Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Kalaivani, K. | Kshirsagarr, Pravin R. | Sirisha Devi, J. | Bandela, Surekha Reddy | Colak, Ilhami | Nageswara Rao, J. | Rajaram, A.

Article Type: Research Article

Abstract: The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much …training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals. Show more

Keywords: Electrocardiography (ECG), electroencephalography (EEG), electromyographic (EMG), deeplearning techniques, prediction, heart attack, emotion recognition, neuromuscular disease, R-CNN

DOI: 10.3233/JIFS-230399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9769-9782, 2023

Authors: Kianifar, Mohammad Ali | Motallebi, Hassan | Bardsiri, Vahid Khatibi

Article Type: Research Article

Abstract: Dynamic Classifier Selection (DCS) techniques aim to select the most competent classifiers from an ensemble per test sample. For each test sample, only a subset of the most competent classifiers is used to estimate its target value. The performance of the DCS highly depends on how we define the local region of competence, which is a local region in the feature space around the test sample. In this paper, we propose a new definition of region of competence based on a new proximity measure. We exploit the observed similarities between traffic profiles at different links, days and hours to obtain …similarities between different values. Furthermore, long-term traffic pattern prediction is a complex problem and most of the traffic prediction literature are based on time-series and regression approaches and their prediction time is limited to next few hours or days. We tackle the long-term traffic pattern prediction as a classification of discretized traffic indicators to improve the accuracy of urban traffic pattern forecasting of next weeks by using DCS. We also employ two different link clustering methods, for grouping traffic links. For each cluster, we train a dynamic classifier system for predicting the traffic variables (flow, speed and journey time). Our results on strategic road network data shows that the proposed method outperforms the existing ensemble and baseline models in long-term traffic prediction. Show more

Keywords: Long-term traffic prediction, monthly SRN data set, traffic link clustering, dynamic classifier selection, region of competence

DOI: 10.3233/JIFS-220759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9783-9797, 2023

Authors: Ramesh, Pinapilli | Yadaiah, Narri

Article Type: Research Article

Abstract: This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich’s criteria. The proposed approach is validated on the benchmark system such …as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600. Show more

Keywords: Type-2 fuzzy logic controller, brain emotional learning, adaptive memberships functions

DOI: 10.3233/JIFS-222143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9799-9820, 2023

Authors: Wang, Yuan | Yu, Xiaobing | Wang, Xuming

Article Type: Research Article

Abstract: Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy …of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train. Show more

Keywords: Multi-verse optimizer, queuing searching algorithm, metropolis rule

DOI: 10.3233/JIFS-223369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9821-9845, 2023

Authors: Zhu, Xingchen | Wu, Xiaohong | Wu, Bin | Zhou, Haoxiang

Article Type: Research Article

Abstract: The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are …run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties. Show more

Keywords: Fuzzy clustering, FCM, PCM, Euclidean distance, distance function

DOI: 10.3233/JIFS-223576

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9847-9862, 2023

Authors: Bolourchi, Pouya | Ghasemzadeh, Aman

Article Type: Research Article

Abstract: In bioinformatics studies, many modeling tasks are characterized by high dimensionality, leading to the widespread use of feature selection techniques to reduce dimensionality. There are a multitude of feature selection techniques that have been proposed in the literature, each relying on a single measurement method to select candidate features. This has an impact on the classification performance. To address this issue, we propose a majority voting method that uses five different feature ranking techniques: entropy score, Pearson’s correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and t -test. By using a majority voting approach, only the features that appear in …all five ranking methods are selected. This selection process has three key advantages over traditional techniques. Firstly, it is independent of any particular feature ranking method. Secondly, the feature space dimension is significantly reduced compared to other ranking methods. Finally, the performance is improved as the most discriminatory and informative features are selected via the majority voting process. The performance of the proposed method was evaluated using an SVM, and the results were assessed using accuracy, sensitivity, specificity, and AUC on various biomedical datasets. The results demonstrate the superior effectiveness of the proposed method compared to state-of-the-art methods in the literature. Show more

Keywords: Classification, correlation coefficient, feature selection, feature ranking, gene data, majority

DOI: 10.3233/JIFS-224029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9863-9877, 2023

Authors: Mecheri, Karima | Klai, Sihem | Souici-Meslati, Labiba

Article Type: Research Article

Abstract: Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web …services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques. Show more

Keywords: Deep learning, recommendation systems, web services, mashup, quality of service, performance evaluation metrics

DOI: 10.3233/JIFS-224565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9879-9899, 2023

Authors: Qu, Ying | Chen, Hong

Article Type: Research Article

Abstract: During an emergency, the negative Internet public opinion in colleges and universities, especially the negative endogenous public opinion, will have a serious impact on the reputation of colleges and universities. It is of great significance to find out the negative influencing factors of endogenous public opinion and explore the mechanism of public opinion dissemination for resolving the crisis of public opinion in universities. The existing research does not distinguish the endogenous Internet public opinion in colleges and universities from the general Internet public opinion in colleges and universities, and the SIR model adopted fails to fully reflect the difference between …students and other dissemination subjects of endogenous public opinion in campus. In addition, various research methods and models currently used focus on the static expression of dissemination results, and the explanation of results is insufficient. The reason is that they do not well express the dynamic interaction mechanism between influencing factors and the dynamic conversion rate between roles. In this study, based on the improved infectious disease model and system dynamics theory, AnyLogic software is used to simulate the improved SNIDR model of infectious disease, to analyze the sensitivity of school supervision, school intervention, school response time and information transparency and to study the dynamic conversion rate between different roles. The SNIDR model effectively simulates the process of endogenous public opinion dissemination in colleges and universities after emergencies. The results show that, what has the greatest impact on the dissemination of public opinion is the school’s supervision and intervention efforts, which can suppress the dissemination from the source. Information transparency is an auxiliary variable and cannot function independently. During the dissemination period, the timelier the school responds, the faster the spreaders will drop to zero, and the better it will be to control the secondary dissemination of public opinion. Show more

Keywords: SNIDR model, governance strategies, internet public opinion, dissemination mechanism, emergency

DOI: 10.3233/JIFS-230002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9901-9917, 2023

Authors: Liu, Biyu | Chen, Ting | Yang, Haidong | Segerstedt, Anders

Article Type: Research Article

Abstract: Suppliers significantly affect the effectiveness of sustainable supply chain management. Hence, it is extremely important to evaluate and select suppliers scientifically and objectively. Based on the theory of triple bottom line (economic, social, and environmental dimension) and a balanced scorecard, a measureable supplier evaluation framework in a sustainable supply chain is first formulated. Second, to reduce the defects of the single weight method, the subjective and objective weights of evaluation indicators are determined by combining the fuzzy best-worst method (BWM) and the entropy method, and then the combination weights are obtained through linear weighting. Third, the grey relational technique for …order performance by similarity to ideal solution (TOPSIS) method is further adopted to evaluate and rank the suppliers. Finally, a case study illustrates and demonstrates the availability of the proposed supplier evaluation index system and evaluation method. Subsequently, some suggestions are proposed according to the results. Show more

Keywords: Sustainable supply chain management, supplier evaluation, the fuzzy BWM, grey relational, TOPSIS

DOI: 10.3233/JIFS-212996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9919-9932, 2023

Authors: Gao, Wang | Ni, Mingyuan | Deng, Hongtao | Zhu, Xun | Zeng, Peng | Hu, Xi

Article Type: Research Article

Abstract:  As people increasingly use social media to read news, fake news has become a major problem for the public and government. One of the main challenges in fake news detection is how to identify them in the early stage of propagation. Another challenge is that detection model training requires large amounts of labeled data, which are often unavailable or expensive to acquire. To address these challenges, we propose a novel Fake News Detection model based on Prompt Tuning (FNDPT). FNDPT first designs a prompt-based template for early fake news detection. This mechanism incorporates contextual information into textual content and extracts …relevant knowledge from pre-trained language models. Furthermore, our model utilizes prompt-based tuning to enhance the performance in a few-shot setting. Experimental results on two real-world datasets verify the effectiveness of FNDPT. Show more

Keywords: Fake news detection, few-shot, prompt-based tuning, pre-trained language model

DOI: 10.3233/JIFS-221647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9933-9942, 2023