A deep learning framework using ConvNeXt-Large accurately identified ten brain PET radiotracers and three biological targets directly from image appearance ...
A new explainable machine learning framework uses Integrated Gradients and automated capability extraction to identify and validate the structural and ...
Researchers have developed an integrated computational framework combining multitask Transformer modeling with policy-constrained runtime orchestration and structured audit packages to ...
A systematic study reveals that gradient-based adversarial attacks can significantly distort explainable AI feature rankings in intrusion and malware detection ...
A new neural framework shows that AI-generated text detectors scoring near-perfectly on familiar benchmarks can collapse to an F1 of ...
A multi-method explainable artificial intelligence study reveals how hybrid forecasting models rely on short-term residual dynamics to predict data center ...
A condition-stratified study of westbound Interstate I-94 found that XGBoost without weather variables delivered the most accurate hour-ahead traffic forecasts.
A proof-of-concept deep learning framework called CardioHolisticAI combines clinical, radiomic and genomic data to stratify coronary artery disease risk, achieving ...
An explainable Kolmogorov-Arnold Network optimised by the Marine Predators Algorithm predicted six mechanical and durability properties of PET- and rice-husk-ash-blended ...
An explainable transformer model fusing MRI with texture descriptors reached over 99 percent accuracy in Alzheimer's disease staging benchmarks, with ...
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