Researchers have developed an explainable deep learning framework that combines Monte Carlo dropout uncertainty, firefly optimization, and SHAP interpretability to ...
A field-validated system combining drone multispectral imaging, explainable XGBoost AI, and edge-controlled variable-rate application cut nitrogen fertilizer use by 21.8 ...
Brazilian researchers have combined vibration analysis with explainable artificial intelligence to diagnose unbalance, misalignment, and cracks in rotors supported by ...
Researchers have built an explainable, fairness-audited early warning system that predicts online student dropout using only the first weeks of ...
A machine learning analysis of 572 field measurements reveals nonlinear temperature and rainfall thresholds that will determine where Chinese milk ...
Researchers compiled 32,714 landslide records and built a selective ensemble machine learning framework that stabilizes runout distance prediction and reveals ...
A new event-based machine learning approach reframes vineyard mildew forecasting as the prediction of disease-risk episodes rather than daily status ...
A hybrid XGBoost-based machine learning framework has distilled Crohn's disease risk prediction down to just 15 genetic variants, achieving over ...
A new study combining econometrics and machine learning finds that coal is the dominant driver of PM2.5 pollution across G7 ...
Researchers have built a unified physics-informed AI framework that couples fracture mechanics with explainable machine learning to predict corrosion-fatigue life, ...
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