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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Yu, Ming | Jia, Jingli | Xue, Cuihong | Yan, Gang | Guo, Yingchun | Liu, Yuehao
Article Type: Research Article
Abstract: Sign language is the primary way of communication between hard-of-hearing and hearing people. Sign language recognition helps promote the better integration of deaf and hard-of-hearing people into society. We reviewed 95 types of research on sign language recognition technology from 1993 to 2021, analyzing and comparing algorithms from three aspects of gesture, isolated word, and continuous sentence recognition, elaborating the evolution of sign language acquisition equipment and we summarized the datasets of sign language recognition research and evaluation criteria. Finally, the main technology trends are discussed, and future challenges are analyzed.
Keywords: Sign language recognition, convolutional neural network, encoder-decoder, dataset
DOI: 10.3233/JIFS-210050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3879-3898, 2022
Authors: Tran, Duc Quynh | Nguyen, Xuan Thao | Nguyen, Doan Dong | Nguyen, Quang Thuan
Article Type: Research Article
Abstract: In this paper, we propose a new formula for the entropy based on similarity measures of intuitionistic fuzzy sets (IFS). The contribution of this work is the proof that the new formula satisfies all the conditions of entropy. The experimentation on some examples shows that the new entropy is useful. Besides, we use the new entropy and similarity measures to design an algorithm for ranking assets in stock markets. The numerical results on 5 benchmark data sets were reported. It points out that the entropy and the similarity measures of IFS may provide an alternative tool for solving portfolio selection …problems. Show more
Keywords: Intuitionistic fuzzy set, similarity measures, stock markets, assets ranking, entropy
DOI: 10.3233/JIFS-211563
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3899-3909, 2022
Authors: Peng, Jiangang | Cai, Ya | Xia, Guang | Hao, Ming
Article Type: Research Article
Abstract: This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and …the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach. Show more
Keywords: Three-way decision, interval type-2 fuzzy set, linguistic term sets, multi-criteria group decision-making
DOI: 10.3233/JIFS-213236
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3911-3932, 2022
Authors: Vanitha, K. | Satyanarayana, D. | Giri Prasad, M.N.
Article Type: Research Article
Abstract: This paper addresses a novel neuro-fuzzy-based approach to set the weighted linking strength of parameter - adaptive reduced pulse coupled neural networks. In reduced PCNN based medical image fusion algorithms, it is quite essential to evaluate the prominence of each pixel in an image. The fusion performance in turn depends on the linking factor, internal activity. Thus, we need to set these values of reduced PCNN in a more adaptive manner with fewer complications and uncertainties. For this, the weighted linking strength i.e., lambda of the reduced PCNN neurons is attentively set by a fuzzy-based approach. Here, lambda of neurons …is represented as fuzzy membership values using the activity level measures such as local information entropy and energy. Finally, a new model called-Fuzzy adaptive reduced pulse coupled neural networks is developed by reducing the number of parameters and fuzzy adaptive settings of them. This leads to a very less complicated network and more computational efficacy, which is a prominent part of health care requirements. The proposed scheme is free from the shortcomings such as loss of boundaries, structural details, unwanted artifacts, degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques. Show more
Keywords: Magnetic resonance imaging, computed tomography, SPECT, discrete wavelet transform
DOI: 10.3233/JIFS-213416
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3933-3946, 2022
Authors: Baffour, Adu Asare | Qin, Zhen | Zhu, Guobin | Ding, Yi | Qin, Zhiguang
Article Type: Research Article
Abstract: Recognizing facial expressions rely on facial parts’ movement (action units) such as eyes, mouth, and nose. Existing methods utilize complex subnetworks to learn part-based facial features or train neural networks with an extensively perturbed dataset. Different from existing methods, we propose a trainable end-to-end convolutional neural network for facial expression recognition. First, we propose a Local Prediction Penalty to stimulate facial expression recognition research with no part-based learning. It is a technique to punish the feature extractor’s local predictive power to coerce it to learn coarse-grained features (general facial expression). The Local Prediction Penalty forces the network to disregard predictive …local signals learned from local receptive fields and instead depend on the global facial region. Second, we propose a Spatial Self-Attention method for fine-grained feature representation to learn distinct face features from pixel positions. The Spatial Self-Attention accumulates attention features at privileged positions without changing the spatial feature dimension. Lastly, we leverage a classifier to carefully combine all learned features (coarse-grained and fine-grained) for better feature representation. Extensive experiments demonstrate that our proposed methods significantly improve facial expression recognition performance. Show more
Keywords: Facial expression recognition, spatial self-attention, coarse-grained, fine-grained, convolutional neural network
DOI: 10.3233/JIFS-212022
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3947-3959, 2022
Authors: Cid-Galiot, Jonathan J. | Aguilar-Lasserre, Alberto A. | Grande-Ramírez, José Roberto | Juárez-Martínez, Ulises | Posada-Gómez, Rubén | Calderón-Palomares, Luis A.
Article Type: Research Article
Abstract: This research is carried out in the Mexican oil and gas industry. An Intelligent Decision Support System (IDSS) is proposed, through support modules for the human operator (fuzzy expert system and artificial neural network) that simulate, forecast and standardize operational decision criteria of a sequential pipeline pumping system, with problems of vandalism, mechanical deterioration in the face of a complex topographic profile, in order to minimize operational subjectivity and prevent contingencies. The research provides new control and monitoring alternatives that guarantee the operational reliability of a pumping station, minimizing the effects of risk by managing the knowledge of the experts …involved in the problem, data mining and association of results, which allow to unify criteria decision. The originality of the work focuses on the ability to model, identify and adapt variables to current international parameters, considering previous works through a comprehensive perspective. Show more
Keywords: Hydrocarbon, pipeline transport system (PTS), fuzzy expert system (FES), artificial neural network (ANN), intelligent decision support system (IDSS)
DOI: 10.3233/JIFS-212411
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3961-3981, 2022
Authors: Tao, Yuwen | Jiang, Yizhang | Dong, Xuan | Zhou, Leyuan | Ding, Yang | Qian, Pengjiang
Article Type: Research Article
Abstract: Epilepsy is a common brain disease, caused by abnormal discharge of human brain neurons, resulting in brain dysfunction syndrome. Although epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can lead to physical and mental impact of patients. At present, the method used to detect epilepsy is to make a comprehensive judgment by EEG examination combined with clinical symptoms. With the application of AI technology, some advanced algorithms have been used to assist medical diagnosis. In this trend, we use extreme learning machine to observe and detect patients with epilepsy. ELM has the …characteristics of high efficiency and high precision, so it is often used in regression and classification problems. However, in the face of different data sets, ELM structure is not enough to achieve good performance. This is caused by the uneven distribution of data in different data sets. To solve this problem, we add the transfer learning module to the basic ELM structure. The purpose of adding transfer learning is to divide the disordered data in the domain space and construct a data set suitable for ELM learning. Specifically, the raw data are mapped to high-dimensional space by kernel method through domain adaptive method. Secondly, in high-dimensional space, the distance between different domains should be reduced appropriately. Finally, ELM method is used to analyze and predict the changed data set. In the whole algorithm process, due to the characteristics of ELM updating weight, only a certain amount of hidden nodes are needed, and the training process is very fast. At the same time, after adding the transfer learning function module, the accuracy of ELM is also satisfactory. In this paper, the epilepsy data of patients were used for comparative experiments. The experimental results show that the method can maintain high efficiency and satisfactory accuracy. Show more
Keywords: Extreme learning machine, domain adaptation, signal classification, Epileptic EEG
DOI: 10.3233/JIFS-212068
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3983-3992, 2022
Authors: Wei, Lixin | Wang, Yexian | Fan, Rui | Hu, Ziyu
Article Type: Research Article
Abstract: In order to solve the premature convergence of multi-objective evolutionary algorithm, a two-stage diversity enhancement differential evolution algorithm for multi-objective optimization problem(TSDE) is proposed. The offspring with better performance needs the generation of high-quality parent generation. In this paper, an improved cell density method is used to screen for the high quality parents by estimating the global distribution of the objective space. Moreover, Principal Component Analysis operator is introduced to the external archive to perturb the non-dominated solution, which not only ensures the convergence but also improves the diversity. In order to verify the effectiveness of the algorithm, TSDE and …other advanced methods are run on 19 test functions. The results show that TSDE performs better than other algorithms. Show more
Keywords: Multi-objective optimization, differential evolution, evolutionary algorithms, principal component analysis
DOI: 10.3233/JIFS-202645
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3993-4010, 2022
Authors: Joseph, Abin John | Asaletha, R.
Article Type: Research Article
Abstract: WCSN is one of the most significant research areas in the terrestrial networking field due to its wide range of applications. One of the most difficult challenges is expanding the overall running time without attaching any new batteries or hardware. Using a novel EDT (Energy, Distance, Time) driven strategy, this paper proposes a clustering algorithm to solve the problems of the hotspot as well as reduce battery energy loss. The CH rotation method was then described in detail. This paper will introduce a new function called SCH (Substitute cluster head), which has replaced CH. The main aim of this research …is to improve energy reliability and network lifetime. Finally, the presented EDT approach can be comparable to current algorithms, where MFSTERP’s network lifetime is 15.4%, EECHS’s is 23.2%, and ABC-DE’s is 11.4%, but our proposed EDT methodology extends network lifetime by 40% as well as decreases energy usage by 7% as compared to LEACH when determining the SCH. Show more
Keywords: Wireless chemical sensor network, EDT strategy, cluster head, substitute cluster head, residual energy
DOI: 10.3233/JIFS-212912
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4011-4021, 2022
Authors: Guang, Jinzheng | Xi, Zhenghao
Article Type: Research Article
Abstract: It is an essential and challenging task to accurately identify unknown plants from images without professional knowledge due to the large intra-class variance and small inter-class variance. Aiming at the problem of low accuracy and model complexity, a lightweight plant species recognition algorithm using EfficientNet with Efficient Channel Attention (ECAENet) is proposed. The proposed approach is based on EfficientNet, which used neural architecture search to gain a baseline network and uniformly scales all dimensions of depth, width, and resolution using a compound coefficient. To overcome Squeeze-and-Excitation block complexity, the proposed method replaces all the two fully-connected layers in the channel …attention modules with a fast one-dimensional convolution with an adaptive kernel, which avoids dimensionality reduction and effectively learns the discriminative features. The experimental results demonstrate that our ECAENet achieves 99.56%, 99.75%, 98.40%, and 93.79% accuracy on the well-known Swedish Leaf, Flavia Leaf, Oxford Flowers, and Leafsnap datasets, respectively. In particular, our method achieves 3.6x fewer network parameters and 8.4x FLOPs than others with similar accuracy. Therefore, our method achieves better recognition performance compared to most of the existing plant recognition methods. Show more
Keywords: Plant species recognition, efficientNet, image Classification, channel attention, convolutional neural networks
DOI: 10.3233/JIFS-213314
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4023-4035, 2022
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