A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of <i>Pinus kesiya</i> var. …”
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Machine Learning Techniques in Chronic Kidney Diseases: A Comparative Study of Classification Model Performance by Nguyen Dong Phuong, Nguyen Trung Tuyen, Vu Thi Thai Linh, Nghi N Nguyen, Thanh Q Nguyen

“…Then, we utilized feature-based stratified splitting with K-means and implemented 6 machine learning algorithms (Random Forest, Support Vector Machine [SVM], Naive Bayes, Logistic Regression, K-Nearest Neighbor [KNN], and XGBoost) to compare their performance based on accuracy. …”
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Pricing in the Sharing Economy—A Hybrid Approach Leveraging Econometrics, Machine Learning, and Artificial Intelligence by Kornilios Vezyroglou, Fotios Siokis

“…This underscores the increasing importance of visual marketing in the sharing economy and the democratization of AI tools for optimizing pricing strategies. We also conduct machine learning analysis, employing algorithms like Random Forest, k-Nearest Neighbors, Support Vector Machine, Neural Network, Gradient Boosting, and AdaBoost. …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“…The aim of the present study was to identify and assess the significant risk factors of CVD and develop evidence-based prevention strategies. To do this, we used machine learning algorithms such as logistic regression, random forests, support vector machines (SVM), and artificial neural networks (ANNs) to forecast cardiovascular risk factors from past medical data and epidemiology trends. …”
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Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques by Zuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes, Pedro Cabral

“…This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. …”
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Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review by Charles Matyukira, Paidamwoyo Mhangara

“…Incorporating algorithms like random forest, support vector machines, neural networks, and XGBRFClassifier has enhanced the monitoring and analysis of vegetation dynamics at various scales. …”
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Towards sustainable agriculture in Iran using a machine learning-driven crop mapping framework by Iman Khosravi

“…Furthermore, the most widely used and flexible methods available in crop mapping studies such as decision tree (DT), random forest (RF), rotation forest (RoF), support vector machine (SVM), and dynamic time warping (DTW) were used in this study. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. …”
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Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm by Fang Liu, Xiaoli Liu, Changyou Yin, Hongrong Wang

“…Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. …”
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Detection of Psychomotor Retardation in Youth Depression: A Machine Learning Approach to Kinematic Analysis of Handwriting by Vladimir Džepina, Nikola Ivančević, Sunčica Rosić, Blažo Nikolić, Dejan Stevanović, Jasna Jančić, Milica M. Janković

“…After recursive feature elimination, classification was achieved through machine learning algorithms: logistic regression, support vector machine, and random forest. …”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…Five algorithms: KNN, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forest (RF), were trained and evaluated. …”
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Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques by Ahmed I. Saleh, Nabil S. Mahmoud, Fikry A. Salem, Mohamed Ghannam

“…Eight supervised ML algorithms were evaluated: Linear Regression, Ridge, Lasso, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Prediction of potential occurrence of historical objects with defensive function in Slovakia using machine learning approach by Jana Vojteková, Saeid Janizadeh, Matej Vojtek, Anna Tirpáková, Matej Ruttkay, František Petrovič

“…Abstract In this article, we aim at the prediction of possible locations of already defunct historical objects with a defensive function (HODFs) in Slovakia, which have not been found and documented so far, using three machine learning methods. Specifically, we used the support vector machine, k-nearest neighbors, and random forest algorithms, which were trained based on the following five factors influencing the possible occurrence of HODFs: elevation, distance from a river, distance from a settlement, lithological rock type, and type of representative geoecosystems. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…Using a comprehensive database of 1404 experimental data points spanning temperature (−10 to 450 °C), pressure (0.098 to 140 MPa), and salinity (0.017 to 6.5 mol/kg), the research evaluates the predictive capabilities of five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression with a radial basis function kernel. …”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

“…It emphasizes the growing application of ML algorithms such as artificial neural networks (ANNs), support vector regression (SVR), random forest (RF), and convolutional neural networks (CNNs) for rock property estimation, underscoring the diversity of techniques utilized. …”
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Human-machine collaboration: Ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice by Nicolas Sadoune, Ke Liu, Han Yan, Ludovic D. C. Jaubert, Nic Shannon, Lode Pollet

“…Using a highly interpretable machine-learning approach based on a support vector machine with a tensorial kernel, we reanalyze these Monte Carlo data, gaining new information about the form of order that could in turn be interpreted by traditionally trained physicists. …”
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Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning by Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić

“…The peak leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) from PROBA-V/Sentinel-3 data were calculated according to ground-truth soybean agricultural parcels in continental Croatia during 2015–2021. Four machine learning regression algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), as well as their combination, were evaluated for predicting the peak LAI and FAPAR on the entire agricultural land in the study area, with RF producing the highest prediction accuracy with an R² in the range of 0.250–0.590. …”
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Machine learning-driven discovery of anoikis-related biomarkers in Adult T-Cell Leukemia/Lymphoma subtypes by Mohadeseh Zarei Ghobadi, Elaheh Afsaneh

“…Subsequently, we employed decision trees, random forest, extreme gradient boosting, support vector machine, and logistic regression algorithms to identify classifier genes distinguishing each ATLL subtype from asymptomatic carriers. …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. …”
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