High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…However, all models faced challenges in accurately classifying extreme vault categories. Conclusions Classification models, particularly gradient boosting and random forest, demonstrated strong potential for predicting clinically significant vault categories, enabling personalized surgical planning and improved risk management. …”
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Integrating remote sensing, GIS, and machine learning for zoonotic cutaneous leishmaniasis modelling by Fatemeh Parto Dezfooli, Mohammad Javad Valadan Zoej, Fahimeh Youssefi, Ebrahim Ghaderpour

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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

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Digital mapping of soil organic carbon in the hilly and mountainous landscape of Indian Himalayan region employing machine-learning techniques by Justin George Kalambukattu, Suresh Kumar, Bappa Das, Trisha Roy

“…The present study tried to overcome this challenge and mapping of SOC was done at a resolution of 30 m by integrating various machine learning (ML) techniques i.e. random forest regression (RF), support vector regression (SVR) and extreme gradient boosting (XGB).Surface soil samples were strategically collected from 421 georeferenced locations representing the dominant elevation zones, geology and land use land cover (LULC) types to develop spatial models for predicting SOC. …”
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SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…In the study discussed here, different machine learning (ML) algorithms were investigated to foresee construction delays, and these include Gaussian Naïve Bayes, Adaboost, Logistic Regression, Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT) and Extreme Gradient Boosting (XGBoost). …”
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A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting by Shiqiang Zheng, Shuangyi Zhang, Youyi Song, Zhizhe Lin, Dazhi Jiang, Teng Zhou

“…To address these issues, we propose an easy-to-implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short-term traffic flow forecasting. …”
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Electric Vehicle charging station load forecasting with an integrated DeepBoost approach by Joveria Siddiqui, Ubaid Ahmed, Adil Amin, Talal Alharbi, Abdulelah Alharbi, Imran Aziz, Ahsan Raza Khan, Anzar Mahmood

“…The proposed approach consists of Categorical Boosting (CatBoost), Extreme Gradient Boosting (XgBoost), Long Short-Term Memory Network (LSTM) and Linear Regression (LR) models. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…The models evaluated include Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regression (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Lasso, and k-Nearest Neighbors (KNN). …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…On this basis, the Light Gradient Boosting Machine (LightGBM) method is used to predict power during normal weather periods, while a LightGBM-Transformer method is proposed for predicting power losses during such periods. …”
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Interpretable machine learning for precision cognitive aging by Abdoul Jalil Djiberou Mahamadou, Emma A. Rodrigues, Vasily Vakorin, Vasily Vakorin, Violaine Antoine, Sylvain Moreno, Sylvain Moreno

“…The EBM’s performance is compared against Logistic Regression, Support Vector Machines, Random Forests, Multilayer Perceptron, and Extreme Gradient Boosting, evaluating predictive accuracy and interpretability.ResultsThe findings reveal that EBM provides valuable insights into cognitive aging, surpassing traditional models while maintaining competitive accuracy with more complex machine learning approaches. …”
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Machine learning prediction of early reoperation following lower extremity tumor resection and endoprosthetic reconstruction: A PARITY trial secondary analysis by Nicole J. Newman-Hung, Akash A. Shah, Joseph K. Kendal, Nicholas M. Bernthal, Lauren E. Wessel, on behalf of the PARITY Investigators

“…Logistic regression (LR), Random Forest, gradient boosting, AdaBoost, and XGBoost were used for model building. …”
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EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19 by Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

“…We evaluate the interaction of these methods with Support Vector Machines (SVM), Logistic Regression (LR), and eXtreme Gradient Boosting (XGBoost) for COVID-19 prediction. …”

A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

“…This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. …”
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Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR by Jiuen Xu, Yinyin Zhao, Xuejian Li, Lujin Lv, Jiacong Yu, Meixuan Song, Lei Huang, Fangjie Mao, Huaqiang Du

“…Then, three types of models—statistical model multiple linear regression (MLR), traditional machine learning models including K-nearest neighbor regression and support vector regression, and ensemble learning models including random forest, extreme gradient boosting, and categorical boosting (CatBoost)—were employed for DBH inversion. …”
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Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms by Binglin Song, Ping Liu, Kangrui Fu, Chun Liu

“…Subsequently, 10 ML models were developed: Random Forest, Logistic Regression, Gradient Boosting Machine, Neural Network, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor, Adaptive Boosting, Light Gradient Boosting Machine, Category Boosting, and Support Vector Machine. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Recursive feature elimination and extreme gradient boosting were used to rank and screen the importance of patient features and reduce the dimensionality of the features. …”
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Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers by Yi Lyu, Quan-Cheng Jiang, Shuai Yuan, Jing Hong, Chun-Feng Chen, Hai-Mei Wu, Yi-Qin Wang, Yu-Jing Shi, Hai-Xia Yan, Jin Xu

“…The top-performing model, Extreme Gradient Boosting, was further assessed through ten-fold cross-validation, external validation, and feature analysis using SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations. …”
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Predicting neonatal mortality using ensemble machine learning algorithms in the case of Ethiopian Rural Areas by Melaku Alelign Mengstie, Misganaw Telake Telele

“…Several ensemble machine-learning algorithms, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and CatBoost, were applied to build the model. …”
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