A hybrid stacking ensemble framework combining physics-based driver models and deep neural networks improves car-following acceleration predictions on snowy highways.
A retrospective cohort study found that late-phase urine output variability over 72 hours predicted 28-day renal recovery in continuous kidney ...
Researchers evaluated three gradient boosting algorithms on six years of epidemiological and climatic data from southeastern Senegal, identifying an optimized ...
A retrospective study developed an XGBoost model using routine laboratory tests to predict recurrence risk in lung adenocarcinoma patients treated ...
A comparative evaluation in Bangladesh's Barishal District demonstrated that machine learning algorithms, led by Random Forest, significantly surpass conventional statistical ...
A new machine learning framework using routine clinical variables predicts medium-term type 2 diabetes risk in patients with histologically confirmed ...
A study in Energy Informatics shows that incorporating temporal load-history information significantly improves the accuracy of short-term HVAC load forecasting ...
A new machine learning study predicts that high-emission climate scenarios will expand debris flow susceptibility zones in Qinghai Province, China, ...
A hybrid stacking ensemble of XGBoost, LightGBM and CatBoost with domain-informed features predicts concrete compressive strength with an R² of ...
An interpretable XGBoost-SHAP analysis in Hebei Province, China, shows that large, continuous blue-green patches dominate carbon sequestration, with distinct thresholds ...
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