Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection by Liehai Cheng, Zhenli Zhang, Giuseppe Lacidogna, Xiao Wang, Mutian Jia, Zhitao Liu

“…Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. …”
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Enhanced Farmland Extraction from Gaofen-2: Multi-Scale Segmentation, SVM Integration, and Multi-Temporal Analysis by Hang Yang, Hao Sun, Ke Wang, Jian Yang, Muhammad Hasan Ali Baig

“…This study proposes an object-oriented multi-scale segmentation method combined with a support vector machine, leveraging spectral reflectance, texture, and temporal differences between farmland and non-farmland plots. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. …”
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Analysis of machine learning approaches for the interpretation of acoustic fields obtained by well noise data modelling by N. V. Mutovkin

“…In order to build the best model, machine learning approaches such as linear regression with different variants of regularisation, Bayesian regression, neural net, methods of supporting vectors, decision tree, random forest and gradient boosting are considered. …”
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An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer by Subrata Das, Vatsal Patel, Shouvik Chakravarty, Arnab Ghosh, Anirban Mukhopadhyay, Nidhan K. Biswas

“…Each mutation was annotated with 41 PCSAs. Three machine learning algorithms—logistic regression, random forest, and support vector machine—along with recursive feature elimination, were used to rank these PCSAs. …”
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Machine Learning in the Teaching Quality of University Teachers: Systematic Review of the Literature 2014–2024 by Walter Zambrano-Romero, Ciro Rodriguez, Josselyn Pita-Valencia, Walter José Zambrano-Romero, José Manuel Moran-Tubay

“…Regarding the advances in machine learning in predicting teacher teaching quality, 30 ML algorithms were identified, the most used being the Back Propagation (BP) neural network and support vector machines (SVM). …”
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Machine Learning Approaches for Predicting Employee Turnover: A Systematic Review by Hojat Talebi, Amid Khatibi Bardsiri, Vahid Khatibi Bardsiri

“…We analyze various ML techniques, including Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Networks, highlighting their effectiveness in predicting turnover based on employee data. …”
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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities by Shoaib Hassan, Qianmu Li, Khursheed Aurangzeb, Affan Yasin, Javed Ali Khan, Muhammad Shahid Anwar

“…The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. …”
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Predicting Dilution in Underground Mines with Stacking Artificial Intelligence Models and Genetic Algorithms by Jorge L. V. Mariz, Tertius S. G. Ferraz, Marinésio P. Lima, Ricardo M. A. Silva, Hyongdoo Jang

“…The models include support vector machines, neural networks, and tree-based approaches. …”
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Analysis of Traffic Conflicts at Roundabout Entrances and Exits – A Machine Learning Approach for Enhanced Safety by Yuzhou DUAN, Zhipeng LIN, Yulong WANG, Qiaowen BAI

“…A real-time traffic safety evaluation method has been developed using machine learning algorithms, including random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and decision tree (DT) model. …”
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Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis by Zenghui Liu, Jing Lin

“…It compares individual cost prediction models (Decision Tree, BP Neural Network, and Support Vector Machine) with combined prediction models (BP-DT and BP-SVM) for high-rise building cost prediction. …”
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Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review by Veronica Atemnkeng Ntam, Tatjana Huebner, Michael Steffens, Catharina Scholl

“…The ML-model applicability was evaluated via information on validation and performance criteria and the compliance with relevant ethical, social, and legal criteria in the EU.ResultsIn the 29 publications reviewed, Random Forest (RF) and Support Vector Machine (SVM) were identified as most frequently used ML-methods. …”
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A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features by Nurhan Gürsel Özmen, Neslihan Baki

“…The performance of the PD and control group was evaluated with Gradient Boosting (GB), Gaussian Naive Bayes (GNB), and K-nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) Algorithms. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. …”
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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…This study adopts a novel approach by utilizing hybrid models, which offer greater accuracy than individual or ensemble ML models. Specifically, support vector regression (SVR) was combined with three optimization algorithms: firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO) to create hybrid models for estimating the SE of carbon fiber-reinforced mortars. …”
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PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION by Gilgen Mate Landry, Rodolphe Nsimba Malumba, Fiston Chrisnovi Balanganayi Kabutakapua, Bopatriciat Boluma Mangata

“…The sample is divided into two sets: 80% for training and 20% for testing. The algorithms evaluated include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Convolutional Neural Networks (CNN). …”
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Non-intrusive load identification method based on VMD and PSO-SVM by YANG Rui, ZOU Xiaosong, XIONG Wei, YUAN Xufeng, ZHENG Huajun, LIU Bin

“…In view of the advantage of variational mode decomposition (VMD) in signal processing, a load identification algorithm based on variational mode decomposition and fast independent component analysis (VMD-FastICA) and variational mode decamposition-entropy-particle swanm optimization fo optimizing support vector machines (VMD-Entropy-PSOSVM) is proposed. …”
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Methods for state of health estimation for lithium-ion batteries: An essential review by Rhdifa Houda, Ammar Abderazzak, Bouattane Omar

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Applications of machine learning algorithms in lithological mapping of Saint Katherine Neoproterozoic rocks in the South Sinai of Egypt using hyperspectral PRISMA data by Mohamed W. Ali-Bik, Tehseen Zafar, Safaa M. Hassan, Mohamed F. Sadek, Saif M. Abo Khashaba

“…Automatic lithological mapping has been carried out using Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms applied to hyperspectral PRISMA data with an overall accuracy (OA) of up to 91.41% and 86.64%, respectively. …”
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