Each parameterization method combined with structured data and structured data-only were compared for predicting intensive care unit transfer or death in the next 24 hours using deep recurrent neural ...
Abstract: In industry, the automatic recognition of surface defects of flat steel products still represents a real challenge. Indeed, in addition to constraints such as the image noise or blur, there ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
Comparison of Deep-Learning Models for Classification of Cellular Phenotype From Flow Cytometry Data
Abstract: This study compares the relative utility of deep learning models as automated phenotypic classifiers, built with features of peripheral blood cell populations assayed with flow cytometry. We ...
+ The efficacy of deep residual networks is fundamentally predicated on the identity shortcut connection. While this mechanism effectively mitigates the vanishing gradient problem, it imposes a ...
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