Automatic vibration fault detection of coal mine explosion-proof electrical equipment based on One-Class Support Vector Machine by ZHENG Tiehua, WANG Fei, ZHAO Gelan, DU Chunhui

“…Experimental results showed that: ① When the number of iterations is 20, the OCSVM algorithm can complete convergence and achieve stability. ② In the electrical equipment signal classification experiment based on OCSVM, the use of the polynomial kernel function accurately classified samples for detection. ③ In the performance analysis of automatic vibration fault detection, the proposed method showed significantly higher accuracy across different sample sizes than infrared thermography and detection methods based on grey wolf optimization and support vector machine. …”
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Discrimination of Chinese prickly ash origin place using electronic nose system and feature extraction with support vector boosting machine by Junbo Lian, Peng Wu, Wenhui Han, Yaping Xie, Yue Zheng, Yuxuan Xu, Xinlin Li, Guofeng Hou, Chengxiang Yong, Qi Lv, Qiansheng Ye, Guohua Hui

“…These novel techniques were coupled with a support vector boosting machine for origin place classification. …”
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Research on bearing fault diagnosis based on improved northern goshawk algorithm optimizing SVM by WU Xiaojun, LI Quwei

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Diesel Engine Urea Injection Optimization Based on the Crested Porcupine Optimizer and Genetic Algorithm by Xu Chen, Changhai Ma, Quanli Dou, Shuzhan Bai, Ke Sun, Zhenguo Li

Subjects: “…crested porcupine optimizer support vector machine…”
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Deep and Domain Specific Feature-Based Cervical Cancer Classification Using Support Vector Machine Optimized With Particle Swarm Optimization by Ritesh Maurya, Lucky Rajput, Satyajit Mahapatra

“…The noisy features have been eliminated by utilising the concept of mutual information whereas, a support vector machine optimized with Particle Swarm Optimization (PSO) was used for the classification. …”
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An Efficient Categorization of Diabetes Imbalanced Data Using SMOTE-ENN With Fine-Tuned LS-SVM Algorithm by Nwayyin Mohammed, Mariwan Hama Saeed

“…The classification of imbalanced datasets is a crucial field in machine learning. The machine learning approach that is used in this study is the Least Square Support Vector Machine LS-SVM to categorize the diabetes patients. …”
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Network Congestion Tracking and Detection in Banking Industry Using Machine Learning Models by Kingsley Ifeanyi Chibueze, Nwamaka Georgenia Ezeji, Nnenna Harmony Nwobodo-Nzeribe

“…This research evaluates various ML algorithms, including Support Vector Machines, Decision Trees, and Random Forests, to identify the most effective approach for congestion detection. …”
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Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM by CHEN Yong-hong, SU Yong-sheng, HU Ping

Subjects: “…least squares support vector machine(lssvm); genetic algorithm(ga); corrosion rate; prediction…”
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Evaluation of Water Resources Carrying Capacity in Heilongjiang Province Based on PSO-SVM Model by WANG Tao, LI Zhijun

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Research on bolt loosening recognition based on sound signal and GA-SVM–RFE by Li-Ye Hou, De-Bing Zhuo

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Fog node intrusion detection and response based on SVMIF and INSGA-II algorithm by Zhuojun Luo

Subjects: “…Support vector machine isolation forest…”
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Comparative Analysis of Regression Models for Stock Price Prediction: Linear, Support Vector, Polynomial, and Lasso by Ștefan Rusu, Marcel Ioan Boloș, Marius Leordeanu

“…Four regression algorithms – linear regression, support vector regression (SVR), polynomial regression and LASSO regression – were applied to Apple Inc’s historical price data for two-years ending on October 1st, 2024, to predict the next day’s closing stock price. …”
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Clustering and Vectorizing Acoustic Emission Events of Large Infrastructures’ Normal Operation by Theocharis Tsenis, Vassilios Kappatos

“…A methodology was developed, processing corresponding acoustic emission recordings originating from lifting dams’ metal gates, using advanced denoising—preprocessing, various decompositions, and spectral embeddings associated with various latest nonlinear processing clustering techniques—thus providing a detailed cluster label morphology and profile of water gates’ normal operating area. Latest machine learning outlier detection algorithms, like One-Class Support Vector Machine, Variational Auto-Encoder, and others, were incorporated, producing a vector of confidence on upcoming out-of-the-normal gate operation and failure prediction, achieving detection contrast enhancement on out-of-the-normal operation points up to 400%.…”
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Research on the Macrocell Wireless Channel Model Based on Physic-Inspired Support Vector Regression Algorithm Wireless Channel Model in Macrocell Environment by Qi Yao, Zhongyu Liu, Lixin Guo, Jiang Guo, Zuoyong Nan, Wei Liu, Jiangting Li

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An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm by Ibrahim Musa Conteh, Gibril Njai, Abass Conteh, Qingguo Du

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Application of PSO-Optimized Twin Support Vector Machine in Medium and Long-Term Load Forecasting under the Background of New Normal Economy by Xiang He, Yan Chen, Kai Hu, Lin Wan

“…In addition, this paper combines the PSO algorithm to optimize the twin support vector machine, constructs the optimized algorithm according to the flow chart, and applies it to the medium and long-term load forecasting under the background of a new normal economy. …”
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Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models by Hongxia Ma, Jiandong Liu, Jia Zhang, Jiandong Huang

“…To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. …”
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Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification by Loris Nanni, Matteo De Gobbi, Roger De Almeida Matos Junior, Daniel Fusaro

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Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron by Jaka Kusuma, Rubianto, Rika Rosnelly, Hartono, B. Herawan Hayadi

“…This study will compare classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron to find the best algorithm in the classification of leaf disease in corn plants, namely, cercospora leaf spot gray, common rust, and northern leaf blight using the VGG-16 deep learning model used as image feature extraction. …”
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Klasifikasi Tingkat Stress dari Data Berbentuk Teks dengan Menggunakan Algoritma Support Vector Machine (SVM) dan Random Forest by Naufal Fathirachman Mahing, Alifi Lazuardi Gunawan, Ahmad Foresta Azhar Zen, Fitra Abdurrachman Bachtiar, Satrio Agung Wicaksono

“…One way to find out someone's stress level is through text analysis.This research was conducted to classify stress levels based on text data using the Support Vector Machine (SVM) and Random Forest algorithms. …”
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