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