AI Learns to Prove Itself: Verified Deep Learning for Injury Detection
A new CNN–LSTM–attention framework combines SHAP explainability and Marabou formal verification to deliver reliable, interpretable injury risk prediction from physiological ...
A new CNN–LSTM–attention framework combines SHAP explainability and Marabou formal verification to deliver reliable, interpretable injury risk prediction from physiological ...
Machine learning analysis of oral rinse samples from The Maastricht Study identified the protein REG1A as a non-invasive marker of ...
A new study combines graph neural networks with explainable AI to map how Laotian migrant workers in Bangkok build and ...
Researchers built an explainable random survival forest model that predicts overall and cancer-specific survival in postoperative gastrointestinal stromal tumor patients, ...
Researchers have developed a machine learning framework that uses an experimental database of 236 cyclically tested beam-column connections to identify ...
A prospective study shows that a random forest model combining shear wave elastography, contrast-enhanced ultrasound and clinical factors can predict ...
Researchers trained six machine learning models on 2,048 laboratory records and found gradient boosting predicts the compressive strength of cement-free ...
A systematic review of 237 studies from 2014 to 2024 maps how explainable AI techniques such as SHAP and LIME ...
A lightweight vision transformer trained on drone hyperspectral imagery detected grapevine red blotch virus across Napa Valley vineyards with 75 ...
A comparative study of Taiwan's Mingde and Shihmen reservoirs shows machine learning models forecast monthly storage with high skill but ...
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© 2026 Scienmag - Science Magazine