Hybrid AI Framework Targets Urban Flood Risk and Green Infrastructure Planning
A new hybrid machine learning framework, GIStorm-Net, combines XGBoost, graph neural networks and optimization algorithms to predict urban flood risk ...
A new hybrid machine learning framework, GIStorm-Net, combines XGBoost, graph neural networks and optimization algorithms to predict urban flood risk ...
A machine learning study of 14,949 students across 40-plus countries found self-testing habits, not AI usage frequency, were the strongest ...
A multi-cohort study reports moderate discrimination for predicting incident disability in older adults using a seven-predictor machine learning model validated ...
An optimized XGBoost model predicts bioavailable lead in farmland soils, identifying the acid-soluble lead fraction and soil properties as the ...
A new hybrid explainable machine learning framework combines satellite environmental data with health records to predict cardiovascular disease risk at ...
A study in Watauga County, North Carolina, shows that blending three freely available 30-meter elevation models with machine learning can ...
Spatial features extracted from routine H&E slides of 471 normal prostate tissue samples significantly improved gene expression models for 2,330 ...
A study of Kenya's prepaid electricity grid found that XGBoost only modestly outperformed simple regression, with recent consumption history dominating ...
Researchers have built an explainable stacking machine learning framework that jointly forecasts electrical load and predicts transformer remaining lifespan in ...
A retrospective two-centre study found that a habitat radiomic model based on contrast-enhanced CT distinguished heavy from light central nodal ...
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© 2026 Scienmag - Science Magazine