a consensus reaching process with minimum adjustment for an innovative behavioral TOPSIS method. Furthermore, a detailed numerical example and the comparison analyses are provided to verify the advantages of the proposed method. At last, we discuss the sensitivity and stability of the method. Show more

Keywords: Concordance correlation measure, Consensus reaching process, Intuitionistic fuzy set, Behavioral TOPSIS

DOI: 10.3233/JIFS-210343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3121-3136, 2021

Authors: Wang, Jinfeng | Huang, Shuaihui | Jiang, Fajian | Zheng, Zhishen | Ou, Jianbin | Chen, Hao | Chen, Runjian | Wang, Wenzhong

Article Type: Research Article

Abstract: Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of …sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor. Show more

Keywords: Parallel computing, sparse storage, fuzzy integral, fuzzy measure

DOI: 10.3233/JIFS-210372

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3137-3159, 2021

Authors: Qiu, Liqing | Yang, Zhongqi | Zhu, Shiwei | Gu, Chunmei | Tian, Xiangbo

Article Type: Research Article

Abstract: Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node …not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy. Show more

Keywords: Social network, influence maximization, local influence, heuristic algorithm

DOI: 10.3233/JIFS-210379

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3161-3172, 2021

Authors: Yiarayong, Pairote

Article Type: Research Article

Abstract: The aim of this manuscript is to apply bipolar fuzzy sets for dealing with several kinds of theories in LA -semigroups. To begin with, we introduce the concept of 2-absorbing (quasi-2-absorbing) bipolar fuzzy ideals and strongly 2-absorbing (quasi-strongly 2-absorbing) bipolar fuzzy ideals in LA -semigroups, and investigate several related properties. In particular, we show that a bipolar fuzzy set A = ( μ A P , μ A N ) over an LA -semigroup S is weakly 2-absorbing …if and only if [ B ⊙ C ] ⊙ D ⪯ A implies B ⊙ C ⪯ A or C ⊙ D ⪯ A or B ⊙ D ⪯ A for any bipolar fuzzy sets B = ( μ B P , μ B N ) , C = ( μ C P , μ C N ) and D = ( μ D P , μ D N ) . Also, we give some characterizations of quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S by bipolar fuzzy points. In conclusion of this paper we prove that the relationship between quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S and quasi-2-absorbing bipolar fuzzy ideals over S . Show more

Keywords: bipolar fuzzy set, 2-absorbing bipolar fuzzy ideal, strongly 2-absorbing bipolar fuzzy ideal, ℒℒ-semigroup, quasi-2-absorbing bipolar fuzzy ideal

DOI: 10.3233/JIFS-210388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3173-3181, 2021

Authors: Zhao, Shan | Li, Zhao

Article Type: Research Article

Abstract: The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate …experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system. Show more

Keywords: Interval type-2 fuzzy system, interval type-2 fuzzy set, interpolation function, universal approximation

DOI: 10.3233/JIFS-210435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3183-3200, 2021

Authors: Zixian, Zhang | Xuning, Liu | Zhixiang, Li | Hongqiang, Hu

Article Type: Research Article

Abstract: The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. …Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model. Show more

Keywords: Coal and gas outburst, Feature selection, Boruta, Apriori, Bayesian optimization, SVM

DOI: 10.3233/JIFS-210466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3201-3218, 2021

Authors: Yang, Han | Qin, Keyun

Article Type: Research Article

Abstract: The theory of three-way concept analysis has been developed into an effective tool for data analysis and knowledge discovery. In this paper, we propose neutrosophic three-way concept lattice by combining neutrosophic set with three-way concept analysis and present an approach for conflict analysis by using neutrosophic three-way concept lattice. Firstly, we propose the notion of neutrosophic formal context, in which the relationships between objects and attributes are expressed by neutrosophic numbers. Three pairs of concept derivation operators are proposed. The basic properties of object-induced and attribute-induced neutrosophic three-way concept lattices are examined. Secondly, we divide the neutrosophic formal context into …three classical formal contexts and propose the notions of the candidate neutrosophic three-way concepts and the redundant neutrosophic three-way concepts. Two approaches of constructing object-induced (attribute-induced) neutrosophic three-way concept lattices are presented by using candidate, redundant and original neutrosophic three-way concepts respectively. Finally, we apply the neutrosophic formal concept analysis to the conflict analysis and put forward the corresponding optimal strategy and the calculation method of the alliance. Show more

Keywords: Three-way concept analysis, neutrosophic set, neutrosophic three-way concept lattice, conflict analysis

DOI: 10.3233/JIFS-210481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3219-3236, 2021

Authors: He, Yan | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: The optimal supplier selection in medical instrument industries could be considered a classical MAGDM issue. The probabilistic uncertain linguistic term sets (PULTSs) could depict uncertain information well and the Taxonomy method is appropriate to compare various alternatives according to their merits and utility degree from studied attributes. In such paper, we develop a Taxonomy method for probabilistic uncertain linguistic MAGDM (PUL-MAGDM) with the completely unknown attribute weights. Above all, the score function’s definition is utilized to derive the weights of attribute based upon the CRITIC method. In addition, the probabilistic uncertain linguistic development pattern (PULDP) is improved and the smallest …development attribute value from the positive ideal solution under PULTSs is calculated to determine the optimal alternative. In the end, taking the supplier selection in medical instrument industries as an example, we demonstrate the usage of the developed algorithms. Based on this, the comparison of methods is conducted with existing methods, such as PUL-TOPSIS method, the PULWA operator, the PUL-EDAS method and the ULWA operator. The results verify that the decision-making framework is valid and effective for supplier selection. Thus, the advantage of this designed method is that it is simple to understand and easy to compute. The designed method can also contribute to the selection of suitable alternative successfully in other selection issues. Show more

Keywords: Multiple attribute group decision making (MAGDM) issues, probabilistic uncertain linguistic sets (PULTSs), taxonomy approach, CRITIC method, supplier selection

DOI: 10.3233/JIFS-210494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3237-3250, 2021

Authors: Amsaprabhaa, M. | Nancy Jane, Y. | Khanna Nehemiah, H.

Article Type: Research Article

Abstract: Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people’s socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic …based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%. Show more

Keywords: Topic modeling, latent dirichlet allocation, grid search, multi-class emotion classification, recurrent neural network

DOI: 10.3233/JIFS-210544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3251-3264, 2021