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Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection

Author(s): Gao Z; Chen X; Xu J; Yu R; Zhang H; Yang J;

Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre- ...

Article GUID: 39771685


CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

Author(s): Islam M; Zunair H; Mohammed N;

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relie ...

Article GUID: 38492455


Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks

Author(s): Ghazikhani H; Butler G;

Transmembrane transport proteins (transporters) play a crucial role in the fundamental cellular processes of all organisms by facilitating the transport of hydrophilic substrates across hydrophobic membranes. Despite the availability of numerous membrane protein sequences, their structures and functions remain largely elusive. Recently, natural language p ...

Article GUID: 37497772


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