Article
Thermodynamics
Lei Yao, Zhanpeng Fang, Yanqiu Xiao, Junjian Hou, Zhijun Fu
Summary: This study proposes an intelligent fault diagnosis method based on support vector machine for Lithium-ion batteries. It includes denoising, modification of covariance matrix to reduce current fluctuation influence, and optimization of SVM parameters through grid search method to achieve high accuracy and timeliness.
Article
Energy & Fuels
Yuanying Chi, Yangyi Zhang, Guozheng Li, Yongke Yuan
Summary: This paper analyzes the influencing factors of electric-energy substitution in Beijing and conducts predictive analysis using machine learning models. The results show that the Gaussian kernel support vector machine model has a good prediction effect on the electric-energy substitution potential, providing guidance for the analysis of electric-energy substitution potential.
Article
Materials Science, Multidisciplinary
Shijie Xie, Hang Lin, Yifan Chen, Hongyu Duan, Hongwei Liu, Baohua Liu
Summary: The shear strength of rock fractures plays a crucial role in determining the strength and deformation behavior of engineering rock masses. A hybrid machine learning model (GS-SVR model) was proposed to predict shear strength, using the support vector regression (SVR) and grid search optimization algorithm (GS) to reduce uncertainties in evaluation. The model achieved accurate predictions by using a large dataset of rock fracture parameters and avoiding complex theoretical equations.
MATERIALS TODAY COMMUNICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Zhengkun Chen, Junzhi Li, Jing Li, Xiangwei Zhu, Caihua Li
Summary: The vulnerability of the global navigation satellite system (GNSS) to spoofing limits its application in military safety and the economy. This paper proposes a GNSS multi-parameter joint detection method based on the support vector machine (SVM) to effectively detect spoofing attacks. The experimental results show significant improvement in the spoofing detection performance compared with traditional single-parameter methods.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Energy & Fuels
Bin Xiao, Bing Xiao, Luoshi Liu
Summary: The paper presents a method for accurate State of Health (SoH) estimation of batteries using the LS-SVR model, based on hybrid pulse power testing and grey correlation analysis. The method achieves high-precision SoH estimation with limited short-term battery data and shows broad application prospects.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan, Juntao Li
Summary: This work proposes a fast regularization parameter tuning algorithm for the twin multi-class support vector machine. By adopting a novel sample data set partition strategy and utilizing linear equations and block matrix theory, the regularization parameters are continuously updated, and the relationship between the Lagrangian multipliers and the regularization parameters is proven. Finally, different events are defined to seek for the starting event for the next iteration, and the effectiveness of the proposed method is validated through experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Business, Finance
Haijun Kang, Xiangyu Zong, Jianyong Wang, Haonan Chen
Summary: A hybrid Support Vector Machine (SVM) model is proposed and applied to forecast the daily returns of five popular stock indices. By utilizing the Binary Gravity Search Algorithm (BGSA), the parameters and inputs of SVM are optimized. The results demonstrate that the forecasts made by this model outperform other methods, with an average accuracy of 52.87%. This study proves the profitability of a trading strategy based on BGSA-SVM prediction in a real stock market.
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE
(2023)
Article
Green & Sustainable Science & Technology
Ling-ling Li, Ze-Yao Cen, Ming-Lang Tseng, Qiang Shen, Mohd Helmi Ali
Summary: This study introduces a hybrid improved cuckoo search arithmetic (HICS) for optimizing the hyper-parameters of the support vector regression machine (SVR) to predict short-term wind power output (HICS-SVR), which improves prediction precision and stability of output results effectively.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jiapeng Wang, Jiaxiang Luo
Summary: This paper proposes a new method for optimizing the kernel and penalty parameters of SVM classifiers. The method introduces a new distance measure in the feature space and presents a fast parameter optimization approach that significantly reduces training time while maintaining competitive model accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Keiji Tatsumi, Shunsuke Tsujioka, Ryota Masui, Yoshifumi Kusunoki, Yeboon Yun
Summary: This paper focuses on the labor shortage issue in the construction industry, particularly in the field of large-scale infrastructure. By recognizing the risk scores at different sections of the construction site, the paper addresses the inconsistency in structure data and introduces multiclass SVMs to improve classification accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Environmental Sciences
Guangxin Liu, Liguo Wang, Danfeng Liu, Lei Fei, Jinghui Yang
Summary: This article proposes a non-parallel SVM model, which improves the classification effect and generalization performance for hyperspectral images by adding an additional empirical risk minimization term and bias constraint.
Article
Environmental Sciences
Wen-jing Niu, Zhong-kai Feng, Shu-shan Li, Hui-jun Wu, Jia-yang Wang
Summary: This paper proposes a practical machine learning model for short-term load prediction based on feature selection and parameter optimization. Experimental results show that the proposed model outperforms several conventional models in short-term load prediction, and the CSA method is an effective tool for determining parameter combinations.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)