Researchers used Differential Evolution-optimized machine learning to predict the compressive strength of fly ash and waste ...
Researchers trained an explainable XGBoost model on 1,670 field-recorded concrete mixes, achieving R-squared 0.9263 in ...
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 ...
Utilizing machine learning to assess distinct depressive symptoms improves the identification of adults with suicidal ideation.
Optimized AI models could help estimate concrete compressive strength in wastewater environments and support future ...
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 ...
A study developed ultrasound radiomics models to distinguish DKD from NDKD in patients with Type 2 diabetes. An integrated ...
Machine learning models showed moderate performance in predicting acute and late radiation-induced skin reactions in rectal cancer.
A machine learning model utilizing longitudinal electronic diary data can accurately forecast the likelihood of next-day migraine attacks.
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