Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis by Jianmin Dong

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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

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Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model by Weichong Zhou, Xingfu Liao, Hui Shi, Mingfeng Wang, Yunchong Xiao, Xilu Yu, Yilong Wu, YanYi Liu, Yin Peng, Hai Su

Subjects: “…The XGBoost algorithm (extreme gradient boosting)…”
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Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study by Jalal Hatem Hussein Bayati, Abid Mahboob, Laiba Amin, Muhammad Waheed Rasheed, Abdu Alameri

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Flood susceptibility mapping using supervised machine learning models: insights into predictors’ significance and models’ performance by Zahid Ur Rahman, Meimei Zhang, Fang Chen, Safi Ullah, Mansoor Ahmad, Aamir Feroz, Samir Shehu Danhassan, Abdullah Azzam

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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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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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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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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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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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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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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

“…The models assessed blanketed Support Vector Machines (SVM), K-Nearest Neighbor (KNN), AdaBoost, Gradient Boosting, Random Forest, Gaussian Naive Bayes, Logistic Regression, Extreme Gradient Boosting (XG boost), and Decision tree. …”
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Multidimensional geographic factors behind conflicts: a case study in Sudan by Yu Gong, Xi Li, Samir Belabbes, Luca Dell'Oro, Yuanxi Ru

“…The results indicate that eXtreme Gradient Boosting model outperforms other models, such as Categorical Boosting and Light Gradient Boosting Machine. …”
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BBDetector: Intelligent border binary detection in IoT device firmware based on a multidimensional feature model. by Shudan Yue, Guimin Zhang, Qingbao Li, Wenbo Zhang, Xiaonan Li, Weihua Jiao

“…This method involves ensemble learning, combining extreme gradient boosting, light gradient boosting machine, and categorical boosting as base learners with random forest as the meta-learner. …”
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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning by Mohamed Kamel Elshaarawy, Martina Zeleňáková, Asaad M. Armanuos

“…Abstract This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). …”
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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

“…Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.…”
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Latent Space Classification for Cardiovascular Disease Detection: A Deep Convolutional Autoencoder-Based Approach for Telemedicine Applications by G Antoni Gracy, Sheena Christabel Pravin

“…The compressed features are classified using seven ML models: K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Histogram Gradient Boosting Trees (HGBT), and three Support Vector Machine variants (SVML, SVMP, SVMR). …”
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