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

Authors: Fatema, Nuzhat | Farkoush, Saeid Gholami | Hasan, Mashhood | Malik, H

Article Type: Research Article

Abstract: In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. …The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA. Show more

Keywords: Gravitational search algorithm, back-propagation algorithm, neural network, machine learning, optimization, occupancy, smart building

DOI: 10.3233/JIFS-189748

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

Authors: Tak, Nihat | Egrioglu, Erol | Bas, Eren | Yolcu, Ufuk

Article Type: Research Article

Abstract: Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the …optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE. Show more

Keywords: Forecast combination, meta-analysis, intuitionistic fuzzy c-means, meta fuzzy functions, meteorology

DOI: 10.3233/JIFS-202021

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

Authors: Liu, Peide | Wang, Xiyu | Teng, Fei

Article Type: Research Article

Abstract: In today’s education industry, online teaching is increasingly becoming an important teaching way, and it is necessary to evaluate the quality of online teaching so as to improve the overall level of the education industry. The online teaching quality evaluation is a typical multi-attribute group decision-making (MAGDM) problem, and its evaluation index can be expressed by linguistic term sets (LTSs) by decision makers (DMs). Especially, multi-granularity probabilistic linguistic term sets (MGPLTSs) produced from many DMs are more suitable to express complex fuzzy evaluation information, and they can not only provide different linguistic term set for different DMs the give their …preferences, but also reflect the importance of each linguistic term. Based on the advantages of MGPLTSs, in this paper, we propose a transformation function of MGPLTSs based on proportional 2-tuple fuzzy linguistic representation model. On this basis, the operational laws and comparison rules of MGPLTSs are given. Then, we develop a new Choquet integral operator for MGPLTSs, which considers the relationship among attributes and does not need to consider the process of normalizing the probabilistic linguistic term sets (PLTSs), and can effectively avoid the loss of evaluation information. At the same time, the properties of the proposed operator are also proved. Furthermore, we propose a new MAGDM method based on the new operator, and analyze the effectiveness of the proposed method by online teaching quality evaluation. Finally, by comparing with some existing methods, the advantages of the proposed method are shown. Show more

Keywords: Multiple-attribute group decision-making, online teaching quality evaluation, multi-granularity probabilistic linguistic term sets, Choquet integral

DOI: 10.3233/JIFS-202543

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

Authors: kaur, Surinder | Chaudhary, Gopal | Dinesh kumar, Javalkar

Article Type: Research Article

Abstract: Nowadays, Biometric systems are prevalent for personal recognition. But due to pandemic COVID 19, it is difficult to pursue a touch-based biometric system. To encourage a touchless biometric system, a less constrained multimodal personal identification system using palmprint and dorsal hand vein is presented. Hand based Touchless recognition system gives a higher user-friendly system and avoids the spread of coronavirus. A method using Convolution Neural Networks(CNN) to extract discriminative features from the data samples is proposed. A pre-trained function PCANeT is used in the experiments to show the performance of the system in fusion scheme. This method doesn’t require keeping …the palm in a specific position or at a certain distance like most other papers. Different patches of ROI are used at two different layers of CNN. Fusion of palmprint and dorsal hand vein is done for final result matching. Both Feature level and score level fusion methods are compared. Results shows the accuracy of upto 98.55% and 98.86% and Equal error rate (EER) of upto 1.22% and 0.93% for score level fusion and feature level fusion, respectively. Our method gives higher accurate results in a less constrained environment. Show more

Keywords: Biometrics, deep learning, feature level fusion, fusion, score level fusion

DOI: 10.3233/JIFS-189753

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

Authors: Jia, Zhifu | Liu, Xinsheng | Zhang, Yu

Article Type: Research Article

Abstract: Uncertain pantograph differential equation (UPDE for short) is a special unbounded uncertain delay differential equation. Stability in measure, stability almost surely and stability in p -th moment for uncertain pantograph differential equation have been investigated, which are not applicable for all situations, for the sake of completeness, this paper mainly gives the concept of stability in distribution, and proves the sufficient condition for uncertain pantograph differential equation being stable in distribution. In addition, the relationships among stability almost surely, stability in measure, stability in p -th moment, and stability in distribution for the uncertain pantograph differential equation are also discussed.

Keywords: uncertainty theory, uncertain pantograph differential equation, stability in distribution, the relationships among stabilities

DOI: 10.3233/JIFS-201864

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

Authors: Iqbal, Naeem | Ahmad, Rashid | Jamil, Faisal | Kim, Do-Hyeun

Article Type: Research Article

Abstract: Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and …social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc. Show more

Keywords: Movie quality prediction, machine learning, data mining, business intelligence, predictive analytics

DOI: 10.3233/JIFS-201844

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

Authors: Emeç, Şeyma | Akkaya, Gökay

Article Type: Research Article

Abstract: Energy consumption increases due to technological developments, urbanization, industrialization and population. The fact that the constantly increasing energy demand is not exactly known is an important issue for countries. In addition, due to changing climate conditions, the amount of emission emitted and energy produced from energy sources are also not quite known. Therefore, determining the energy demand, protecting the environment, and minimizing the energy cost by using resources effectively has become one of the most important problems of countries. In this context, the present study developed a fuzzy optimal renewable energy model (F-OREM) to solve the energy problem involving fuzzy …parameters. Fuzzy linear programming (FLP) models provide the best decision by producing faster and more flexible solutions compared to classical linear programming (CLP) models in situations where there are uncertainties and a lack of information. The purpose of the developed model was to minimize the cost of generating electrical energy from different energy sources in an uncertain environment under potential, demand, emission and efficiency constraints. The developed F-OREM was operated using CPLEX decoder in the GAMS 24.2.3 package program and using the particle swarm optimization (PSO) for ∝ different values between 0-1. The results showed that the results of the metaheuristic method and the results of the GAMS package program were the same, and the results were consistent According to the results obtained, the emission level at which the objective function was minimum (when ∝=1) was at the lowest level. In this case, the total emitted amount was 1,06125E+14 g-CO2/kWh.. In this context, the developed model can be applied using metaheuristic or heuristic methods for larger test cases with thousands of variables. This study contributed to the practicality of FLP by offering decision-makers a wider solution area than the CLP approach. Show more

Keywords: Energy economics, energy policy, fuzzy programming, mathematical model, optimization

DOI: 10.3233/JIFS-201994

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

Authors: Jun, Jia | Fu, Rui | Jian, Wang | Dong, Dai Yong | Xiang, Shen | Atassi, Reem

Article Type: Research Article

Abstract: This paper briefly introduces the background, significance, and development status of 3D radar technology at home and abroad, and then explains the concept, working principle, system composition, and workflow of the radar system. Combined with the current development trend of smart grids, it focuses on the application scope of this technology in the field of transmission line construction, operation, and maintenance. Then, through the specific implementation of the project cases, the daily operation and maintenance of four 500 kV transmission lines in Nanjing have played a certain guiding role. Finally, according to the development trend of smart grid and the actual …demand of power system production and business integration, this paper briefly prospects the function expansion of this technology in transmission line operation evaluation, fault analysis and diagnosis, emergency rescue plan formulation, and other fields. Show more

Keywords: Radar measurement, transmission line, 3D mapping

DOI: 10.3233/JIFS-189788

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

Authors: Xiao, Yanjun | Yu, Anqi | Qi, Hao | Jiang, Yunfeng | Zhou, Wei | Gao, Nan

Article Type: Research Article

Abstract: In the industrial field, the lithium battery industry has a long history and a large market scale. Lithium battery electrode strip rolling mill belongs to the high-end production equipment in the lithium battery industry. However, due to its complex structure, the tension of lithium battery electrode mill is prone to large fluctuation. This will lead to the phenomenon of wrinkle and looseness, which will affect the quality of the electrode strip. At present, the tension control method of lithium battery electrode mill mostly adopts traditional Proportional-Integral-Differential(PID) control. Under this control mode, the production speed and precision of lithium battery electrode …mill need to be improved. In this paper, the fuzzy PID tension control method of lithium battery electrode mill based on genetic optimization is studied. Based on fuzzy theory and PID control method, a tension fuzzy PID model is established for experimental verification, and the initial parameters and fuzzy rules of fuzzy PID are optimized by Genetic Algorithm(GA). This method has better stability, can improve the precision of strip tension control, make the tension more stable when the rolling mill is running, and help to improve the quality of electrode strip production. Show more

Keywords: Fuzzy theory, genetic algorithm, lithium battery electrode mill, PID, tension

DOI: 10.3233/JIFS-201675

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2021

Authors: Jin, Yilun | Liu, Yanan | Zhang, Wenyu | Zhang, Shuai | Lou, Yu

Article Type: Research Article

Abstract: With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a ne1 w feature-enhanced stacking method is proposed to construct an effective ensemble model by …composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models. Show more

Keywords: Credit scoring, ensemble model, imbalanced learning, K-means, stacking

DOI: 10.3233/JIFS-201954

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

Authors: Sarita, Kumari | Devarapalli, Ramesh | Kumar, Sanjeev | Malik, H. | Márquez, Fausto Pedro García | Rai, Pankaj

Article Type: Research Article

Abstract: Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also …for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis. Show more

Keywords: Machine learning, industry 4.0, PCA, condition monitoring, predictive maintenance, preprocessing

DOI: 10.3233/JIFS-189755

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

Authors: Xiao, Hui-Min | Wang, Mei-Qi | Cao, Yan-Li | Guo, Yu-Jie

Article Type: Research Article

Abstract: In this paper, to improve the situation of singleness of selecting results in hesitant fuzzy set decision-making and expand the range of choices for decision makers, we construct a hesitant fuzzy set clustering algorithm combined with fuzzy matroid operation. The algorithm synthesizes the r-cut set, fuzzy shrinking matroids in the fuzzy matroids and the operational properties of the fuzzy derived matroids, the r value also is used to connect the two types of fuzzy matroids to form a clustering algorithm. Finally, we apply the algorithm to the hesitant fuzzy set decision-making of job seekers choosing recruitment websites, each recruitment website …as an optional scheme is divided into three categories of excellent to inferior schemes to provide job seekers with ideas and methods for favorably selecting recruitment websites. Show more

Keywords: Hesitant fuzzy set decision-making, fuzzy matroid, contraction matroid, derived matroid, clustering algorithm

DOI: 10.3233/JIFS-201476

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

Authors: Fatema, Nuzhat | Malik, H | Abd Halim, Mutia Sobihah Binti

Article Type: Research Article

Abstract: This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this …procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics. Show more

Keywords: ARIMA model, explanatory feature, multi-step ahead, medical tourism forecasting, Monte Carlo simulation, feature extraction

DOI: 10.3233/JIFS-189785

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

Authors: Xu, Tongtong | Xiang, Zheng

Article Type: Research Article

Abstract: In this work, modified constant modulus algorithm based on bat algorithm is proposed for wireless sensor communications systems. The bat algorithm is a swarm intelligence optimization algorithm, which mainly used to solve optimization problems. The proposed algorithm focused on modified constant modulus algorithm, which is also applicable to the constant modulus algorithm. The error function of blind equalization algorithm is used as the evaluation function of the bat algorithm, and then the initial value of the weight vector is calculated adaptively by the bat algorithm. Theoretical analysis is provided to illustrate that the proposed algorithm has a faster convergence speed …than the original one and is suitable for almost all blind channel equalization algorithms. The simulation results support the newly proposed improved algorithm. The proposed algorithm could be applied to some more complex wireless channel environments to improve the reception performance of sensor communication systems. Show more

Keywords: Wireless sensor communications, blind equalization, bat algorithm, weight vector, modified constant modulus algorithm

DOI: 10.3233/JIFS-189709

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Chen, Yingjiao | Liang, Hu

Article Type: Research Article

Abstract: Rural revitalization is an attempt by China’s rural reform plan. Among them, the pastoral complex emphasizes the integration of agriculture and tourism, extends the agricultural chain, and integrates health care, technology, tourism, creativity, and leisure. However, the pastoral complex is faced with solving rural-urban integration in the context of the current rural revitalization strategy, involving complex factors such as land, ecological, cultural, and social issues. Therefore, this study analyzes the complex factors affecting the pastoral complex. Fuzzy calculation theory is introduced in the pastoral complex to discuss the pastoral complex system’s operational characteristics and further explore how to build an …adaptive system evaluation system at different system levels. Considering that the process needs to consider several Conflicting factors from qualitative to quantitative, to deal with the uncertainty of human judgment in the evaluation process, the process uses fuzzy analytical hierarchy process to obtain the weight of each factor and understand the degree of influence of each factor. The research results show that various factors have different degrees of influence on the pastoral complex. Therefore, in the complex pastoral process, more attention should be paid to the operation mechanism factors to make the complex pastoral system more scientific. Show more

Keywords: Fuzzy calculation, rural revitalization, pastoral complex, FAHP

DOI: 10.3233/JIFS-189727

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

Authors: Zong, Ke | Kong, Ye

Article Type: Research Article

Abstract: This research focus on the formation mechanism and intervention strategy of coal miners’ job burnout, based on a simulation study using system dynamics method. The simulation result indicates that, work assignment alienation has higher sensitivity to intervention strategies than other elements of coal miners’ job burnout, while health damage is least sensitive to intervention. The top three adoptable strategies shall be reasonable working hours, self-psychological adjustment, and psychological counseling program. As the impact of one intervention strategy weakens with time, it is necessary to constantly change intervention strategies or to adopt a strategy combination to intervene miners’ burnout. This study …explains the formation mechanism of coal miners’ job burnout and offers targeted advice for coal enterprises, aiming to effectively improve their safety management mechanism and to reduce casualties. Show more

Keywords: Coal miners, job burnout, intervention strategy, system dynamics, simulation

DOI: 10.3233/JIFS-189728

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

Authors: Zhu, Wenye | Tan, Chengxiang | Xu, Qian | Xiao, Ya

Article Type: Research Article

Abstract: The cross-trust domain environment in which heterogeneous identity alliances are located often does not have a completely trusted centralized trust root, and different trust domains and entities also have specific security requirements. In view of the above problems, we believe that trust measurement of cross-domain identities based on risk assessment is an effective method to achieve decentralized proof of user identities in heterogeneous cyberspace. There are various risk assessment models. We choose the more mature attack graph theory in the existing research to apply to the new field of cross-trust domain management of heterogeneous identities. We propose an attribute attack …graph evaluation model to evaluate cross-domain identities through risk measurement of attributes. In addition, heterogeneous identity alliances also have architectural risks, especially the risk of decentralized underlying structures. In response to this problem, we identify the risk of the identity alliance infrastructure, and combine the risk assessment and presentation system design to verify the principle. Show more

Keywords: Heterogeneous identity alliance, attribute attack graph, proof of identity, cyber security assessment, trust management

DOI: 10.3233/JIFS-189729

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

Authors: Malik, Hasmat | Gopal, | Srivastava, Smriti

Article Type: Editorial

Abstract: The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such …as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation based performance demonstration and finally conclusive summary of the developed tool/technique along with future scope. Show more

Keywords: Digital transformation, advancement, artificial intelligence, machine learning, application, data analytics, cybersecurity, condition monitoring, fault detection and diagnosis, prediction, forecasting, renewable energy, feature extraction, feature selection, healthcare, greater sustainability, resilient infrastructure, automation

DOI: 10.3233/JIFS-189787

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Zhao, Hu | Sayed, O.R. | El-Sanousy, E. | Ragheb Sayed, Y.H. | Chen, Gui-Xiu

Article Type: Research Article

Abstract: Different from the separation axioms in the framework of (L , M )-fuzzy convex spaces defined by Liang et al.(2019). In this paper, we give some new investigations on separation axioms in (L , M )-fuzzy convex structures by L -fuzzy hull operators and r -L -fuzzy biconvex. We introduce the concepts of r -LFS i spaces where i  = {0, 1, 2, 3, 4}, and obtain various properties. In particular, we discuss the invariance of these separation properties under subspace and product.

Keywords: r-LFS0 space, r-LFS1 space, r-LFS2 space, r-LFS3 space, r-LFS4 space

DOI: 10.3233/JIFS-200340

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

Authors: Ding, Xiaobing | Yang, Kaihe | Hu, Hua | Liu, Zhigang

Article Type: Research Article

Abstract: There is a close relationship between the operation safety and the application of training equipment. If the relationship is not handled well, it will lead to serious problems, even the Conflicts. So designing training equipment management information system is extremely urgent. First, the main training categories are carded, such as drivers training, construction and maintenance, daily safety management, canteen safety, etc., and the basic flow chart of the 4 types of training are drawn; Second, the training equipment management database Train_Database is constructed based on training process, equipment involved, trainers, contingency plans of the 4 kinds of raw data, which …lay the foundation for the follow-up of the management information system design and development; Third, the training equipment declaration and management system is developed, which is called Training_Equipment_MS, and the main modules are: equipment resource information management module, equipment declaration module, equipment audit module, safety check form filling module, equipment declaration results publicity module, etc. Finally, the functions of each module are shown in details. It has good practical guidance to the application and operation of rail transit, which can reduce accidents and hidden danger in the course of training. Show more

Keywords: Rail transit training, safety management of equipment, resource declaration and audit, design and development of system

DOI: 10.3233/JIFS-189725

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

Authors: Alzubi, Jafar A. | Jain, Rachna | Alzubi, Omar | Thareja, Anuj | Upadhyay, Yash

Article Type: Research Article

Abstract: The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver’s negligence towards safety a possible solution is to monitor the driver and …driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. Show more

Keywords: Machine learning, deep learning, convolutional neural network, CNN, distraction detection, model compression, pruning, quantization, deep compression

DOI: 10.3233/JIFS-189786

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

Authors: Yan, Zheping | Zhang, Jinzhong | Zeng, Jia | Tang, Jialing

Article Type: Research Article

Abstract: In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and …higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust. Show more

Keywords: Water wave optimization (WWO), autonomous underwater vehicle (AUV), path planning, randomly generated threat areas, generated fixed threat areas

DOI: 10.3233/JIFS-201544

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

Authors: Wei, Guangcun | Rong, Wansheng | Liang, Yongquan | Xiao, Xinguang | Liu, Xiang

Article Type: Research Article

Abstract: Aiming at the problem that the traditional OCR processing method ignores the inherent connection between the text detection task and the text recognition task, This paper propose a novel end-to-end text spotting framework. The framework includes three parts: shared convolutional feature network, text detector and text recognizer. By sharing convolutional feature network, the text detection network and the text recognition network can be jointly optimized at the same time. On the one hand, it can reduce the computational burden; on the other hand, it can effectively use the inherent connection between text detection and text recognition. This model add the …TCM (Text Context Module) on the basis of Mask RCNN, which can effectively solve the negative sample problem in text detection tasks. This paper propose a text recognition model based on the SAM-BiLSTM (spatial attention mechanism with BiLSTM), which can more effectively extract the semantic information between characters. This model significantly surpasses state-of-the-art methods on a number of text detection and text spotting benchmarks, including ICDAR 2015, Total-Text. Show more

Keywords: Scene text spotting, End-to-end, Joint optimization, TCM, SAM-BiLSTM

DOI: 10.3233/JIFS-200903

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

Authors: Wang, Jian | Zhu, Yuanguo

Article Type: Research Article

Abstract: Uncertain delay differential equation is a class of functional differential equations driven by Liu process. It is an important model to describe the evolution process of uncertain dynamical system. In this paper, on the one hand, the analytic expression of a class of linear uncertain delay differential equations are investigated. On the other hand, the new sufficient conditions for uncertain delay differential equations being stable in measure and in mean are presented by using retarded-type Gronwall inequality. Several examples show that our stability conditions are superior to the existing results.

Keywords: Uncertainty theory, uncertain delay differential equation, analytic solution, stability

DOI: 10.3233/JIFS-202507

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

Authors: Li, Chengzheng | Peng, Ying | Peng, Peng | Cao, Lei

Article Type: Research Article

Abstract: Investigating the factors influencing the performance of social conditioning in the network environment is the core issue for improving academic performance. Through the search of existing literature, the paper analyzes the main factors that influence social conditioning learning in current research, and through the questionnaire survey and in-depth processing of the raw data, the advanced behavioral indicators related to learning are obtained and analyzed by Spearman correlation coefficient and fuzzy modeling in machine learning. The results showed that the twelve dimensions of motivation regulation, trust building, efficacy management, cognitive strategy, time management, goal setting, task strategy, peer support, team assessment, …help seeking, environment construction, and team supervision were significantly related to group performance, with team supervision having a significant negative relationship with group performance. In addition, trust building, team supervision and environment construction were the main factors for online social learning, effectiveness management, task strategy, peer support and help-seeking were the secondary factors, while motivation regulation, cognitive strategies, goal setting and team assessment had little impact on the final performance. The findings have some implications for the optimization of social conditioning learning support services and the improvement of social conditioning learning performance. Show more

Keywords: Learning analysis, online collaborative learning, socially modulated learning, machine learning

DOI: 10.3233/JIFS-189724

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

Authors: Liu, Fang | Liu, Yi | Abdullah, Saleem

Article Type: Research Article

Abstract: Based on decision theory rough sets (DTRSs), three-way decisions (TWDs) provide a risk decision method for solving multi-attribute decision making (MADM) problems. The loss function matrix of DTRS is the basis of this method. In order to better solve the uncertainty and ambiguity of the decision problem, we introduce the q-rung orthopair fuzzy numbers (q-ROFNs) into the loss function. Firstly, we introduce concepts of q-rung orthopair fuzzy β -covering (q-ROF β -covering) and q-rung orthopair fuzzy β -neighborhood (q-ROF β -neighborhood). We combine covering-based q-rung orthopair fuzzy rough set (Cq-ROFRS) with the loss function matrix of DTRS in the q-rung …orthopair fuzzy environment. Secondly, we propose a new model of q-ROF β -covering DTRSs (q-ROFCDTRSs) and elaborate its relevant properties. Then, by using membership and non-membership degrees of q-ROFNs, five methods for solving expected losses based on q-ROFNs are given and corresponding TWDs are also derived. On this basis, we present an algorithm based on q-ROFCDTRSs for MADM. Then, the feasibility of these five methods in solving the MADM problems is verified by an example. Finally, the sensitivity of each parameter and the stability and effectiveness of these five methods are compared and analyzed. Show more

Keywords: Covering-based q-rung orthopair fuzzy rough sets, q-ROF β-covering decision-theoretic rough sets, q-ROF β-neighborhood, MADM, DTRSs

DOI: 10.3233/JIFS-202291

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

Authors: Liu, Shulin

Article Type: Research Article

Abstract: Under the background of the national fitness craze, the demand space for social sports professionals is constantly expanding. However, according to the author’s investigation, the overall situation shows that the number of high-quality social sports professionals in Chinese colleges and universities is relatively small. Among them, the unsound teaching quality evaluation system of social sports major is one of the important reasons affecting the cultivation of high-quality talents, so it is imperative to construct a sound teaching quality evaluation system of social sports major. At the same time, the perfect social physical education teaching quality evaluation system is an important …basis for teachers’ teaching job evaluation and strengthening teachers’ management. And it is frequently considered as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS method and intuitionistic fuzzy sets (IFSs), this essay designs a novel intuitive distance based IF-TOPSIS method for teaching quality evaluation of physical education. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each alternative. Eventually, an application about teaching quality evaluation of physical education and some comparative analysis have been given. The results think that the designed method is useful for teaching quality evaluation of physical education. Show more

Keywords: Multi-attribute group decision-making (MAGDM), intuitionistic fuzzy sets (IFSs), TOPSIS method, CRITIC method, teaching quality evaluation, physical education

DOI: 10.3233/JIFS-201672

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

Authors: Wang, Zhiru | Wang, Min | He, Ruyu | Bhamra, Ran S. | Yang, Lili

Article Type: Research Article

Abstract: In order to better achieve active defense in the escalator risk management, this study based on the vulnerability theory, task driven theory, management error theory, proposed a Gray Relational Analysis (GRA) based fuzzy assessment of escalator accident risk approach. The risk assessment index system of subway station escalator accident was constructed based on the commonness and essence of management defects; the weight of risk index was calculated scientifically and reasonably by using Analytic Hierarchy Process (AHP); escalator accident risk was evaluated by the combination of GRA and Fuzzy approach. The results show that escalator equipment, environment, safety knowledge of riders …are all in good condition in the station. However, ‘Maintenance’ of escalator in the Beijing subway station is in an extremely high risk level. The contributions of this studies are: (1) general risk elements analysis model for escalator accidents which enable to compose any risk factor possible to induce escalator accident in subway station; (2) GRA based risk assessment approach can avoid the problem when expend the range to left and right. It can also judge whether the continuous improvement effect of the object is significant by the difference degree of each risk level before and after. Show more

Keywords: Subway escalator incident, risk assessment, gray relational analysis (GRA), gray clustering, fuzzy method

DOI: 10.3233/JIFS-189722

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

Authors: Li, Jingyuan

Article Type: Research Article

Abstract: In 2020, under the major impact of the global COVID-19 epidemic, people are paying more and more attention to safety issues including health and personal safety, and relevant supervision and testing methods are also constantly updated across different countries. In industrial enterprises, both spread of diseases and occurrence of accidents are largely caused by unsafe state of things and unsafe behaviors of people, among which, human factor is the most important factor. In recent years, many scholars have conducted theoretical and practical research on individual behavior from the perspective of individual psychological characteristics. Where, individual initiative as an important individual …psychological trait is increasingly incorporated in the research category of safety behavior. A close correlation exists between individual initiative and evolution of safety production behavior. According to constraint conditions and replicator dynamics equation, this paper uses evolutionary game method and computer fuzzy system Matlab simulation software to conduct numerical experiment analysis on the ideal state of the game between organizations and individuals, thereby studying the behavior evolution trend. The basic idea of fuzzy control is to use computer to realize human’s qualitative control experience. Research is found that whether an individual adopts safety obedience behavior will be directly affected by whether the organization adopts regulatory safety production management model. And if the organization adopts regulatory safety production management model and the individual does not implement safety obedience behavior, it is impossible to achieve a stable state. The evolution process of the two to the ideal state is affected by multiple factors. Show more

Keywords: Safety management, safety behavior, fuzzy system simulation, evolutionary game

DOI: 10.3233/JIFS-189723

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

Authors: Hua, Xing | Yue, Qi

Article Type: Research Article

Abstract: Based on a survey database of cross-border acquisitions by Chinese private firms, this study uses a fuzzy-set qualitative comparative analysis (fsQCA) to explore the holistic impact of acquisition ownership, organizational factors and environmental factors on acquisition performance in cross-border acquisitions. It is found that the cross-border acquisitions taken by Chinese private enterprises have four kinds of acquisition ownership strategies leading to high acquisition performance under different internal and external conditions. This study points out that ownership strategy is a key decision affecting cross-border acquisition performance and provides a variety of paths leading to the same outcome rather than just finding …the linear relationship between corporate activity and performance. This study supports the assumption of equivalence, and reveals a variety of scenarios in which cross-border acquisition ownership contributes to the outcome of high cross-border acquisition performance, and further confirms the view of causal asymmetry between condition and outcome. This study reveals whether the proportion of cross-border acquisition ownership affects cross-border acquisition performance and under what circumstances is conducive to the realization of expected cross-border acquisition performance. Show more

Keywords: Fuzzy-set qualitative comparative analysis (fsQCA), acquisition ownership, acquisition performance, cross-border acquisition

DOI: 10.3233/JIFS-189720

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

Authors: Li, Dong | Sun, Xin | Gao, Furong | Liu, Shulin

Article Type: Research Article

Abstract: Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce false alarm rate. We …present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases. Show more

Keywords: Negative selection algorithm, anomaly detection, artificial immune algorithms, machine learning

DOI: 10.3233/JIFS-200405

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

Authors: Yin, Tingting | Yang, Zhong | Wu, Youlong | Jia, Fangxiu

Article Type: Research Article

Abstract: The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation …method has the advantages of the high accuracy and good real-time performance. Show more

Keywords: Ordnance science and technology, high precision, roll attitude estimation, PMSG, hall-effect sensor, relative roll dynamic model

DOI: 10.3233/JIFS-189718

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

Authors: Mohanta, Bhabendu Kumar | Jena, Debasish | Mohapatra, Niva | Ramasubbareddy, Somula | Rawal, Bharat S.

Article Type: Research Article

Abstract: Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random …Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices. Show more

Keywords: Intelligent data analytics, machine learning, intelligent transportation system, secure communication, internet of things

DOI: 10.3233/JIFS-189743

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

Authors: Bai, Luyi | Li, Nan | Liu, Lishuang | Hao, Xuesong

Article Type: Research Article

Abstract: With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to …RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall , precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper. Show more

Keywords: Data integration, fuzzy query, fuzzy spatiotemporal data, RDF semantic model

DOI: 10.3233/JIFS-202357

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

Authors: Tabrez, Md | Iqbal, Atif | Sadhu, Pradip Kumar | Husain, Mohammed Aslam | Bakhsh, Farhad Ilahi | Singh, S. P.

Article Type: Research Article

Abstract: Impedance mismatching between different phases of a multiphase transformer is generally observed e.g., in a three-phase to seven-phase transformer, due to an unequal number of turns in different coils. This mismatching introduces error in the study of per phase equivalent circuit diagrams as well as induces an imbalance in output voltages and currents. Therefore, it is a challenging task to develop a per-phase equivalent circuit for the secondary and primary sides (In some cases) too. This paper proposes an artificial intelligence optimization technique like PSO based modeling of the per-phase equivalent circuit of the secondary side. This paper deals with …the modeling and simulation of a three-phase to seven-phase power transformer using Artificial Intelligence technique like particle swarm optimization (PSO) and Genetic Algorithm (GA). The proposed model is optimized through PSO and GA algorithms and tested for minimum voltage error in each phase. The proposed model is designed and the objective function is optimized by PSO & GA in MATLAB environment. It is found that the optimized model can be effectively implemented as a per-phase equivalent circuit for the secondary side. Show more

Keywords: Genetic algorithm, multiphase, particle swarm optimization, transformer, seven-phase

DOI: 10.3233/JIFS-189741

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

Authors: Xia, Yingchun | Xie, Zhiqiang | Xin, Yu | Zhang, Xiaowei

Article Type: Research Article

Abstract: The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed …to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible. Show more

Keywords: Customized products, integrated scheduling, multiple workshop, fixed output, branch and bound

DOI: 10.3233/JIFS-189721

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

Authors: Li, Chongchong | Xiong, Jiangyong | Liu, Tingshan | Zhang, Ziang

Article Type: Research Article

Abstract: In order to further improve vehicle ride performance, a dynamic monitoring feedback iteration control algorithm is proposed by combining the features of a variable-damping semi-active suspension system and applying them to the system. A seven-degree-of-freedom finished vehicle simulation model is built based on MATLAB/Simulink. The root-mean-square values of the acceleration of the sprung mass, the dynamic travel of the suspension and the dynamic tire load are taken as evaluation indicators of vehicle ride performance. An analytic hierarchy process (AHP) is used to determine the weighting coefficients of the evaluation indicators, and a genetic algorithm is utilized to determine the optimal …damping of the suspension under various typical working conditions. Suspension damping is controlled with a dynamic monitoring feedback iteration algorithm. The correction coefficients of the control algorithm are determined according to the deviation between the obtained damping and the optimized damping so that the control parameters will agree with the optimal result under typical working conditions, and the control effect under other working conditions is verified. The simulation results indicate that the proposed dynamic monitoring feedback iteration control algorithm can effectively reduce the root-mean-square value of the acceleration of the sprung mass by 10.56% and the root-mean-square value of the acceleration of the dynamic travel of the suspension by 11.98% under mixed working conditions, thus improving vehicle ride performance. The study in this paper provides a new attempt for damping control of semi-active suspension and lays a theoretical foundation for its application in engineering. Show more

Keywords: Semi-active suspension, controlled damping, dynamic monitoring feedback iteration, analytic hierarchy process, genetic algorithm, ride performance

DOI: 10.3233/JIFS-189719

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

Authors: YE, Lv | Yang, Yue | Zeng, Jian-Xu

Article Type: Research Article

Abstract: The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The …captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent. Show more

Keywords: Intelligent recommendation, interpretability, XGBoost, attention mechanism, cross feature

DOI: 10.3233/JIFS-202308

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

Authors: Zhang, Pengdan | Liu, Qing | Kang, Bingyi

Article Type: Research Article

Abstract: Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA …weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model. Show more

Keywords: Fuzzy analytic hierarchy process(Fuzzy AHP), ordered weighted average (OWA), analytic hierarchy process (AHP), uncertain preferences, multi-attribute decision-making (MADM)

DOI: 10.3233/JIFS-202168

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

Authors: Zhang, Haowen | Dong, Yabo | Xu, Duanqing

Article Type: Research Article

Abstract: Time series classification is a fundamental problem in the time series mining community. Recently, many sophisticated methods which can produce state-of-the-art classification accuracy on the UCR archive have been proposed. Unfortunately, most of them are parameter-laden methods and require fine-tune for different datasets. Besides, training these classifiers is very computationally demanding, which makes them difficult to use in many real-time applications and previously unseen datasets. In this paper, we propose a novel parameter-light algorithm, MDTW, to classify time series. MDTW has a few parameters which do not require any fine-tune and can be chosen arbitrarily because …the classification accuracy is largely insensitive to the parameters. MDTW has no training step; thus, it can be directly applied to unseen datasets. MDTW is based on a popular method, namely the nearest neighbor classifier with Dynamic Time Warping (NN-DTW). However, MDTW performs much faster than NN-DTW by representing time series in different resolutions and using filters-and-refine framework to find the nearest neighbor. The experimental results demonstrate that MDTW performs faster than the state-of-the-art, with small losses (<3%) in average classification accuracy. Besides, we embed a technique, prunedDTW, into the MDTW procedure to make MDTW even faster, and show by experiments that this combination can speed up the MDTW from one to five times. Show more

Keywords: Time series classification, Dynamic Time Warping, nearest neighbor, multilevel representations, filters-and-refine

DOI: 10.3233/JIFS-201281

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

Authors: Guo, Shunsheng | Gao, Yuji | Guo, Jun | Yang, Zhijie | Du, Baigang | Li, Yibing

Article Type: Research Article

Abstract: With the aggravation of market competition, strategic supplier is becoming more and more critical for the success of manufacturing enterprises. Suppler selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the long-term development of organizations. Hence, strategic supplier selection must be restructures considering the long-term relationships and prospects for sustainable cooperation. This paper proposes a novel multi-stage multi-attribute group decision making method under an interval-valued q-rung orthopair fuzzy linguistic set (IVq-ROFLS) environment considering the decision makers’ (DMs) psychological state in the group decision-making process. First, the initial comprehensive fuzzy evaluations of DMs are …represented as IVq-ROFLS. Subsequently, two new operators are proposed for aggregating different stages and DMs’ preferences respectively by extending generalized weighted averaging (GWA) to IVq-ROFLS context. Later, a new hamming distance based linear programming method based on entropy measure and score function is introduced to evaluate the unknown criteria weights. Additionally, the Euclidean distance is employed to compute the gain and loss matrix, and objects are prioritized by extending the popular Prospect theory (PT) method to the IVq-ROFLS context. Finally, the practical use of the proposed decision framework is validated by using a strategic supplier selection problem, as well as the effectiveness and applicability of the framework are discussed by using comparative analysis with other methods. Show more

Keywords: Strategic supplier selection, multi-stage multi-attribute group decision making, interval-valued q-rung orthopair fuzzy linguistic set, hamming distance based linear programming, prospect theory

DOI: 10.3233/JIFS-202415

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

Authors: Sharma, Ajit Kumar | Bhushan, Bharat

Article Type: Research Article

Abstract: The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy …system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed. Show more

Keywords: Sliding mode control (SMC), inverted pendulum, adaptive control, fuzzy control, fuzzy sliding mode control (FSMC)

DOI: 10.3233/JIFS-189740

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

Authors: Zhang, Wei Min | Zhang, Long | Zhang, Zheyu | Sun, Mingjun

Article Type: Research Article

Abstract: With the many varieties of AI hardware prevailing on the market, it is often hard to decide which one is the most suitable to use but not only with the best performance. As there is an industry-wide trend demand for deep learning deployment, the inference benchmark for the effectiveness of DNN processor becomes important and is of great help to select and optimize AI hardware. To systematically benchmark deep learning deployment platforms, and give more objective and useful metrics comparison. In this paper, an end to end benchmark evaluation system was brought up called IBD, it combined 4 steps include …three components with 6 metrics. The performance comparison results are obtained from the chipsets from Qualcomm, HiSilicon, and NVIDIA, which can provide hardware acceleration for AI inference. To comprehensively reflect the current status of the DNN processor deploying performance, we chose six devices from three kinds of deployment scenarios which are cloud, desktop and mobile, ten models from three different kinds of applications with diverse characteristics are selected, and all these models are trained from three major training frameworks. Several important observations were made by using our methodologies. Experimental results showed that workload diversity should focus on the difference came from training frameworks, inference frameworks with specific processors, input size and precision (floating and quantized). Show more

Keywords: AI, deep neural network processor, benchmark, end to end, inference

DOI: 10.3233/JIFS-202552

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

Authors: Shi, Honghua | Ni, Yaodong

Article Type: Research Article

Abstract: Today’s supply chains have a greater likelihood of disruption risks than ever before. Sometimes, a lengthy recovery period is needed for supply chains to return to regular operation after being disrupted. During the recovery time window, how to increase the performance of supply chains is not sufficiently studied. Furthermore, the works considering parameter uncertainty arising from the lack of historical data are also limited. To address these problems, we formulate the recovery scheduling of supply chains under major disruption as mixed-integer linear programming models. In the presented models, outsourcing strategy and capacity expansion strategy are introduced to increase the service …level of the supply chain after the disruption. The effects of disruption risks on supply chain performance are quantified using uncertainty theory in the absence of historical data. A set of computational examples illustrate that cost may increase markedly when more facilities are disrupted simultaneously. Thus, decision-makers have to pay close attention to supply chain disruption management and plan for disruption in advance. Moreover, the results suggest that outsourcing strategy is more useful to reduce cost when a higher service level is required. Show more

Keywords: Supply chain, facility disruptions, recovery strategies, uncertainty

DOI: 10.3233/JIFS-202176

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

Authors: Jain, Achin | Jain, Vanita

Article Type: Research Article

Abstract: This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, …we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T -Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time. Show more

Keywords: Classification, sentiment analysis, genetic algorithm, support vector machine, machine learning

DOI: 10.3233/JIFS-189738

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

Authors: Sanaullah, Asif | Fatema, Nuzhat | Malik, H | Sanaullah, Arif | Ather, Muhammad

Article Type: Research Article

Abstract: The purpose of this study was to examine the relationship of relationship benefit and commitment in developing customer loyalty first and then to develop the intelligent model to predict the customer loyalty. Survey methodology was used to gather data from three different service sector based on the classification by Bowen. A sample of 600 customers and responses were collected randomly from the front desk of services. Regression analysis by Using SPSS 20 was applied to analyze the data collected. The finding of the study revealed that relationship benefit and commitment had direct positive influence on customer loyalty. Furthermore the commitment …of customer towards an organization is instrumental in developing loyalty. After performing the advance data analytics, ANN model was developed to predict the loyalty, which can be utilized to prepare the further directions and road map for service industry. Obtained results reveals that proposed machine intelligence approach is very useful for service industry for short-term as well long-term future planning. Show more

Keywords: Relationship benefit (RB), customer loyalty (CL), confidence benefit (CB), special treatment benefit (STB), social benefit (SB), affective commitment (AC), normative commitment (NC) and calculative commitment CC, ANN

DOI: 10.3233/JIFS-189742

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

Authors: Ray, Papia | Salkuti, Surender Reddy | Biswal, Monalisa

Article Type: Research Article

Abstract: In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target Distributed Generation (DG) location of the distribution network under study. In one of the schemes, Discrete Wavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of …loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9 MW wind farm driven by six 1.5 MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based approach provides a better resolution and quick detection of islanding than the wavelet transform approach. Show more

Keywords: Artificial neural network, islanding detection, wavelet transforms, distributed generation, S-transform

DOI: 10.3233/JIFS-189746

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

Authors: Wang, Caichuan | Li, Jiajun

Article Type: Research Article

Abstract: The decision on the investment project is to analyze the feasibility and rationality of the project plan from multiple angles. However, due to the limitations of the actual project investment decision-making, this paper proposes a group decision making method based multifunctional intuitively fuzzy VIKOR interval sets. Firstly, according to the established investment decision-making model, the first round of preliminary candidate project schemes is selected. According to the definition of interval intuitionistic fuzzy sets and the traditional VIKOR method, established the research method of this article, and the project investment decision-making model based on VIKOR interval intuitionistic fuzzy sets is established. …Finally, the project schemes are sorted according to the closeness degree of schemes. The results show that when sorting each candidate by Qi value, A4 >  A3 >  A2 >  A1 can be obtained. Because Q4 = 0, Q3 = 0.31, the condition q3-q4 >  0.25 is satisfied. It is concluded that the method can not only meet the needs of actual decision-making, but also has strong operability and practicability. The research results have reference value and guiding significance for project investment decision-making, and can promote the sustainable development of the project. Show more

Keywords: Project investment decision, break intuitively vague sets, VIKOR method, multi-attribute group decision making method

DOI: 10.3233/JIFS-189735

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

Authors: Jia, Heming | Lang, Chunbo

Article Type: Research Article

Abstract: Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the …proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way. Show more

Keywords: Salp swarm algorithm, crossover scheme, Lévy flight, functions optimization

DOI: 10.3233/JIFS-201737

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

Authors: Xu, Tingting | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method …is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses. Show more

Keywords: Pythagorean fuzzy set, 2-tuple probability weight, real-value 2-tuple, TOPSIS method

DOI: 10.3233/JIFS-201533

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