Transformer Fault Diagnosis Based on Multi-Strategy Enhanced Dung Beetle Algorithm and Optimized SVM by Shuming Zhang, Hong Zhou

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Brain Computer Interface using Genetic Algorithm with modified Genome and Phenotype Structures by Rohtash Dhiman, A Anshul

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Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook by Amna Naseeb, Muhammad Zain, Nisar Hussain, Amna Qasim, Fiaz Ahmad, Grigori Sidorov, Alexander Gelbukh

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Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm by Wenliao Du, Ansheng Li, Pengfei Ye, Chengliang Liu

“…A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM) with particle swarm optimization (PSO) algorithm for plunger pump in truck crane is proposed. …”
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Application of Improved MDSMOTE and FC-SVM in Imbalanced Data Set Classification by WEN Xue-yan, ZHAO Li-ying, XU Ke-sheng, LU Guang

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Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random F... by Mamadou Ndiaye, René Boissy, Mbagnick Faye, N’kpomé Styvince Romaric Kouao

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Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms by Benjamin Mukanya Ntumba, Jean Paul Ngbolua Koto-Te-Nyiwa, Blaise Bikandu Kapesa, Nathanael Kasoro Mulenda

“…In this study, we propose a hybrid approach integrating K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms. …”
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Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning by JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin

“…Selecting the first five modes for the flow field and the first seven modes for the dust concentration field balanced the accuracy and efficiency of POD reconstruction. The support vector machine (SVM) model outperformed the Random Forest and Neural Network models in predicting mode coefficients. …”
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Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency by Senthil G.A., Prabha R., Asha R.M., Suganthi S.U., Sridevi S.

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Predicting the availability of power line communication nodes using semi-supervised learning algorithms by Kareem Moussa, Khaled Mostafa Elsayed, M. Saeed Darweesh, Abdelmoniem Elbaz, Ahmed Soltan

“…Self-training classifier has been used to allow Light Gradient Boosting Machine (LGBM) and Support Vector Machine (linear and non-linear kernel) to behave in a self-training manner as well as the training of label propagation and label spreading algorithms. …”
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Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. …”
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Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen by Sharazita Dyah Anggita, Ferian Fauzi Abdulloh

“…This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. …”
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Leveraging Machine Learning for Exchange Rate Prediction: A Business and Financial Management Perspective in Nigeria by Adedeji Daniel Gbadebo

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Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain by Mohammad Barzegar, Aliakbar Hasani

“…The features of higher importance were identified to build the model in the first stage using the logistic regression algorithm. In the second stage, the support vector machine algorithm, a critical supervised learning algorithm, was used to classify the customers and rank the essential features. …”
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Improved empirical wavelet transform combined with particle swarm optimization-support vector machine for EEG-based depression recognition by Yongxin Wang, Longqi Xu, Hongxu Qian, Haijun Lin, Xuhui Zhang

“…In this paper, we propose an improved method for the early objective diagnosis of depression utilizing an empirical wavelet transform (EWT) technique enhanced by a particle swarm optimization-support vector machine (PSO-SVM) algorithm. Our approach specifically focuses on the Fpz channel in the prefrontal lobe of the brain, which most accurately reflects the electrical anomalies associated with depression among 128 channels of resting-state electroencephalogram (EEG). …”
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Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine by Wenjie Guo, Jie Liu, Jun Ma, Zheng Lan

“…To address this problem, a novel hybrid forecasting framework based on adaptive mode decomposition (AMD) and improved least squares support vector machine (ILSSVM) is proposed for effective short-term power load forecasting. …”
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Detecting Fake Reviews in E-Commerce: A Case Study on Shopee Using Support Vector Machine and Random Forest by Khoirotulmuadiba Purifyregalia, Khothibul Umam, Nur Cahyo Hendro Wibowo, Maya Rini Handayani

“…This study aims to detect fake reviews on Shopee by applying a text classification approach using Random Forest and Support Vector Machine (SVM) algorithms. A dataset consisting of 3,686 Shopee product reviews was collected and underwent preprocessing steps including data cleaning, normalization, tokenization, and TF-IDF weighting. …”
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Grassland biome fragmentation analysis using sentinal-2 images and support vector machine learning model in South Africa by Andisani Netsianda, Paidamwoyo Mhangara, Eskinder Gidey

“…Given the paucity of research on the threatened ecosystem, the support vector machine learning algorithm (SVM) is employed to investigate fragmentation from 2016 to 2023. …”
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Open phase fault-tolerant support vector machine predictive power control for six-phase induction generator WECS by Yanis Hamoudi, Maher G.M. Abdolrasol, Hocine Amimeur, Faycal Hassaini, Pin Jern Ker, Taha Selim Ustun

“…Then, open-phase localization is achieved using the Support Vector Machine (SVM) with hyperparameter Bayesian Optimization (BO). …”
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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine by Hanliang Bian, Zhongxun Sun, Jiahan Bian, Zhaowei Qu, Jianwei Zhang, Xiangchun Xu

“…Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ( $$q_c$$ ) and sleeve friction ( $$f_s$$ ) as input variables. …”
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