Driving Pattern Analysis, Gear Shift Classification, and Fuel Efficiency in Light-Duty Vehicles: A Machine Learning Approach Using GPS and OBD II PID Signals by Juan José Molina-Campoverde, Juan Zurita-Jara, Paúl Molina-Campoverde

“…Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. …”
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Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques by Zheshu Jia, Deyun Chen

“…In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. …”
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Application of machine learning in predicting adolescent Internet behavioral addiction by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…Gender, age, residence type, and other data were compared between the groups, and independent risk factors for adolescent Internet addiction were analyzed using a logistic regression model. Six methods—multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting—were used to construct the model. …”
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Classification of patients with lithium-treated bipolar disorder based on gene expression: Dirichlet Bayesian network model by Nader Salari, Sahar Souri Pilangorgi, Afshin Almasi, Soodeh Shahsavari, Andrew J. Fournier

“…To classify patients with bipolar disorder who are receiving lithium treatment based on their gene expression profiles, using a Dirichlet Bayesian network model and compared with Support Vector Machine and Random Forest algorithms. …”
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Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil by Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Viana, Adão Robson Elias, Giovani Benin, Lucas Scolari, Arthur Armadori, Diandra Ganascini Donato

“…The tested supervised machine learning algorithms included Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), combined with vegetation indices from the visible spectrum (RGB), multispectral indices, and bands. …”
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A Data-Driven Signal Subspace Approach for Indoor Bluetooth Ranging by Zaid Bin Tariq, Jayson P. Van Marter, Anand G. Dabak, Naofal Al-Dhahir, Murat Torlak

“…Our results show an improved performance of our proposed approach by more than 37%, while still enjoying the lowest computational complexity than existing MUSIC and support vector regression approaches for BLE ranging.…”
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A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure by Yan Xue, Weiliang Gao, Yanming Zhao

“…Moreover, a concrete durability prediction model based on particle swarm optimization-least squares support vector machine (PSO-LSSVM) and improved NSGA-II (nondominated sorting genetic algorithm II) algorithm was proposed to quickly and accurately determine the optimization scheme of self-healing concrete mix proportion. …”
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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…It integrates an improved support vector regression algorithm and Kalman smoother. …”
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Application of federated learning in predicting breast cancer by Chai Jiarui

“…During the local training process, the data is normalized and feature extracted, initially classified using support vector machines (SVM) or penalized logistic regression and optimized using stochastic gradient descent (SGD). …”
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Dynamic feature selection for silicon content prediction in blast furnace using BOSVRRFE by Junyi Duan

“…This study proposes a Bayesian online sequential update and support vector regression recursive feature elimination (BOSVRRFE) algorithm for dynamic feature selection. …”
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Prediction of cardiovascular diseases based on GBDT+LR by Zengxiao Chi, Li Liu, Liqin Yi, Lin Shi

“…Using the UCI cardiovascular disease dataset, we conduct experimental comparisons between the proposed model and other common disease classification algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). …”
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Prediction of Traction Energy Consumption for Urban Rail Transit Trains in Relative Speed Mode by GUO Tuansheng

“…［Objective］It is aimed to accurately predict the traction energy consumption of urban rail transit trains operating in relative speed mode using support vector machine（SVM）regression and genetic algorithms, ultimately enhancing energy efficiency during train operation. …”
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Unleashing the power of intelligence: revolutionizing malaria outbreak preparedness with an advanced warning system in Benin, West Africa by Gouvidé Jean Gbaguidi, Nikita Topanou, Walter Leal Filho, Komi Agboka, Guillaume K. Ketoh

“…Subsequently, an intelligent model for forecasting malaria outbreaks was developed using support vector machine (SVM) algorithm. The developed model for malaria outbreaks was then employed to establish an intelligent system for warning and forecasting malaria incidence on a monthly basis, utilising the Meteostat platform, an online weather data service provider, in conjunction with the Streamlit framework. …”
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Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospe... by Meng Zheng, Xiaowei Zhang, Haihong Wang, Ping Yuan, Qiulan Yu

“…The research group adopted four machine learning algorithms: Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). …”
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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…For thermal conductivity, support vector regression achieved the best predictive performance with R² = 0.933. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…We developed four machine learning models (logistic regression, support vector machine, decision tree, and extreme gradient boosting [XGBoost]), and compared their predictive performances. …”
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Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction by Nan Zhou, Jin Hong, Bo Song, Shichao Wu, Yichen Wei, Tao Wang

“…To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. …”
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An integrated approach of feature selection and machine learning for early detection of breast cancer by Jing Zhu, Zhenhang Zhao, Bangzheng Yin, Canpeng Wu, Chan Yin, Rong Chen, Youde Ding

“…The efficacy of the proposed method was assessed using five machine learning models, K-Nearest Neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Light Gradient Boosting Machine (LightGBM), applied to the Wisconsin Breast Cancer Diagnosis (WBCD) datasets. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…Specifically, the Random Forest model achieved an R² value of 0.969, reflecting improvements of 27.5%, 1.89%, 3.8%, and 34.02% compared to artificial neural network, gradient boosting, polynomial regression, and support vector regression models, respectively. …”
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