Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia by George S. Chen, BSc, Terry Lee, PhD, Jennifer L.Y. Tsang, MD, Alexandra Binnie, MD, Anne McCarthy, MD, Juthaporn Cowan, MD, Patrick Archambault, MD, Francois Lellouche, MD, Alexis F. Turgeon, MD, MSc, Jennifer Yoon, MD, Francois Lamontagne, MD, Allison McGeer, MD, Josh Douglas, MD, Peter Daley, MD, Robert Fowler, MD, David M. Maslove, MD, Brent W. Winston, MD, Todd C. Lee, MD, Karen C. Tran, MD, Matthew P. Cheng, MD, Donald C. Vinh, MD, John H. Boyd, MD, Keith R. Walley, MD, Joel Singer, PhD, John C. Marshall, MD, James A. Russell, MD, for the Community-Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) Investigators

“…This retrospective observational study trained separate models using random-forest classifier (RFC), support vector machines (SVMs), Extreme Gradient Boosting (XGBoost), and multilayer perceptron (MLP) to predict three endpoints: eventual use of invasive ventilation, vasopressors, and RRT during hospitalization. …”
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Quantum AI: A Cognitive Machine Learning Technique based on Nurturing Food Security Sustainability Predictive Analysis for Life Science - Bioengineering in Healthcare by Senthil G.A., Monica K.M., Prabha R., Prinslin L., Elavarasi R.

“…It enables tailored dietary recommendations. Quantum Support Vector Machines (QSVM), Quantum Neural Network (QNN), and Quantum Reinforcement Learning (QRL). …”
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Enhanced Category-Feature Association Measure by Soran S. Badawi, Ari M. Saeed, Sara A. Ahmed, Diyari A. Hassan

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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 × 107 Random forest, 6.23 × 103 Support vector machine, 3.64 × 104 K-nearest neighbours and 1.88 × 102 Naïve Bayes) compared to using all features. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

“…This study explores how factors like temperature, pressure, pH, and ion concentration influence CaCO₃ deposition and how it affects reservoir performance. Using machine learning models—Support Vector Regression (SVR), Extra Trees (ET), and Extreme Gradient Boosting (XGB)—the research aims to predict how much permeability is lost due to scaling. …”
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Research on Fault Diagnosis of Traction Power Supply System Based on PSO-LSSVM by Lei ZHANG

“…A fault diagnosis model based on PSO optimized least squares support vector machine was established, and PCA algorithm was used to extract data characteristics as input of fault diagnosis model, and reduce input dimension. …”
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Stress–Strain Prediction for Steam-Cured Steel Slag Fine Aggregate Concrete Based on Machine Learning Algorithms by Chuanshang Wang, Di Hu, Qiang Jin

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Flood Inundation Mapping of a River Stretch Using Machine Learning Algorithms in the Google Earth Engine Environment by Maaz Ashhar, Venkata Reddy Keesara, Venkataramana Sridhar

“…Sentinel‐1 SAR data from 6th July 2022 to 20th July 2022 were used to perform flood inundation mapping. Various machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Tree (GBT), and Classification and Regression Tree (CART), were employed. …”
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A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms by Mohammed Alenezi, Fatih Anayi, Michael Packianather, Mokhtar Shouran

“…This study introduces a novel machine learning framework that integrates Particle Swarm Optimization (PSO) and Dwarf Mongoose Optimization (DMO) algorithms for feature selection and hyperparameter tuning, combined with advanced classifiers such as Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM). …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities by Manoj Lamichhane, Sushant Mehan, Kyle R. Mankin

“…Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. …”
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Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression M... by Ru Yu, Xiaoli Wang, Xiaojun Xu, Zhiwen Zhang

“…Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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Prediction Model of Water Demand for Scouring Siltation in Coastal River Networks Based on APSO and SVM: A Case Study of Doulong Port by MA Zhutong, XIANG Long, YAN Ke

“…A predictive model of water demand for scouring siltation was constructed, which combined adaptive particle swarm optimization (APSO) algorithm with support vector machine (SVM) and optimized the model parameters of the SVM through the APSO algorithm, enhancing the prediction accuracy of the APSO-SVM model. …”
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Identification of Biomarkers Associated with Heart Failure Caused by Idiopathic Dilated Cardiomyopathy Using WGCNA and Machine Learning Algorithms by Mengyi Sun, Linping Li

“…Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. …”
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Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms by M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami

“…The study utilizes the Gradient Tree Boosting (GTB), Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) classifiers within the Google Earth Engine (GEE) platform. …”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

“…PCA revealed 87.97 % variance, demonstrating distinct groupings. Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. …”
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Machine-Learning-Algorithm-Assisted Portable Miniaturized NIR Spectrometer for Rapid Evaluation of Wheat Flour Processing Applicability by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. …”
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