A new multi-task AI framework fuses raw waveforms, spectral features, and statistics through bidirectional cross-modal attention to detect heart murmurs,...

Researchers in Qinghai have developed MHCNet, a hypergraph-based multimodal AI that classifies Tibetan medicinal plants with 98.11 percent accuracy by...

A systematic review in Artificial Intelligence Review maps six methodological families of time series one-class classification and charts the challenges...

A new transformer-based framework called THIMDA fuses multi-modal microRNA and disease representations to predict disease associations with high accuracy.

A new study in Discover Chemistry shows how artificial intelligence, machine learning, and computational chemistry can predict the toxicity of...

A new explainable deep learning framework segments marine biofouling at the pixel level, improving underwater inspection accuracy and structural health...

Researchers report an explainable dual-path attention convolution network that classifies emotions in speech with 98.6% accuracy on the RAVDESS benchmark...

A new special issue in Medical & Biological Engineering & Computing showcases how deep learning is transforming ultrasound image segmentation,...

Researchers combined statistics, machine learning, and GIS to predict water quality in Dhaka's heavily polluted Turag River with over 90...

Researchers have built a geometry problem solver that turns diagrams into heterogeneous graphs and aligns them with text, outperforming existing...