Machine learning ranks building damage after Nepal’s deadly 2015 Gorkha earthquake
A study of 762,106 buildings damaged in Nepal's 2015 Gorkha earthquake combines an MLP classifier with imbalance correction, sensitivity analysis, ...
A study of 762,106 buildings damaged in Nepal's 2015 Gorkha earthquake combines an MLP classifier with imbalance correction, sensitivity analysis, ...
A new study combines machine learning and spatial explainable AI to predict surface urban heat island intensity across the Mumbai ...
A self-supervised CNN framework with Grad-CAM heatmaps reached up to 94.38% validation accuracy on a 2,137-image cotton leaf disease dataset.
A systematic review finds hybrid CNN-transformer models lead stroke lesion analysis, but explainable AI appears in only 11 percent of ...
A new review argues that deep learning skin lesion analysis must prove itself through external validation, fairness and clinical utility ...
Researchers built a dual-stream deep learning framework that localizes and classifies facial emotions and binary gender labels together, achieving 94.56% ...
Random Forest and XGBoost models predicted loss to follow-up among ART patients in Zimbabwe with high internal accuracy, while LIME ...
A systematic review of 177 peer-reviewed studies synthesizes the shift from Industry 4.0 to human-centric, sustainable and resilient Industry 5.0 ...
A new explainable AI framework for network intrusion detection audited its semantic rule layer on held-out benchmark data, reporting high ...
A structured narrative review finds that explainable AI techniques may improve transparency in diagnosing skin-manifesting neglected tropical diseases, while deep ...
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© 2025 Scienmag - Science Magazine