Researchers have developed a novel AI-driven framework using the XGBoost algorithm to accurately evaluate the skid resistance of asphalt pavements under various conditions. Published in Smart ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Engineered nanozymes and explainable machine learning enable sensitive bacterial detection across complex conditions. The system uses three distinct signals and delivers transparent, verifiable ...
A mother's health during pregnancy, childbirth and the postpartum period is the foundation of lifelong well-being, directly influencing a child's development and long-term outcomes, yet most ...
Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
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