A systematic benchmarking study on a curated BRISC 2025 dataset demonstrated that deep model optimization and complete pretraining configurations strongly ...
A prospective study using deep-learning continuous acoustic monitoring revealed that preterm infant bowel sounds undergo a nonlinear developmental evolution with ...
A dual-stream deep learning architecture combining hierarchical taxonomic priors with attention-guided augmentation improved mosquito species classification precision to 84.6 percent ...
Engineers have developed a multimodal artificial intelligence framework combining synchronized computer vision and sensor data to accurately localize cracks and ...
A comprehensive scientific review examines how machine learning algorithms accelerate the synthesis, structural characterization, and property prediction of multielement nanoparticles ...
Researchers have applied convolutional neural networks to direct laser writing to predict and pre-correct manufacturing deviations prior to printing.
A systematic review of 54 studies indicates that multimodal artificial intelligence models achieve high accuracy identifying Alzheimer's disease but face ...
A hybrid AI framework combining LeViT and ResNet architectures outperformed existing models in staging liver fibrosis from ultrasound images.
A dual-branch neural network combining residual and multi-scale feature extraction with gradient-based visual explanations achieves over 99 percent accuracy in ...
A dual-timescale spatiotemporal neural network improves lithium-ion battery state-of-health estimation accuracy by combining intra-cycle signal patterns and inter-cycle degradation trajectories.
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