Accurate prediction of user actions is essential for optimizing digital platform workflows, enabling proactive recommendations, resource prefetching, and intelligent user assistance. Traditional Markov chain-based methods, though widely used for mode... read more
Accurate segmentation of anatomical structures in cardiac magnetic resonance imaging (MRI) plays an irreplaceable role in the clinical management of cardiovascular diseases, serving as a cornerstone for precise diagnosis, individualized treatment pla... read more
Artificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists' burden and enhance healthcare delivery, yet adoption remains challenging. This study identified key facilitators and barriers to AI ado... read more
Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or rando... read more
BACKGROUND: Artificial intelligence enhances pathology screening efficiency, yet clinical adoption remains limited because most systems operate as opaque black boxes. We aim to resolve this opacity by establishing a framework that generates transpare... read more
The research examines how RL and DRL models can be used to enhance the prediction of maintenance needs in the IIoT setting. The purpose is to assess the accuracy, precision, recall, F1 score and the AUC-ROC of adaptive models against non-adaptive mod... read more
Early and reliable detection of smoke and fire is essential for minimizing damage and ensuring public safety in smart city environments; however, existing vision-based approaches often suffer from limited contextual understanding, high false-alarm ra... read more
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