Tag: transformers

Researchers have developed a unified multimodal learning framework designed to detect mental stress by integrating physiological data from wearable sensors ...

Researchers developed Pepti-DrugPhAI, a multimodal algorithm that evaluates peptide pharmacokinetics and toxicity to improve drug candidate selection.

Researchers adapted the BEATs audio transformer framework to identify pig distress vocalizations in commercial barn recordings, achieving 94.5 percent accuracy ...

A narrative review in Discover Artificial Intelligence maps the evidence gaps for hybrid CNN-ViT architectures in mammography, highlighting the disparity ...

A hybrid deep learning model combining convolutional autoencoders, transformer self-attention, and recurrent networks predicts concrete compressive strength from microscopic imagery ...

A systematic review of 163 studies traces the progression of vision transformers and highlights where convolutional networks remain competitive.

Researchers evaluate cross-view generalization in pose-based fall detection, demonstrating that sequential Transformers and coordinate augmentations enhance robustness across unseen camera ...

Researchers have developed an integrated computational framework combining multitask Transformer modeling with policy-constrained runtime orchestration and structured audit packages to ...

Researchers have developed a hybrid deep learning pipeline that automates character transcription and diacritic restoration for fragile historical Tamil palm ...

Researchers developed IBformer, an architecture that introduces inductive bias and specialized enhancement modules to tackle spatial and temporal heterogeneity across ...