Journal of Intelligent & Fuzzy Systems - Volume 42, issue 4

Authors: Muhammed Anees, V. | Santhosh Kumar, G.

Article Type: Research Article

Abstract: Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate …models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results. Show more

Keywords: Crowd flow, surveillance, optical flow, crowd model, stability analysis

DOI: 10.3233/JIFS-200667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2829-2843, 2022

Authors: Liang, Tao | Zhao, Qing | Shi, Huan

Article Type: Research Article

Abstract: Wind energy, a highly popular renewable clean energy, has been increasingly valued by the international community and been leaping forward. However, the original wind speed signal characterized by intermittent fluctuations impose heavy burdens on wind speed forecasting of wind farms. This study proposed a wind speed forecasting method by complying with a model integrating the Variational Mode Decomposition (VMD) and the Improved Multi-Objective Dragonfly Optimization Algorithm (IMODA). First, the VMD was adopted to decompose the original wind speed signal, as an attempt to obtain multiple sub-sequences (IMFs) exhibiting stable frequency domain. Second, to simplify the calculation, the sample entropy (SE) …was adopted for the sequence recombination, and the respective recombined sub-sequence of the wind speed was forecasted by using four advanced neural networks. Lastly, the IMODA algorithm was adopted to fuse the forecasting results of the neural network, and the results of the optimal wind speed were forecasted. To verify the effectiveness and adaptability of the algorithm, the wind farm data in four different regions were forecasted. As indicated from the results, this algorithm could outperform other algorithms in the comprehensive forecasting accuracy and the model calculation time, and it could be effectively applied for the wind speed forecasting in wind farms. Show more

Keywords: Wind speed forecasting, variational mode decomposition, IMODA, combined model

DOI: 10.3233/JIFS-201191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2845-2861, 2022

Authors: Zhao, Yucheng | Liang, Jun | Chen, Long | Wang, Yafei | Gong, Jinfeng

Article Type: Research Article

Abstract: Driving behavior type is a hotspot in transportation field, but there have been few studies on free driving behavior type. The factor of current driving behavior evaluation model is single, and its environmental adaptability is insufficient, and driving behavior type is difficult to predict accurately. In addition, free driving behavior as one kind of the important driving operation behaviors lacks quantitative assessment methods and models. In view of these deficiencies, evaluation and prediction of free driving behavior based on Fuzzy Comprehensive Support Vector Machine (FC-SVM) is proposed. Firstly, a variety of individual decision-making behavior data obfuscating with environmental complexity are …collected. These obtained parameters were used as FC multi-factor evaluation parameters to quantitatively evaluate free driving behavior from multiple aspects, and to qualitatively derive the driver’s driving behavior type. Further, the SVM used the RBF kernel function to obtain the optimal parameters and train the SVM network, and it used the obtained SVM model for the prediction of driving behavior type in short time. The results of simulations using different methods show that the SD value of FC-SVM evaluation results is the lowest, only 1.273. Compared with other common methods, its MacroP reaches 89.2%. It is interesting to find that aggressive driving can be more distinct from other behavior types. Moreover, the mixed traffic flow composed of aggressive driver has a higher traffic efficiency in basic sections. This work is of great value for improving driving behavior, reducing road congestion and improving road traffic efficiency in the mixed intelligent traffic. Show more

Keywords: Free driving behavior, fuzzy comprehensive, support vector machine, evaluation and prediction, intelligent system

DOI: 10.3233/JIFS-201680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2863-2879, 2022

Authors: Porto de Lima, Byanca | da Silva, Aneirson Francisco | Marins, Fernando Augusto Silva

Article Type: Research Article

Abstract: This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PROMETHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD’s customer requirements (CRs) and PROMETHEE II was used to select the best packaging …design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives. Show more

Keywords: Decision making problem, hesitant fuzzy linguistic term sets, hesitant fuzzy, house of quality, AHP, PROMETHEE

DOI: 10.3233/JIFS-201739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2881-2897, 2022

Authors: Ren, Shengbing | Zuo, Xing | Chen, Jun | Tan, Wenzhao

Article Type: Research Article

Abstract: The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these …extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth. Show more

Keywords: Fault localization framework, program spectrum, feature extraction, RBF neural network, ridge regression

DOI: 10.3233/JIFS-202931

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2899-2914, 2022

Authors: Sun, Jinyang | Liu, Baisong | Ren, Hao | Huang, Weiming

Article Type: Research Article

Abstract: The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At …the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators. Show more

Keywords: Recommendation system, GAN, implicit feedback, neural networks

DOI: 10.3233/JIFS-210123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2915-2923, 2022

Authors: Zhang, Yaling | Liu, Hongwei

Article Type: Research Article

Abstract: A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used …to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem. Show more

Keywords: Second-order cone programming, G-norm distance function, Neural network, Dynamic differential equation

DOI: 10.3233/JIFS-210164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2925-2937, 2022

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 an 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. 42, no. 4, pp. 2939-2956, 2022

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. 42, no. 4, pp. 2957-2977, 2022

Authors: Anandhalekshmi, A.V. | Srinivasa Rao, V. | Kanagachidambaresan, G.R.

Article Type: Research Article

Abstract: Internet of Things (IoT) based healthcare monitoring system is becoming the present and the future of the medical field around the world. Here the monitoring system acquires the regular health details of hospital discharged patients like elderly patients, patients out of critical operations, and patients from remote areas, etc., and transmits it to the doctors. But the system is highly susceptible to sensor faults. Hence a data-driven hybrid approach of Hidden Markov Model (HMM) based on baum-welch algorithm with Support Vector Machine (SVM) is proposed to predict the abnormality caused by the medical sensors. The proposed work first perform the …abnormality detection on the sensor data using the HMM based on baum-welch algorithm in which the normal data is separated from abnormal data followed by classifying the abnormal data as critical patient data or sensor fault data using the SVM. Here the proposed work efficiently performs fault diagnosis with an overall accuracy of 99.94% which is 0.59% better than the existing SVM model. And also a comparison is made between the hybrid approach and the existing ML algorithms in terms of recall and F1-score where the proposed approach outperforms the other algorithms with a recall value of 100% and F1-score of 99.7%. Show more

Keywords: Internet of Things, Healthcare, Fault diagnosis, Hidden Markov Model, Baum-Welch algorithm, Support Vector Machine

DOI: 10.3233/JIFS-210615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2979-2988, 2022

Authors: Saif, Shahela | Tehseen, Samabia

Article Type: Research Article

Abstract: Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being …designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution. Show more

Keywords: Video forgery, forgery detection, deepfakes, deepfake videos, deepfake detection

DOI: 10.3233/JIFS-210625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2989-3009, 2022

Authors: Fathy, E. | Ammar, E. | Helmy, M.A.

Article Type: Research Article

Abstract: In real-world problems, the parameters of optimization problems are uncertain. A class of multilevel linear programming (MLLP) with uncertainty problem models cannot be determined exactly. Hence, in this paper, we are concerned with studying the uncertainty of MLLP problems. The main motivation of this paper is to obtain the solution to a multilevel rough interval linear programming (MLRILP) problem. To obtain that, we start turning the problem into its competent crisp equivalent using the interval method. Moreover, we rely on three methods to address the problem of multiple levels. First, by applying the constraint method in which upper levels give …satisfactory solutions that are reasonable in rank order to the lower levels, second, by an interactive approach that uses the satisfaction test function, and third, by the fuzzy approach that is based on the concept of the tolerance membership function. A numerical example is given for illustration and to examine the validity of the approach. An application to deduce the optimality for the cost of the solid MLLP transportation problem in rough interval environment is presented. Show more

Keywords: Multilevel linear programming, constraint method, interactive approach, fuzzy approach, rough interval programming

DOI: 10.3233/JIFS-210694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3011-3028, 2022

Authors: Tao, Nana | Ding, Chunxiao | Zhu, Yuanguo

Article Type: Research Article

Abstract: The pessimistic value of uncertain variables is a critical value to deal with optimization problems in environments with uncertainty. In many uncertain decision problems, pessimistic values at a certain level of reliability sometimes get attention, such as the problem that the objective function is time or cost. This article introduces two definitions of pessimistic value stability and attractivity. And the corresponding judgment conditions of attractivity are presented for linear differential systems with uncertainty. Furthermore, pessimistic value stability is analyzed for three kinds of nonlinear uncertain differential systems. Then pessimistic value attractivity is considered for a kind of nonlinear differential system …with uncertainty. Show more

Keywords: Pessimistic value, uncertainty, stability, attractivity, differential systems

DOI: 10.3233/JIFS-210744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3029-3036, 2022

Authors: Liu, Xuwang | Li, Huihui | Qi, Wei

Article Type: Research Article

Abstract: With recent developments in information technology and the extensive promotion of Internet Plus, the use of online centralized procurement by governments and enterprise groups has become progressively more common, and the winning bid evaluation decision making method is particularly important in this context. However, experts might not be completely rational during the process of bid evaluation, which may induce the enhancement or repression of bid scores. To address such behaviors during the process of bid evaluation, an automatic mechanism to identify and correct such tendencies is proposed in this study. Because experts have different preferences for different alternatives, which are …directly reflected in the evaluation of attribute values. Based on selection preference, this paper proposes a selection preference method for solving the subjective weight of attributes. Firstly, the weights of relevant attributes are first determined via the entropy weight method and the selection preference method, and the weights corresponding to groups are determined according to the differences between the scores assigned by experts. Then, a grouped multi-attribute bid evaluation decision making method is proposed based on the selection preference. Finally, an example is used to verify the effectiveness of the method and its superiority over existing methods. Thus, a theoretical basis and a decision support mechanism are provided in this study for centralized procurement departments of governments and enterprises. Further, it also provides guidance for multi-attribute decision making problems with identical grouped features. Show more

Keywords: Centralized procurement, multi-attribute decision making, selection preference, entropy weight

DOI: 10.3233/JIFS-210748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3037-3049, 2022

Authors: Ferrari, Allan Christian Krainski | Silva, Carlos Alexandre Gouvea da | Osinski, Cristiano | Pelacini, Douglas Antonio Firmino | Leandro, Gideon Villar | Coelho, Leandro dos Santos

Article Type: Research Article

Abstract: The Whale Optimization Algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio …of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design. Show more

Keywords: Humpback whale, Metaheuristics, optimization, identification process, Whale Optimization Algorithm

DOI: 10.3233/JIFS-210781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3051-3066, 2022

Authors: Nguyen, Linh Anh

Article Type: Research Article

Abstract: The problem of checking whether a state in a finite fuzzy labeled transition system (FLTS) crisply simulates another is one of the fundamental problems of the theory of FLTSs. This problem is of the same nature as computing the largest crisp simulation between two finite FLTSs. A naive approach to the latter problem is to crisp the given FLTSs and then apply one of the currently known best methods to the obtained crisp labeled transition systems. The complexity of the resulting algorithms is of order O  (l  (m  + n ) n ), where l is the number of fuzzy values …occurring in the specification of the input FLTSs, m is the number of transitions and n is the number of states of the input FLTSs. In the worst case, l can be m  + n and O  (l  (m  + n ) n ) is the same as O  ((m  + n ) 2 n ). In this article, we design an efficient algorithm with the complexity O  ((m  + n ) n ) for computing the largest crisp simulation between two finite FLTSs. This gives a significant improvement. We also adapt our algorithm to computing the largest crisp simulation between two finite fuzzy automata. Show more

Keywords: Fuzzy labeled transition systems, fuzzy automata, simulation, bisimulation

DOI: 10.3233/JIFS-210792

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3067-3078, 2022

Authors: Yin, Longjun | Zhang, Qinghua | Zhao, Fan | Mou, Qiong | Xian, Sidong

Article Type: Research Article

Abstract: In uncertain information processing, new knowledge can be discovered by measuring the proximity between discovered and undiscovered knowledge. Pythagorean Fuzzy Sets (PFSs) is one of the important tools to describe the natural attributes of uncertain information. Therefore, how to appropriately measure the distance between PFSs is an important topic. The earth mover’s distance (EMD) is a real distance metric that can be used to describe the difference between two distribution laws. In this paper, a new distance measure for PFSs based on EMD is proposed. It is a new perspective to measure the distance between PFSs from the perspective of …distribution law. First, a new distance measure namely D EMD is presented and proven to satisfy the distance measurement axiom. Second, an example is given to illustrate the advantages of D EMD compared with other distance measures. Third, the problem statements and solving algorithms of pattern recognition, medical diagnosis and multi-criteria decision making (MCDM) problems are given. Finally, by comparing the application of different methods in pattern recognition, medical diagnosis and MCDM, the effectiveness and practicability of D EMD and algorithms presented in this paper are demonstrated. Show more

Keywords: Pythagorean Fuzzy Sets, Intuitionistic Fuzzy Sets, Pattern recognition, Medicinal diagnosis, Multi-criteria decision making

DOI: 10.3233/JIFS-210800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3079-3092, 2022

Authors: Wang, Zhenggang | Jin, Jin

Article Type: Research Article

Abstract: Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the …initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply. Show more

Keywords: Remote sensing, core data, nebular clustering, parameter optimization, objective function

DOI: 10.3233/JIFS-210802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3093-3106, 2022

Authors: Xi, Dejun | Qin, Yi | Wang, Zhiwen

Article Type: Research Article

Abstract: An efficient visual detection method is explored in this study to address the low accuracy and efficiency of manual detection for irregular gear pitting. The results of gear pitting detection are enhanced by embedding two attention modules into Deeplabv3 + to obtain an improved segmentation model called attention Deeplabv3. The attention mechanism of the proposed model endows the latter with an enhanced ability for feature representation of small and irregular objects and effectively improves the segmentation performance of Deeplabv3. The segmentation ability of attention Deeplabv3+ is verified by comparing its performance with those of other typical segmentation networks using two public …datasets, namely, Cityscapes and Voc2012. The proposed model is subsequently applied to segment gear pitting and tooth surfaces simultaneously, and the pitting area ratio is calculated. Experimental results show that attention Deeplabv3 has higher segmentation performance and measurement accuracy compared with the existing classical models under the same computing speed. Thus, the proposed model is suitable for measuring various gear pittings. Show more

Keywords: Image segmentation, Deeplabv3+, attention mechanism, feature expression, gear pitting

DOI: 10.3233/JIFS-210810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3107-3120, 2022

Authors: Wei, Dongmei | Rong, Yuan | Garg, Harish | Liu, Jun

Article Type: Research Article

Abstract: Teaching quality evaluation (TQE) can not only improve teachers’ teaching skills, but also provide an important reference for school teaching management departments to formulate teaching reform measures and strengthen teaching management. TQE is a process of grading and ranking a given teachers based on the comprehensive consideration of multiple evaluation criteria by expert. The Maclaurin symmetric mean (MSM), as a powerful aggregation function, can capture the correlation among multiple input data more efficient. Although multitude weighted MSM operators have been developed to handle the Pythagorean fuzzy decision issues, these above operators do not possess the idempotency and reducibility during the …procedure of information fusion. To conquer these defects, we present the Pythagorean fuzzy reducible weighted MSM (PFRWMSM) operator and Pythagorean fuzzy reducible weighted geometric MSM (PFRWGMSM) operator to fuse Pythagorean fuzzy assessment information. Meanwhile, several worthwhile properties and especial cases of the developed operators are explored at length. Afterwards, we develop a novel Pythagorean fuzzy entropy based upon knowledge measure to ascertain the weights of attribute. Furthermore, an extended weighted aggregated sum product assessment (WASPAS) method is developed by combining the PFRWMSM operator, PFRWGMSM operator and entropy to settle the decision problems of unknown weight information. The efficiency of the proffered method is demonstrated by a teaching quality evaluation issue, as well as the discussion of sensitivity analysis for decision outcomes. Consequently, a comparative study of the presented method with the extant Pythagorean fuzzy approaches is conducted to display the superiority of the propounded approach. Show more

Keywords: Teaching quality evaluation, Pythagorean fuzzy set, information fusion, Reducible weighted MSM, WASPAS

DOI: 10.3233/JIFS-210821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3121-3152, 2022

Authors: Xu, Kai | Luo, Xilin | Pang, Xinyu

Article Type: Research Article

Abstract: Based on the nonlinearity of energy consumption systems and the influence of multiple factors, this paper presents a nonlinear multivariable grey prediction model with parameter optimization and estimates the parameters and the approximate time response function of the model. Next, a genetic algorithm is applied to optimize the nonlinear terms of the novel model to seek the optimal parameters, and the modelling steps are outlined. Then, to assess the effectiveness of the novel model, this paper adopts Chinese oil, gas, coal and clean energy as research objects, and three classical grey forecasting models and one time series method are chosen …for comparison. The results indicate that the new model attains a high simulation and prediction accuracy, basically higher than that of the three grey prediction models and the time series method. Show more

Keywords: Grey prediction model, energy consumption, simulated annealing optimization, genetic algorithm

DOI: 10.3233/JIFS-210822

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3153-3168, 2022

Authors: Riaz, Muhammad | Riaz, Mishal | Jamil, Nimra | Zararsiz, Zarife

Article Type: Research Article

Abstract: Pharmaceutical logistics are primarily concerned with handling transportation and supply chain management of numerous complex goods most of which need particular requirements for their logistical care. To find the high level of specialization, suppliers of pharmaceutical logistics must be selected under a mathematical model that can treat vague and uncertain real-life circumstances. The notion of bipolarity is a key factor to address such uncertainties. A bipolar fuzzy soft set (BFSS) is a strong mathematical tool to cope with uncertainty and unreliability in various real-life problems including logistics and supply chain management. In this paper, we introduce new similarity measures (SMs) …based on certain properties of bipolar fuzzy soft sets (BFSSs). The proposed SMs are the extensions of Frobenius inner product, cosine similarity measure, and weighted similarity measure for BFSSs. The proposed SMs are also illustrated with respective numerical examples. An innovative multi-attribute decision-making algorithm (MADM) and its flow chart are being developed for pharmaceutical logistics and supply chain management in COVID-19. Furthermore, the application of the suggested MADM method is presented for the selection of the best pharmaceutical logistic company and a comparative analysis of the suggested SMs with some of the existing SMs is also demonstrated. Show more

Keywords: Bipolar fuzzy soft sets, similarity measures, pharmaceutical logistics, multi-attribute decision-making

DOI: 10.3233/JIFS-210873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3169-3188, 2022

Authors: Sung, Tien-Wen | Xu, Yuntao | Hu, Xiaohui | Lee, Chao-Yang | Fang, Qingjun

Article Type: Research Article

Abstract: With the construction of smart grids, smart meters are gradually being installed in every house. In order to transfer the user data collected by smart meters to the control center, it is necessary to transfer the data to the data aggregation point (DAP) before being transmitted to the control center. The numbers and locations of DAPs affect the communication quality and cost of the smart meter neighborhood network, and because smart meters rely on wireless technology to transmit data, their transmission range is limited. Thus, suburban and rural areas require a large number of DAP installation needs, and it is …very important to reduce their numbers. For this problem, this study proposes a grid-based relay DAP placement scheme and presents the corresponding algorithms to reduce the number of DAPs and to avoid the large impact of relay DAP locations on communication quality for the two cases of whether or not the number of relay DAPs is limited. This paper used random smart meter coordinates for testing, and the test results verify that the proposed solution can in fact significantly reduce the number of DAPs and avoid the large impact of relay DAP locations on communication quality. Show more

Keywords: Smart grid, data aggregation point, advanced metering infrastructure, smart meter network

DOI: 10.3233/JIFS-210881

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3189-3201, 2022

Authors: Alqurashi, Fahad A. | Alsolami, F. | Abdel-Khalek, S. | Sayed Ali, Elmustafa | Saeed, Rashid A.

Article Type: Research Article

Abstract: Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. …Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends. Show more

Keywords: IoUAV, machine learning, deep learning, QoE, network optimization, smart unmanned systems

DOI: 10.3233/JIFS-211009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3203-3226, 2022

Authors: Xue, Feng | Liu, Yongbo | Ma, Xiaochen | Pathak, Bharat | Liang, Peng

Article Type: Research Article

Abstract: To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search …ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability. Show more

Keywords: Grey wolf optimization algorithm, nonlinear convergence factor, inertial step size, dynamic weight, K-harmonic means clustering, hybrid clustering

DOI: 10.3233/JIFS-211034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3227-3240, 2022

Authors: Seethalakshmi, K. | Valli, S. | Veeramakali, T. | Kanimozhi, K.V. | Hemalatha, S. | Sambath, M.

Article Type: Research Article

Abstract: Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high …accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset. Show more

Keywords: Fuzzy anisotropy diffusion, edge detection, contrast enhancement, CNN (ResNet), feature extraction, ANFIS deep learning

DOI: 10.3233/JIFS-211114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3241-3250, 2022

Authors: Xuejian, Zhang | Xiaobing, Hu | Hang, Li

Article Type: Research Article

Abstract: To ensure the cutting speed during the cutting operation, this paper proposes a groove cutting speed inference planning system that relies on production experience and set parameters and is based on machine vision and a two-level fuzzy neural hybrid network. The overall structure of the inference system is designed, including the mechanical body, vision system, and fuzzy neural hybrid network. The contour information of the part is obtained using industrial cameras and digital image processing systems. The cutting speed of the trajectory segment is inferred based on the related processing parameters and the secondary fuzzy neural hybrid network. Finally, all …of the processing parameters are transmitted to the PLC, so that the robot can work according to the predetermined displacement and speed. Simulations verify that the speed inference planning system offers certain advantages compared to the traditional one. The appearance of the speed inference planning realises independent design and planning of the cutting speed, and further ensures the unity of the cutting quality and cutting speed. This proposed method provides a new direction for the development and transformation of machining processes that rely on manual experience and in which expert systems cannot be used. Show more

Keywords: groove cutting speed, machine vision, fuzzy neural network, MATLAB simulation

DOI: 10.3233/JIFS-211116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3251-3264, 2022

Authors: Gunapriya, D. | Muniraj, C. | Lakshmi, K.

Article Type: Research Article

Abstract: The detection as well as analysis of faults in Induction Motor (IM) is prominent in the industrial process in recent decades, since it has been a demanding issue in industries to confirm the safe and reliable operations of IM. Though the electrical faults, mechanical faults and environmental faults cause damages in IM, as per Electric Power Research Institute (EPRI) statistical studies, the faults due to (i) rotor mass unbalance and (ii) rotor shaft bending substantially contribute 8-9% of the total motor fault. This present research work focuses on the issue of detecting and analysing the faults by studying the current …and vibration data obtained from the three-phase squirrel cage IM under healthy and faulty conditions using the experimental workbench. It also depicts the development of a fault detection model for IM which comprises the integrated approach of Principal Component Analysis (PCA) and Fuzzy Interference System (FIS) and two level decision fuzzy measures. Besides, fuzzy integral data fusion technique has been used in this work for the improvement of diagnosing accuracy. The data acquired from the workbench system are first investigated through the PCA to extricate the appropriate features that provide the major information of collected data without reducing its dimensions. The projected data space using the principal components is non-deterministic for further synthesis process of fault classification. Hence, to classify the faults in IM, the obtained feature vectors from PCA are fed into FIS as an input and the classification performance is compared finally. The work experiment has been carried out under the healthy and different faulty conditions of motor and the proposed integrated approach is executed by using MATLAB. Show more

Keywords: Fuzzy logic, fuzzy integral, fuzzy measure, induction motor faults, principal component analysis, current and vibration signals

DOI: 10.3233/JIFS-211124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3265-3283, 2022

Authors: Medeiros, Alessandro | Sartori, Andreza | Stefenon, Stéfano Frizzo | Meyer, Luiz Henrique | Nied, Ademir

Article Type: Research Article

Abstract: Contamination in insulators results in an increase in surface conductivity. With higher surface conductivity, insulators are more vulnerable to discharges that can damage them, thus reducing the reliability of the electrical system. One of the indications that the insulator is losing its insulating properties is its increase in leakage current. By varying the leakage current over time, it is possible to determine whether the insulator will develop an irreversible failure. In this way, by predicting the increase in leakage current, it is possible to carry out maintenance to avoid system failures. For forecasting time series, there are many models that …have been studied and the definition of which model is suitable for evaluation depends on the characteristics of the data associated with the analysis. Thus, this work aims to identify the most suitable model to predict the increase in leakage current in relation to the time the insulator is outdoors, exposed to environmental variations using the same database to compare the methods. In this paper, the models based on linear regression, support vector regression (SVR), multilayer Perceptron (MLP), deep neural network (DNN), and recurrent neural network (RNN) will be analyzed comparatively. The best accuracy results for prediction were found using the RNN models, resulting in an accuracy of up to 97.25%. Show more

Keywords: Failure prediction, time series forecasting, artificial neural network, insulators

DOI: 10.3233/JIFS-211126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3285-3298, 2022

Authors: Padmapriya, V. | Kaliyappan, M.

Article Type: Research Article

Abstract: In this paper, we develop a mathematical model with a Caputo fractional derivative under fuzzy sense for the prediction of COVID-19. We present numerical results of the mathematical model for COVID-19 of most three infected countries such as the USA, India and Italy. Using the proposed model, we estimate predicting future outbreaks, the effectiveness of preventive measures and potential control strategies of the infection. We provide a comparative study of the proposed model with Ahmadian’s fuzzy fractional mathematical model. The results demonstrate that our proposed fuzzy fractional model gives a nearer forecast to the actual data. The present study can …confirm the efficiency and applicability of the fractional derivative under uncertainty conditions to mathematical epidemiology. Show more

Keywords: Fuzzy triangular number, fuzzy fractional derivative, Caputo derivative, COVID-19, Mathematical model

DOI: 10.3233/JIFS-211173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3299-3321, 2022

Authors: Yang, Songyue | Yu, Guizhen | Meng, Zhijun | Wang, Zhangyu | Li, Han

Article Type: Research Article

Abstract: In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to …ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%. Show more

Keywords: UAV, obstacle avoidance, DQN, overestimation, convergence rate

DOI: 10.3233/JIFS-211192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3323-3335, 2022

Authors: Zhu, Wuqiang | Lu, Yang | Zhang, Yongliang | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: End-to-end deep learning has gained considerable interests in autonomous driving vehicles. End-to-end autonomous driving uses the deep convolutional neural network to establish input-to-output mapping. However, existing end-to-end driving models only predict steering angle with front-facing camera data and poorly extract spatial-temporal information. Based on deep learning and attention mechanism, we propose an end-to-end driving model which combines the multi-stream attention module with the multi-stream network. As a multimodal multitask model, the proposed end-to-end driving model not only fully extracts spatial-temporal information from multimodality, but also adopts the multitask learning method with hard parameter sharing to predict the steering angle and …speed. Furthermore, the proposed multi-stream attention module predicts the attention weights of streams based on the multimodal feature fusion, which encourages the proposed end-to-end driving model to pay attention to streams that positively impact the prediction result. We demonstrate the efficiency of the proposed driving model on the public Udacity dataset compared to existing models. Experimental results show that the proposed driving model has better performances than other existing methods. Show more

Keywords: End-to-end autonomous driving, attention mechanism, multimodal, multitask

DOI: 10.3233/JIFS-211206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3337-3348, 2022

Authors: Işık, Gürkan | Kaya, İhsan

Article Type: Research Article

Abstract: Although traditional acceptance sampling plans (ASPs) need certain mass quality characteristics, it is not easy to define them as crisp value in some real case problems. The fuzzy set theory (FST) is one of the popular techniques to model uncertainties of the process and therefore fuzzy ASPs have been offered in the literature. Fuzzy set extensions have been proposed recently for better modeling of the uncertainties having different sources and characteristics. One of these extensions named neutrosophic sets (NSs) can be used to increase the sensitiveness and flexibility of ASPs. The ASPs based on NSs can give ability to classify …the items as defective, non-defective and indeterminate. Since the operator can become indecisive for slightly defective items, these plans can provide a good representation of human evaluations under uncertainty. In this study, single and double ASPs are designed based on NSs by using binomial and poisson distributions that are also re-analyzed based on NSs. For this aim, some characteristics functions of ASPs such as probability of accepting a lot (P a ), average outgoing quality (AOQ ), average total inspection (ATI ) and average sample number (ASN ) have also been analyzed based on NSs. Numerical examples are presented to analyze the proposed plans. Show more

Keywords: Acceptance sampling plans, fuzzy sets, neutrosophic sets, neutrosophic poisson distribution

DOI: 10.3233/JIFS-211232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3349-3366, 2022

Authors: Khalil, Ahmed Mostafa

Article Type: Research Article

Abstract: In this article, we will define the new notions (e.g., semi-θ-neighbor-hood system of point, semi-θ-closure (interior) of a set and semi-θ-closed (open) set) based on fuzzy logic (i.e., fuzzifying topology). Then, we will explain the interesting properties of above five notions in detail. Several basic results (for instance, Definition 2.3, Theorem 2.5 (iii), (v) and (vi), Theorem 2.10, Theorem 2.14 and Theorem 4.6) in classical topology are generalized to the fuzzy case based on Łukasiewicz logic. In addition to, we will show that every fuzzifying semi-θ-closed set is fuzzifying semi-closed set (by Theorem 2.5 (vi)). Further, we will study the …notion of fuzzifying semi-θ-derived set and fuzzifying semi-θ-boundary set, and discuss several of their fundamental basic relations and properties. Also, we will present a new type of fuzzifying strongly semi-θ-continuous mapping between two fuzzifying topological spaces. Finally, several characterizations of fuzzifying strongly semi-θ-continuous mapping, fuzzifying strongly semi-θ-irresolute mapping, and fuzzifying weakly semi-θ-irresolute mapping along with different conditions for their existence are obtained. Show more

Keywords: Fuzzy logic, fuzzifying topology, fuzzifying semi-θ-closure of a set, fuzzifying semi-θ-closed sets, fuzzifying strongly semi-θ-continuous mapping

DOI: 10.3233/JIFS-211301

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3367-3379, 2022

Authors: Gong, Zengtai | Xiao, Zhiyong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-211306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3381-3391, 2022

Authors: Cai, Qian | Xiong, Xingliang | Gong, Weiqiang | Wang, Haixian

Article Type: Research Article

Abstract: BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-class classification. METHOD: To complete the multi-class classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metric. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding …is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-class classification of action intention understanding brain signals. Show more

Keywords: Action intention understanding, EEG, classification, feature extraction, graph metric

DOI: 10.3233/JIFS-211333

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3393-3403, 2022

Authors: Jiang, Zhiwei | Wei, Guiwu | Guo, Yanfeng

Article Type: Research Article

Abstract: In the garment manufacturing industry, purchasing management is an important link. The materials of making clothes often need high cost. In addition, customers put forward a request in the quality of clothes. Thus, choosing an optimal supplier is an essential part of job. Reaching cooperation with an optimal supplier not only can help garment manufacturer improve the quality of clothes but also is benefit to reduce the cost of producing. Most importantly, it can improve the competitiveness of manufacture enterprises. So, it is important for managers to find an optimal supplier and make a cooperation with it. In this paper, …we analysis an issue about choosing an optimal supplier during four different suppliers. With analyzing this problem, we can introduce an extended method under picture fuzzy environment to evaluate and choose an optimal supplier. In this article, we describe some basic knowledges about picture fuzzy sets (PFSs) and picture fuzzy numbers (PFNs). Then, we introduce the extension of MABAC method which is on the basis of prospect theory (PT) with picture fuzzy numbers (PF-PT-MABAC) and utilize the PF-PT-MABAC model to evaluate different suppliers to choose an optimal supplier. Finally, we compare the result of PF-PT-MABAC with the result of traditional MABAC, PFWG operators and traditional TODIM method to test the efficiency of PF-PT-MABAC model. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), MABAC method, prospect theory (PT), suppliers selection

DOI: 10.3233/JIFS-211359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3405-3415, 2022

Authors: Kashif, Agha | Rashid, Tabasam | Noor, Bibi | Sindhu, Muhammad Sarwar

Article Type: Research Article

Abstract: Motivated by intuitionistic fuzzy sets and soft sets, a novel concept of lattice ordered interval-valued intuitionistic fuzzy soft sets (LOIVIFSSs) is introduced in this article. Operational rules like union, intersection, complement and some properties of LOIVIFSSs are demonstrated with examples. In this regard, an algorithm is developed to solve the multiple criteria decision-making (MCDM) problems based on LOIVIFSSs. Further, a benchmark problem concerning medical diagnosis have been investigated and a comparative analysis with existing technique is furnished to strengthen our approach.

Keywords: Fuzzy sets, intuitionistic fuzzy sets, soft sets, lattice

DOI: 10.3233/JIFS-211376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3417-3430, 2022

Authors: Jiang, Tianhua | Zhu, Huiqi | Gu, Jiuchun | Liu, Lu | Song, Haicao

Article Type: Research Article

Abstract: This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the …search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP. Show more

Keywords: Dual-resource constraint, energy-saving scheduling, flexible job shop, discrete animal migration optimization

DOI: 10.3233/JIFS-211399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3431-3444, 2022

Authors: Joshi, Pallavi | Raghuvanshi, Ajay Singh

Article Type: Research Article

Abstract: The abrupt changes in the sensor measurements indicating the occurrence of an event are the major factors in some monitoring applications of IoT networks. The prediction-based approach for data aggregation in wireless sensor networks plays a significant role in detecting such events. This paper introduces a prediction-based aggregation model for sensor selection named the Grey prediction model and the Kalman filter-based data aggregation model with rank-based mutual information (GMKFDA-MI) that has a dual synchronization mechanism for aggregating the data and selecting the nodes based on prediction and cumulative error thresholds. Furthermore, the nodes after deployment are clustered using K-medoids clustering …along with the Salp swarm optimization algorithm to obtain an optimized aggregator position concerning the base station. An efficient clustering promises energy efficiency and better connectivity. The experiments are accomplished on real-time datasets of air pollution monitoring applications and the results for the proposed method are compared with other similar state-of-the-art techniques. The proposed method promises high prediction accuracy, low energy consumption and enhances the throughput of the network. The energy-saving is recorded to be more than 10 to 30% for the proposed model when compared with other similar approaches. Also, the proposed method achieves 97.8% accuracy as compared to other methods. The method proves its best working efficiency in the applications like event reporting, target detection, and event monitoring. Show more

Keywords: IoT, wireless sensor networks, data aggregation, grey model, kalman filter, mutual information, K-medoids clustering, salp swarm optimization

DOI: 10.3233/JIFS-211436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3445-3464, 2022

Authors: Ghafour, Karzan Mahdi

Article Type: Research Article

Abstract: Demand forecasting is a fundamental element in industrial problems. Forecasts are crucial for accurately estimating intermittent demand to establish inventory measurements. The demand estimation by the Croston method gives less accurate values, which increases the standard deviation value. This increase indicates that the forecasted method is an inappropriate method of intermittent demand data because of the zero values. However, real data were adopted in an industrial sector for three years with constant lead-time. Furthermore, an integration of Bernoulli distribution and geometric distribution has been done to establish the new formulation, then extracting the mean equation and the standard deviation equation …of intermittent demand during lead-time. Relying on it, the optimal quantity of safety stock and reorder levels have been obtained. Furthermore, the proposed modified forecasting method was evaluated based on the criteria of CV and the results that obtained gives a less ratio dispersion of data thus accurate results. These procedures are very important to the industrial sectors in drawing future inventory policies. Show more

Keywords: Intermittent demand, forecasting, croston’s method, reorder level and safety stock

DOI: 10.3233/JIFS-211454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3465-3475, 2022

Authors: Prajna, Yellamelli | Nath, Malaya Kumar

Article Type: Research Article

Abstract: Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the …classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation. Show more

Keywords: Fundus image, U-Net, MSRI, accuracy, ROC curve

DOI: 10.3233/JIFS-211479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3477-3489, 2022

Authors: Farooq, Muhammad | Qamar-uz-zaman, | Ijaz, Muhammad

Article Type: Research Article

Abstract: The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of …FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. Show more

Keywords: FEW, MLE, statistical properties, applications, and simulation

DOI: 10.3233/JIFS-211519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3491-3499, 2022

Authors: Tripathy, Santosh Kumar | Sudhamsh, Repala | Srivastava, Subodh | Srivastava, Rajeev

Article Type: Research Article

Abstract: Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed …them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches. Show more

Keywords: Atrous (Dilated)-CNN, multiscale spatial-temporal features, dimension reduction, PCA, OC-SVM

DOI: 10.3233/JIFS-211556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3501-3516, 2022

Authors: Khan, Izaz Ullah | Aftab, Muhammad

Article Type: Research Article

Abstract: This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan …comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable. Show more

Keywords: Fuzzy Linear Programming (FLP), fuzzy mathematical programming, dynamic programming, education, fuzzy sets and fuzzy modeling, resource allocation

DOI: 10.3233/JIFS-211577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3517-3535, 2022

Authors: Sun, Quan | Yu, Xianghai | Li, Hongsheng | Peng, Fei | Sun, Guodong

Article Type: Research Article

Abstract: With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is …converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks. Show more

Keywords: Continuous wavelet transform, convolution neural network, fault detection, power electronic converter

DOI: 10.3233/JIFS-211632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3537-3549, 2022

Authors: Jin, LeSheng | Mesiar, Radko | Yager, Ronald | Kaya, Sema Kayapinar

Article Type: Research Article

Abstract: The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is …presented. Show more

Keywords: Aggregation operators, basic uncertain information, decision making, interval basic uncertain information, preference aggregation

DOI: 10.3233/JIFS-211635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3551-3558, 2022

Authors: Li, Feng

Article Type: Research Article

Abstract: Many experts and scholars focus on the Maclaurin symmetric mean (MSM) equation, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy data intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the security evaluation of Wireless Sensor Network (WSN). And the WSN security evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. In this paper, we expand the generalized Maclaurin symmetric mean (GMSM) equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy generalized MSM (FNIFGMSM) …equation and fuzzy number intuitionistic fuzzy weighted generalized MSM (FNIFWGMSM) equation in this study. A few MADM tools are developed with FNIFWGMSM equation. Finally, taking WSN security evaluation as an example, this paper illustrates effectiveness of the depicted approach. Moreover, by comparing and analyzing the existing methods, the effectiveness and superiority of the FNIFWGMSM method are further certified. Show more

Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy sets (FNIFSs), fuzzy number intuitionistic fuzzy GMSM (FNIFGMSM) equation, fuzzy number intuitionistic fuzzy weighted GMSM (FNIFWGMSM) equation, Wireless Sensor Network (WSN)

DOI: 10.3233/JIFS-211731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3559-3573, 2022

Authors: Varatharaj, Nagaraj | Ramalingam, Sumithira Thulasimani

Article Type: Research Article

Abstract: Most revolutionary applications extending far beyond smartphones and high configured mobile device use to the future generation wireless networks’ are high potential capabilities in recent days. One of the advanced wireless networks and mobile technology is 5G, where it provides high speed, better reliability, and amended capacity. 5 G offers complete coverage, which is accommodates any IoT device, connectivity, and intelligent edge algorithms. So that 5 G has a high demand in a wide range of commercial applications. Ambrosus is a commercial company that integrates block-chain security, IoT network, and supply chain management for medical and food enterprises. This paper proposed a …novel framework that integrates 5 G technology, Machine Learning (ML) algorithms, and block-chain security. The main idea of this work is to incorporate the 5 G technology into Machine learning architectures for the Ambrosus application. 5 G technology provides continuous connection among the network user/nodes, where choosing the right user, base station, and the controller is obtained by using for ML architecture. The proposed framework comprises 5 G technology incorporate, a novel network orchestration, Radio Access Network, and a centralized distributor, and a radio unit layer. The radio unit layer is used for integrating all the components of the framework. The ML algorithm is evaluated the dynamic condition of the base station, like as IoT nodes, Ambrosus users, channels, and the route to enhance the efficiency of the communication. The performance of the proposed framework is evaluated in terms of prediction by simulating the model in MATLAB software. From the performance comparison, it is noticed that the proposed unified architecture obtained 98.6% of accuracy which is higher than the accuracy of the existing decision tree algorithm 97.1%. Show more

Keywords: 5 G Technology, internet of things, block-chain security, wireless communication, machine learning models

DOI: 10.3233/JIFS-211745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3575-3590, 2022

Authors: Garg, Harish | Alodhaibi, Sultan S. | Khalifa, Hamiden Abd El-Wahed

Article Type: Research Article

Abstract: Rough set theory, introduced by Pawlak in 1981, is one of the important theories to express the vagueness not by means of membership but employing a boundary region of a set, i.e., an object is approximately determined based on some knowledge. In our real-life, there exists several parameters which impact simultaneously on each other and hence dealing with such different parameters and their conflictness create a multi-objective nonlinear programming problem (MONLPP). The objective of the paper is to deal with a MONLPP with rough parameters in the constraint set. The considered MONLPP with rough parameters are converted into the two-single …objective problems namely, lower and upper approximate problems by using the weighted averaging and the ɛ - constraints methods and hence discussed their efficient solutions. The Karush-Kuhn-Tucker’s optimality conditions are applied to solve these two lower and upper approximate problems. In addition, the rough weights and the rough parameter ɛ are determined by the lower and upper the approximations corresponding each efficient solution. Finally, two numerical examples are considered to demonstrate the stated approach and discuss their advantages over the existing ones. Show more

Keywords: Multiobjective nonlinear programming, rough set, lower approximation programming problem, upper approximation programming problem, weighting method, ɛ- constraints method, parametric analysis

DOI: 10.3233/JIFS-211747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3591-3604, 2022