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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: Zhang, Guili | Li, Pengxi | Zhang, Hanyue | Yu, Yinglong | Liang, Zhao
Article Type: Research Article
Abstract: Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing …system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education. Show more
Keywords: 5G, Blended campus network, intelligence teaching, VR practice teaching system
DOI: 10.3233/JIFS-237768
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9727-9738, 2024
Authors: He, Liu | Zhu, Yuanguo | Ye, Tingqing
Article Type: Research Article
Abstract: In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional …differential equations, uncertain differential equations and stochastic differential equations. Show more
Keywords: Uncertainty theory, Uncertain fractional differential equations, Parameter estimation, Least squares estimation, Uncertain stock price model
DOI: 10.3233/JIFS-237977
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9739-9753, 2024
Authors: Sung, Tien-Wen | Zhao, Baohua | Zhang, Xin | Lee, Chao-Yang | Fang, Qingjun
Article Type: Research Article
Abstract: Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of …different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability. Show more
Keywords: QUATRE algorithm, swarm-based optimization, fixed dimension updating, 3D path planning, unmanned aerial vehicle
DOI: 10.3233/JIFS-230928
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9755-9781, 2024
Authors: Amsaprabhaa, M.
Article Type: Research Article
Abstract: Human pose recognition from videotapes has become an emerging research topic for tracking human movements. The objective of this work is to develop a deep multimodal Spatio-Temporal Harris Hawk Optimized Pose Recognition (STHHO-PR) framework for self-learning fitness exercises. The presented STHHO-PR framework uses audio modality and visual modality to classify the different poses. In audio modality, the VGG-16 network paradigm is used to extract the audio traits for fitness pose recognition. In visual modality, Harris Hawks Optimization (HHO) along with the Minimum Cross Entropy (MCE) method is employed to find out the optimum threshold values for body parts segmentation. These …segmented body parts highlight the human joint points that are connected through the skeletonization process to extract the skeletal information. The extracted spatio-temporal features from audio modality and visual modality are optimally fused and used in the classification process. Weighted Majority Voting Ensemble (WMVE) classifier is adopted to build the classification model. This work is experimented with yoga videos acquired from publicly available datasets. The results show that the presented STHHO-PR framework outperforms other state-of-art procedures in terms of prediction accuracy. Show more
Keywords: Harris Hawks Optimization, Minimum Cross Entropy, Weighted Majority Voting Ensemble classifier, yoga video, yoga poses classification
DOI: 10.3233/JIFS-233286
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9783-9805, 2024
Authors: Cui, Wanqiu
Article Type: Research Article
Abstract: Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, …the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing. Show more
Keywords: Graph data storage, social networks, topic cluster, equivalent partition
DOI: 10.3233/JIFS-233540
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9807-9818, 2024
Authors: Narendiranath Babu, T. | Kothari, Ayush Jain | Rama Prabha, D. | Mokashe, Rohan | Kagita, Krish Babu | Raj kumar, E.
Article Type: Research Article
Abstract: In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and …its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds. Show more
Keywords: Helical gearbox, vibration analysis, signal processing, fault diagnosis, artificial neural network, K-nearest neighbor, support vector machine, decision tree, random forest
DOI: 10.3233/JIFS-233602
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9819-9840, 2024
Authors: Guo, Sheng | Tan, Mian | Cai, Shan | Zhang, Zaijun | Liang, Yihui | Feng, Hongxi | Zou, Xue | Wang, Lin
Article Type: Research Article
Abstract: Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the …local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. Show more
Keywords: Facial expression recognition, deep convolution, multi-feature convolution module, local cross-attention module
DOI: 10.3233/JIFS-233748
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9841-9856, 2024
Authors: Geng, Xiuli | Du, Yuanhao | Cao, Shuyuan | Cheng, Sheng
Article Type: Research Article
Abstract: Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are …employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers. Show more
Keywords: Low-carbon, customer satisfaction, importance performance analysis, latent dirichlet allocation, support vector machine, random forest
DOI: 10.3233/JIFS-235074
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9857-9871, 2024
Authors: Ratmele, Ankur | Thakur, Ramesh
Article Type: Research Article
Abstract: As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers’ decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the …textual content of reviews and classify buyer’s opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product’s features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer’s opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. Show more
Keywords: Opinions, Opinion Extraction (OE), product features, decision making, hierarchical attention mechanism, GloVe
DOI: 10.3233/JIFS-235389
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9873-9887, 2024
Authors: Mi, Xiaodong | Luo, Qifang | Zhou, Yongquan
Article Type: Research Article
Abstract: Panchromatic and multi-spectral image fusion, called panchromatic sharpening, is the process of combining the spatial and spectral information of the source image into the fused image to give the image a higher spatial and spectral resolution. In order to improve the spatial resolution and spectral information quality of the image, an adaptive multi-spectral image fusion method based on an improved arithmetic optimization algorithm is proposed. This paper proposed improved arithmetic optimization algorithm, which uses dynamic stochastic search technique and oppositional learning operator, to perform local search and behavioral complementation of population individuals, and to improve the ability of population individuals …to jump out of the local optimum. The method combines adaptive methods to calculate the weights of linear combinations of panchromatic and multi-spectral gradients to improve the quality of fused images. This study not only improves the quality and effect of image fusion, but also focuses on optimizing the operation efficiency of the algorithm to have real-time and high efficiency. Experimental results show that the proposed method exhibits strong performance on different datasets, improves the spatial resolution and spectral information quality of the fused images, and has good adaptability and robustness. The source code is available at: https://github.com/starboot/IAOA-For-Image-Fusion . Show more
Keywords: Image fusion, multi-spectral image, panchromatic image, oppositional learning operator, arithmetic optimization algorithm, meta-heuristic
DOI: 10.3233/JIFS-235607
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9889-9921, 2024
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