Researchers evaluated an artificial intelligence framework combining hyperspectral imaging and infrared spectroscopy to accurately identify expired medicines across six pharmaceutical ...
A new artificial intelligence framework improves connected vehicle predictive maintenance by fusing internal diagnostic telemetry with external environmental and behavioural ...
Researchers developed a physics-guided uncertainty calibration framework that significantly improves probabilistic forecast accuracy for heavy and extreme rainfall regimes while ...
A hybrid stacking ensemble of XGBoost, LightGBM and CatBoost with domain-informed features predicts concrete compressive strength with an R² of ...
Computer scientists have developed a hybrid machine learning pipeline that accurately predicts secondary testosterone deficiency using six routine clinical and ...
A study analyzing 348,404 charging sessions in Jiaxing, China, demonstrates that machine learning forecasts and an offline decision twin reduce ...
A comparative study of seven uncertainty-aware machine learning frameworks on real-world cargo EV data shows trade-offs between prediction accuracy, interval ...
A condition-stratified study of westbound Interstate I-94 found that XGBoost without weather variables delivered the most accurate hour-ahead traffic forecasts.
A machine learning model built on eight material descriptors predicted the specific capacity of nitrogen-doped porous carbon cathodes for zinc-ion ...
A dual-track AI framework reads Amazon reviews with DeBERTa-v3 encoders and aspect-specific attention, assigning sentiment to product, delivery, and service ...
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