Fungal endophytes boost salt tolerance and seed quality in quinoa ecotypes along a latitudinal gradient by Roberto Miño, Roberto Miño, Gabriel I. Ballesteros, Gabriel I. Ballesteros, Karina B. Ruiz, Ian S. Acuña-Rodríguez, Ian S. Acuña-Rodríguez, Marco A. Molina-Montenegro, Marco A. Molina-Montenegro, Marco A. Molina-Montenegro

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Machine Learning Prediction of CO₂ Diffusion in Brine: Model Development and Salinity Influence Under Reservoir Conditions by Qaiser Khan, Peyman Pourafshary, Fahimeh Hadavimoghaddam, Reza Khoramian

“…The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. …”
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A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation by Yinling Wang, Lei Yu, Mazhar Ali, Imran Ali Khan, Tahir Maqsood, Haining Gao, Qi Wang, Xiaolei Guo

“…Indeed, a hybrid model was developed combining CFD and ML for the first time to predict temperature distribution versus special coordinates in a photovoltaic thermal system. Three advanced machine learning models, i.e., Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB) were applied to analyze and predict T in system. …”
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Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces by Mbah O. M., Madueke C. I., Umunakwe R., Agba M. N.

“…In this study, Extreme Gradient Boosting (XGBoost) model was developed using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily global solar radiation on tilted surfaces. …”
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Gradient Boosted Trees Predictive Models for Surface Roughness in High-Speed Milling in the Steel and Aluminum Metalworking Industry by Victor Flores, Brian Keith

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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…The study introduces an Extreme Gradient Boosting Regression (XGBR) model optimized with three distinct metaheuristic algorithms: Battle Royal Optimization (BRO), Moth Flame Optimization (MFO), and Artificial Bee Colony (ABC). …”
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Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI by Nisreen Innab, Shtwai Alsubai, Ebtisam Abdullah Alabdulqader, Aisha Ahmed Alarfaj, Muhammad Umer, Silvia Trelova, Imran Ashraf

“…The proposed methodology makes use of data upsampled using the synthetic minority oversampling technique (SMOTE) to handle the class imbalance problem that is very crucial in medical diagnosing with a light gradient boosting machine. The results show that the proposed model gives 0.9989 accuracy, 0.9988 area under the curve, 0.9832 recall, 0.9834 precision, 0.9832 F1 score, 0.9748 Kappa score, and 0.9749 Matthews correlation coefficient value on the test dataset. …”
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Target Threat Assessment Method for UUVs Based on EBM by Shuwei LIU, Jianqing CHENG, Kai LIU

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Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms by Wanicha Tepakhan, Wisarut Srisintorn, Tipparat Penglong, Pirun Saelue

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Optimized Gradient Boosting Models for Adaptive Prediction of Uniaxial Compressive Strength in Carbonate Rocks Using Drilling Data by Stephen Adjei, Ahmed Gowida, Salaheldin Elkatatny, John Ojuu Oleka

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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

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PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE by Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry

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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

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Evaluation of net-zero materials in mortar bricks with predictive modelling using random forest and gradient boosting techniques by Golla Uday kiran, Nakkeeran Ganesan, Dipankar Roy, Sumant Nivarutti Shinde, Mamillapalli Indumathi, George Uwadiegwu Alaneme, Val Hyginus Udoka Eze, Kuzmin Anton

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Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data by Miriam Zambudio Martínez, Larissa Haringer Martins da Silveira, Rafael Marin-Perez, Antonio Fernando Skarmeta Gomez

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Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting by Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed

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Testing an inverse modeling approach with gradient boosting regression for stroke volume estimation using patient thermodilution data by Vasiliki (Vicky) Bikia, Dionysios Adamopoulos, Dionysios Adamopoulos, Dionysios Adamopoulos, Marco Roffi, Marco Roffi, Georgios Rovas, Stéphane Noble, Stéphane Noble, François Mach, François Mach, Nikolaos Stergiopulos

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A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications by Matthew Wiens, Alissa Verone‐Boyle, Nick Henscheid, Jagdeep T. Podichetty, Jackson Burton

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Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis by Jingtai Ma, Jingtai Ma, Yiting Fang, Yiting Fang, Shiqi Li, Shiqi Li, Lilian Zeng, Siyi Chen, Zhifeng Li, Guiyuan Ji, Xingfen Yang, Wei Wu, Wei Wu

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Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score by Matthew M. Churpek, MD, MPH, PhD, ATSF, Kyle A. Carey, MPH, Ashley Snyder, MPH, Christopher J Winslow, MD, Emily Gilbert, MD, Nirav S Shah, MD, MPH, Brian W. Patterson, MD, MPH, Majid Afshar, MD, MSCR, Alan Weiss, MD, MBA, Devendra N. Amin, MD, Deborah J. Rhodes, MD, Dana P. Edelson, MD, MS

“…The objective of our multicenter retrospective and prospective observational study was to develop and prospectively validate a gradient-boosted machine model (eCARTv5) for identifying clinical deterioration on the wards. …”
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