Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Atmaca, S. | Zorlutuna, I.

Article Type: Research Article

Abstract: In 2020, r-near topological spaces on Near Approximation Spaces were introduced by Atmaca [1 ]. In this study, we introduce the concept of continuity on r-near topological spaces and examine some properties of it.

Keywords: Near set, r-near topology, topology

DOI: 10.3233/JIFS-211017

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-5, 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. Pre-press, no. Pre-press, pp. 1-13, 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. Pre-press, no. Pre-press, pp. 1-14, 2021

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. Pre-press, no. Pre-press, pp. 1-15, 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. Pre-press, no. Pre-press, pp. 1-20, 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. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Riaz, Muhammad | Habib, Anam | Aslam, Muhammad

Article Type: Research Article

Abstract:  A cubic bipolar fuzzy set (CBFS) is a new approach in computational intelligence and decision-making under uncertainty. This model is the generalization of bipolar fuzzy sets to deal with two-sided contrasting features which can describe the information with a bipolar fuzzy number and an interval-valued bipolar fuzzy number simultaneously. In this paper, the Dombi’s operations are analyzed for information aggregation of cubic bipolar fuzzy numbers (CBFNs). The Dombi’s operations carry the advantage of more pliability and reliability due to the existence of their operational parameters. Owing to the pliable nature of Dombi’s operators, this research work introduces new aggregation operators …named as cubic bipolar fuzzy Dombi weighted averaging (CBFDWA) operator and cubic bipolar fuzzy Dombi ordered weighted averaging (CBFDOWA) operator with ℙ -order and ℝ -order, respectively. Additionally, this paper presents some significant characteristics of suggested operators including, idempotency, boundedness and monotonicity. Moreover, a robust multi-criteria decision making (MCDM) technique is developed by using ℙ -CBFDWA and ℝ -CBFDWA operators. Based on the suggested operators a practical application is demonstrated towards MCDM under uncertainty. The comparison analysis of suggested Dombi’s operators with existing operators is also given to discuss the rationality, efficiency and applicability of these operators. Show more

Keywords: Cubic bipolar fuzzy sets, Dombi’s operations, cubic bipolar fuzzy Dombi weighted averaging operator, cubic bipolar fuzzy Dombi ordered weighted averaging operator, ℙ-order and ℝ-order operations, multi-criteria decision making

DOI: 10.3233/JIFS-210667

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 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. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Xiao, Yanjun | Han, Furong | Ding, Yvheng | Liu, Weiling

Article Type: Research Article

Abstract: The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge …base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms. Show more

Keywords: Rapier loom, expert system, fault tree, fault diagnosis

DOI: 10.3233/JIFS-210741

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Aydın, Serhat | Yörükoğlu, Mehmet | Kabak, Mehmet

Article Type: Research Article

Abstract: The fourth party logistics (4PL) is an combiner that designs and implements the holistic supply chain solutions by using skills, knowledge, technology and resources of the service provider and its customer. A 4PL provider is also a technological service provider with eligible intellectual capital and the sufficient computer/software infrastructure. Defining the most appropriate 4PL service provider from the alternatives is not easy for companies, the solution can be addressed within the framework of the Multi-Criteria Decision Making (MCDM) problem, and subjective and uncertain data are required for this solution. “Fuzzy set theory” is a helpful tool for dealing with such …subjectivity and uncertainty. In recent times, extensions of fuzzy sets have been evolved to address and describe the subjectivities and uncertainties more widely. Neutrosophic sets are one of the extensions of fuzzy sets, and unlike other extensions, they use the independent indeterminacy-membership function, thereby extracting important information and improving the accuracy of the decision-making process. A neutronophic MCDM method was proposed for the assessment of 4PL providers’ performance. In the application part of the study, neutrosophic language scale was used by three experts to evaluate the performance of 4PL providers. Then the closeness coefficient of each alternative was computed and sequenced in descending order. We also presented a comparative analysis with neutrosphic TOPSIS method. The results determined that the proposed neutrosophic MCDM method could be used in the performance evaluation of 4PL providers and similar problems. Show more

Keywords: Supply chain management, 4PLs, multi criteria decision making, neutrosophic sets

DOI: 10.3233/JIFS-219209

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 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. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Karimi, Saeed | Mirzamohammadi, Saeed | Pishvaee, MirSaman

Article Type: Research Article

Abstract: As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy …parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers. Show more

Keywords: Project portfolio selection, project interdependencies, possibilistic robustness, fuzzy uncertainty

DOI: 10.3233/JIFS-210144

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 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. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Chen, Hongyu | Wang, Shengsheng

Article Type: Research Article

Abstract: Since the end of 2019, the COVID-19, which has swept across the world, has caused serious impacts on public health and economy. Although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for clinical diagnosis, it is very time-consuming and labor-intensive. At the same time, more and more people have doubted the sensitivity of RT-PCR. Therefore, Computed Tomography (CT) images are used as a substitute for RT-PCR. Powered by the research of the field of artificial intelligence, deep learning, which is a branch of machine learning, has made a great success on medical image segmentation. However, general full supervision methods …require pixel-level point-by-point annotations, which is very costly. In this paper, we put forward an image segmentation method based on weakly supervised learning for CT images of COVID-19, which can effectively segment the lung infection area and doesn’t require pixel-level labels. Our method is contrasted with another four weakly supervised learning methods in recent years, and the results have been significantly improved. Show more

Keywords: COVID-19, deep learning, weakly supervised learning, computed tomography, automated segmentation

DOI: 10.3233/JIFS-210569

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 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. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: TuncalıYaman, Tutku | Bilgiç, Emrah | Fevzi Esen, M.

Article Type: Research Article

Abstract: Injury severity in motor vehicle traffic accidents is determined by a number of factors including driver, vehicle, and environment. Airbag deployment, vehicle speed, manner of collusion, atmospheric and light conditions, degree of ejection of occupant’s body from the crash, the use of equipment or other forces to re-move occupants from the vehicle, model and type of vehicle have been considered as important risk factors affecting accident severity as well as driver-related conditions such as age, gender, seatbelt use, alcohol and drug involvement. In this study, we aim to identify important variables that contribute to injury severity in the traffic crashes. …A contemporary dataset is obtained from National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS). To identify accident severity groups, we performed different clustering algorithms including fuzzy clustering. We then assessed the important factors affecting injury severity by using classification and regression trees (CRT). The results which would guide car manufacturers, policy makers and insurance companies indicate that the most important factor in defining injury severity is deployment of air-bag, followed by extrication, ejection occurrences, and travel speed and alcohol involvement. Show more

Keywords: Traffic accidents, fuzzy clustering, data mining, injury severity, clustering, CRT

DOI: 10.3233/JIFS-219213

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Sergi, Duygu | Sari, Irem Ucal | Senapati, Tapan

Article Type: Research Article

Abstract: Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFSs) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF …benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques. Show more

Keywords: Interval-valued Fermatean fuzzy sets, fuzzy engineering economics, fuzzy net present value, fuzzy equivalent uniform annual value, fuzzy benefit cost ratio

DOI: 10.3233/JIFS-219196

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Roy, Ranadip | Mukherjee, V. | Pratap Singh, Rudra

Article Type: Research Article

Abstract: This paper aims to investigate the application of Harris hawks optimization (HHO) optimization for the solution of model order reduction (MOR) problem of power system application. The proposed approach is implemented to determine the reduced order equivalent model of large-scale power system model. The obligations encountered by the higher-scale model like stability, calculative effort and problem into local optima can be enhanced by this propounded methodology. These yielded reduced models have been tried in addition to the existing algorithms and the obtained results are contrasted considering various technical parameters to accomplish its effectiveness, reliability and robustness of the adopted strategy. …Thus, superior performance of the proposed method is demonstrated. The validation of the methodology in terms of error index, time and frequency domain outputs, convergence curves with scalability of the approach are outlined in the investigation of the system. Show more

Keywords: Butterfly optimization algorithm, Harris hawks optimization, power system, reduced-order model, transfer function

DOI: 10.3233/JIFS-211132

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Zhu, Kun | Zhang, Shuai | Zhang, Wenyu | Zhang, Zhiqiang

Article Type: Research Article

Abstract: Accurate taxi demand forecasting is significant to estimate the change of demand to further make informed decisions. Although deep learning methods have been widely applied for taxi demand forecasting, they neglect the complexity of taxi demand data and the impact of event occurrences, making it hard to effectively model the taxi demand in highly dynamic areas (e.g., areas with frequent event occurrences). Therefore, to achieve accurate and stable taxi demand forecasting in highly dynamic areas, a novel hybrid deep learning model is proposed in this study. First, to reduce the complexity of taxi demand time series, the seasonal-trend decomposition procedures …based on loess is employed to decompose the time series into three simpler components (i.e., seasonal, trend, and remainder components). Then, different forecasting methods are adopted to handle different components to obtain robust forecasting results. Moreover, considering the instability and nonlinearity of the remainder component, this study proposed to fuse the event features (in particular, text data) to capture the unusual fluctuation patterns of remainder component and solve its extreme value problem. Finally, genetic algorithm is applied to determine the optimal weights for integrating the forecasting results of three components to obtain the final taxi demand. The experimental results demonstrate the better accuracy and reliability of the proposed model compared with other baseline forecasting models. Show more

Keywords: Taxi demand forecasting, deep learning, seasonal-trend decomposition procedures based on loess, data fusion, text data, genetic algorithm

DOI: 10.3233/JIFS-210657

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Singla, 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. Pre-press, no. Pre-press, pp. 1-10, 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. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Goraghani, Simin Saidi | Ali Borzooei, Rajab | Ahn, Sun Shin

Article Type: Research Article

Abstract: In recent years, A. Di Nola et al. studied the notions of MV -semiring and semimodules and investigated related results [9, 10, 12, 26 ]. Now in this paper, by using an MV -semiring and an MV -algebra, we introduce the new definition of MV -semimodule, study basic properties and find some examples. Then we study A -ideals on MV -semimodules and Q -ideals on MV -semirings, and by using them, we study the quotient structures of MV -semimodule. Finally, we present the notions of prime A -ideal, torsion free MV -semimodule and annihilator on MV -semimodule and we study …the relations among them. Show more

Keywords: MV-semiring, MV-algebra, MV-module, MV-semimodule, Q-ideal, prime A-ideal, 06D35, 16Y60

DOI: 10.3233/JIFS-211130

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 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. Pre-press, no. Pre-press, pp. 1-14, 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. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Duan, Huiming | Huang, Jiangbo | Wang, Siqi | He, Chenglin

Article Type: Research Article

Abstract: The stock market is an important embodiment of a national economy and financial activities and has an important impact on a country, enterprises and individuals. Stock forecasting can allow investment institutions and investors to understand the trend of the stock market in advance, which is a challenging and meaningful study. First, through the impulse phenomenon of the stock market, this paper discusses the problem of stock price prediction with delay, and the impulse delay differential equation is established. Second, according to the difference between the differential and the difference, the nonlinear delay grey prediction model is established. Next, the model …parameters are estimated and the solving steps are obtained. The nonlinear parameters and delay time are optimized by the particle swarm optimization algorithm. Finally, the new model is applied to the prediction of the Shanghai stock market and the Shenzhen stock market closing indexes; the results show that the new model can effectively predict stock prices, which is much better than the existing four grey models and a time series model. Show more

Keywords: Stock price, impulse delay, grey model, forecasting

DOI: 10.3233/JIFS-210726

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Sharma, Sudeep | Padhy, Prabin K.

Article Type: Research Article

Abstract:  The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. …The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept. Show more

Keywords: Identification, estimation, modeling, adaptive learning, time delays, neural networks, lyapunov theory, intelligent systems

DOI: 10.3233/JIFS-210580

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 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. Pre-press, no. Pre-press, pp. 1-10, 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. Pre-press, no. Pre-press, pp. 1-18, 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. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Ullah, Kifayat | Ali, Zeeshan | Mahmood, Tahir | Garg, Harish | Chinram, Ronnason

Article Type: Research Article

Abstract: T-spherical fuzzy set (TSFS) is a generalized version of the spherical fuzzy set (SFS) and picture fuzzy set (PFS) to manage awkward and unpredictable information in realistic decision issues. TSFS deals with yes, abstinence, no, and refusal type of fuzzy information. This manuscript aims to observe the drawbacks of some existing dice similarity measures (DSMs) and to propose some new DSMs in the environment of TSFSs. The validation of the new DSMs is proved. The defined DSMs are further extended to introduce some generalized DSMs (GDSMs) and their special cases are studied. Additionally, the TOPSIS method using the entropy measures …(EMs) based on TSFSs is also explored and verified with the help of some examples. The proposed new GDSMs and TOPSIS method are applied to the problem of building material recognition, medical diagnosis, clustering, and the results obtained are investigated. A comparison of the new theory is established where the advancement of the proposed DSMs is elaborated under some conditions. The advantages of the new DSMs and the drawbacks of the previous DSMs of IFSs, PyFSs, and PFSs have been studied because of their applicability. The article is comprehensively summarized, and some possible future directions are stated. Show more

Keywords: Information measures, medical diagnosis, pattern recognition, T-spherical fuzzy set, TOPSIS method

DOI: 10.3233/JIFS-210402

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 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. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Process capability analysis (PCA) is a tool for measuring a process’s ability to meet specification limits (SLs), which the customers define. Process capability indices (PCIs) are used for establishing a relationship between SLs and the considered process’s ability to meet these limits as an index. PCA compares the output of a process with the SLs through these capability indices. If the customers’ needs contain vague or imprecise terms, the classical methods are inadequate to solve the problem. In such cases, the information can be processed by the fuzzy set theory. Recently, ordinary fuzzy sets have been extended to several new …types of fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets. In this paper, a new extension of intuitionistic fuzzy sets, which is called penthagorean fuzzy sets, is proposed, and penthagorean fuzzy PCIs are developed. The design of production processes for COVID-19 has gained tremendous importance today. Surgical mask production and design have been chosen as the application area of the penthagorean fuzzy PCIs developed in this paper. PCA of the two machines used in surgical mask production has been handled under the penthagorean fuzzy environment. Show more

Keywords: Process capability analysis, process capability indices, penthagorean fuzzy sets, surgical mask, COVID-19

DOI: 10.3233/JIFS-219205

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Unal, Yagmur | Temur, Gul T.

Article Type: Research Article

Abstract: In today’s highly competitive business market, the issue of sustainable supplier selection attracts great attention due to increased awareness of both social and environmental issues. The power of companies in the supply chain is determined not only by their own performance, but also by the power of other actors in the chain. As a field of study, the selection of sustainable suppliers remains its popularity because it requires developing a systematic procedure that addresses conflicting quantifiable and non-quantifiable factors simultaneously. In this study, one of the advanced fuzzy approaches called Spherical Fuzzy Sets (SFS) has been used to prioritize criteria …that affect sustainable supplier selection. The proposed methodology combines the Spherical Fuzzy Sets and the analytic hierarchy process (AHP) and takes into account four main criteria: economic, quality, social and environmental criteria. In order to prioritize the main and sub criteria, questionnaires were conducted with three experts who have valuable experience in this field of study. After then, for the selection of the best sustainable supplier, a real selection problem in an international company was handled. The figured out ranking of the criteria and supplier alternatives can be used as a guide by the researchers and industrial experts who are responsible for sustainable supplier selection. To measure how much unit increase in weights assigned to key criteria affects supplier selection, a sensitivity analysis was carried out. It was noticed that the selection procedure for the selected company is not highly sensitive to changes. Show more

Keywords: Analytic hierarchy process (AHP), spherical fuzzy sets, sustainable supplier selection

DOI: 10.3233/JIFS-219214

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Seker, Sukran

Article Type: Research Article

Abstract: The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. …Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study. Show more

Keywords: Risk assessment, uncertainty, risk matrix, IVIF-TOPSIS

DOI: 10.3233/JIFS-219210

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Huang, Danyang | Zhou, Zhiheng | Deng, Ming | Li, Zhihao

Article Type: Research Article

Abstract: Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads …into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art method by at least 0.31% and 0.34%. Show more

Keywords: Nighttime vehicle detection, advanced driver-assistance systems, attention mechanism, deep learning

DOI: 10.3233/JIFS-202676

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 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. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Trisal, Sushil Kumar | Kaul, Ajay

Article Type: Research Article

Abstract: Stress has become a household word which generates emotional distress, physical diseases, dysfunction and social ills. An abundant evidence is present in the literature that makes the stress research and theory high profile and important for physiological, psychological and social health. It can be legitimately said that due to the advent of social media, it has opened up inputs for the exploration of stress. The social media has become very prominent as it has touched daily lives. It has changed the way we are looking at the things, it has changed the life style, it has changed the way we …are consuming the information. It has created a bridge of trust among the people of different professional’s. Social media has become undeniably a global phenomenon in the last decade or so, since the founding of social media sites like Twitter and Facebook. It is of significant importance to detect and manage the stress from theses interactions at early stage otherwise it wreaks havoc on your emotional equilibrium and your physical health. It narrows your ability to think clearly, function effectively and enjoy life. In this work our endeavor is that to present a novel method to detect the different stress levels from the social media interactions using fuzzy and factor graph methods. A correlation analysis between stressed, non-stressed and emotion tweets is carried out for social engagement correlation and behavior correlation analysis of the social media users. The proposed method performs better when results are compared with the other state of art machine learning methods. Show more

Keywords: Stress, social engagement, correlation, psychological stress, machine learning, social media

DOI: 10.3233/JIFS-202035

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Xu, Haiyan | Chang, Yuqing | Zhao, Yong | Wang, Fuli

Article Type: Research Article

Abstract: Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by …separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets. Show more

Keywords: Ensemble empirical mode decomposition, long short-term memory network, elman neural network, error correction

DOI: 10.3233/JIFS-210779

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Neffati, Syrine | Ben Abdellafou, Khaoula | Aljuhani, Ahmad | Taouali, Okba

Article Type: Research Article

Abstract: The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, …we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorithm is tested on the OASIS MRI database. The proposed kernel-based classifier is found to be better performant than the existing methods. Show more

Keywords: Dimensionality reduction, CAD system, Optimization, KLPS, classification

DOI: 10.3233/JIFS-210595

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Alkan, Nurşah | Kahraman, Cengiz

Article Type: Research Article

Abstract: A circular intuitionistic fuzzy set (CIFS) recently introduced by Atanassov as a new extension of intuitionistic fuzzy sets is represented by a circle whose radius is r and whose center is composed of membership and non-membership degrees. The idea is similar to type-2 fuzzy sets, which are based on the fuzziness of membership functions with a third dimension. CIFSs help us define membership functions more flexibly, taking into account the vagueness in membership and non-membership degrees. In this study, TOPSIS, which is a multi-criteria decision-making (MCDM) method, is developed under circular intuitionistic fuzzy environment. The proposed CIF-TOPSIS method is …applied to determine the most appropriate pandemic hospital location selection problem. Then, a sensitivity analysis based on criteria weights and the weight of the decision maker’s optimistic and pessimistic attitudes are conducted to check the robustness of the decisions given by the proposed approach. A comparative analysis with the single-valued intuitionistic fuzzy TOPSIS, Pythagorean fuzzy TOPSIS, picture fuzzy TOPSIS methods is also performed to verify the developed approach and to demonstrate its effectiveness. Show more

Keywords: Circular intuitionistic fuzzy sets, intuitionistic fuzzy sets, MCDM, TOPSIS, pandemic, hospital location selection

DOI: 10.3233/JIFS-219193

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2021

Authors: Yan, Wei | Ding, Yuhan

Article Type: Research Article

Abstract: With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of …the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time. Show more

Keywords: RDF knowledge graph, keyword, type graph, frequent search pattern, Bloom coding

DOI: 10.3233/JIFS-210950

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Hu, Junying | Qian, Xiaofei | Cheng, Hao | Tan, Changchun | Liu, Xinbao

Article Type: Research Article

Abstract: Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, …the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance. Show more

Keywords: Remaining useful life, support vector regression, phase space reconstruction, variable neighborhood search, aircraft engine

DOI: 10.3233/JIFS-210740

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 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. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Sun, Kexin | Xin, Yuelan | Ma, Yide | Lou, Meng | Qi, Yunliang | Zhu, Jie

Article Type: Research Article

Abstract: U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM …is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively. Show more

Keywords: Mammograms, mass segmentation, convolutional neural network, adaptive scale module, feature refinement module

DOI: 10.3233/JIFS-210393

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Li, Shugang | Wang, Ru | Zhang, Yuqi | Lu, Hanyu | Cai, Nannan | Yu, Zhaoxu

Article Type: Research Article

Abstract: Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands …the users’ AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter. Show more

Keywords: Concept marketing, social media influencers, attention concentration index, consensus smart discriminant algorithm

DOI: 10.3233/JIFS-201809

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 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. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Wang, Jing | Yu, Liying

Article Type: Research Article

Abstract: In Dempster-Shafer theory, belief structure plays a key role, which provides a useful framework for information representation of uncertain variables. Basic Probability Assignment (BPA) is the most important component, which is difficult to be determined due to the uncertainty of information. Generally, there are two ways to get BPA of evidential theory: One is a subjective judgment of the expert’s experience, Interval Belief Structure (IBS) can solve the fuzziness and uncertainty of expert’s judgment. The other is objective calculation by sampling existing data, in which BPA is viewed as the point estimate. Therefore, one of the contributions of this paper …is that the definitions and theories of Confidential Interval Belief Structure (CIBS) is developed to describe BPA in Dempster-Shafer theory, which can give a range of population parameter values and contain more information to deal with the uncertainty and fuzziness of existing data. And then, based on evidential reasoning rule for counter-intuitive behavior, another contribution of this paper is that the extended evidential reasoning approach with CIBS is proposed to obtain the combined belief degree. The proposed method can be flexibly adjusted by appropriate errors and confidence levels, which is the main advantage. Finally, a case of sustainable operation of Shanghai rail transit system to verify the feasibility of proposed method and great performance of the extended method is shown. Show more

Keywords: Evidential reasoning, confidence interval, belief structures

DOI: 10.3233/JIFS-210286

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Zhang, Qiang | Zhang, Luyu | Sun, Bingzhen

Article Type: Research Article

Abstract: In 2020, the spread of the COVID-19 epidemic has attracted global attention. As a large-scale group that is receiving higher education, college students also show greater mood swings. How to reduce the psychological harm of anxiety to college students is a problem that needs to be solved urgently. Based on this, this paper proposes an evaluation model for the anxiety level of college students in different regions under the influence of COVID-19. First of all, the general influence index of college student’s anxiety level is obtained by correlation analysis. Secondly, the logical OR of the double quantization variable precision fuzzy …set model and the degree fuzzy rough set model is used to establish the evaluation model of the anxiety level of college students under the influence of COVID-19. Finally, used big data, the idea of fuzzy upper and lower approximation, combined with the principle of maximum membership in fuzzy set theory, achieved the quantitative ranking of the anxiety levels of college students in different areas. The research shows that when the accuracy of decision-making is 45%, the anxiety level of the township college students group and the provincial capital or municipality college students group is higher. When the accuracy of decision-making is 65%, the anxiety level of the provincial capital or municipality college students group is higher than others. Show more

Keywords: Fuzzy rough set, two universes, COVID-19, college students anxiety level

DOI: 10.3233/JIFS-202760

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zeng, Detian | Shi, Jingjia | Zhan, Jun | Liu, Shu

Article Type: Research Article

Abstract: To use the electromagnetic chuck to precisely absorb industrial parts in manufacturing, this paper presents a hybrid algorithm for grasping pose optimization, especially for the part with a large surface area and irregular shape. The hybrid algorithm is based on the Gaussian distribution sampling and the hybrid particle swarm optimization (PSO). The Gaussian distribution sampling based on the geometric center point is used to initialize the population, and the dynamic Alpha-stable mutation enhances the global optimization capability of the hybrid algorithm. Compared with other algorithms, the experimental results show that ours achieves the best results on the dataset presented in …this work. Moreover, the time cost of the hybrid algorithm is near a fifth of the conventional PSO in the discovery of optimal grasping pose. In summary, the proposed algorithm satisfies the real-time requirements in industrial production and still has the highest success rate, which has been deployed on the actual production line of SANY Group. Show more

Keywords: Particle swarm optimization, Gaussian distribution, alpha-stable distribution, grasping pose

DOI: 10.3233/JIFS-210520

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Yang, Zhan | Li, Chengliang | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment …classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM. Show more

Keywords: Aspect-based sentiment analysis, sentiment classification, dependency-relationship, attention mechanism

DOI: 10.3233/JIFS-202747

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021