Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
Researchers from King Abdullah University of Science and Technology (KAUST) have developed deepBlastoid, the first deep-learning platform ...
Abstract: Nowcasting plays a critical role in disaster warning systems, and recent advancements in deep learning have shown great potential in improving the accuracy and timeliness of such predictions ...
AI analysis of 20 years of satellite data shows floating macroalgae blooms expanding worldwide, with rapid growth beginning around 2008–2010. While floating algae can support marine life offshore, ...
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 strictly ...
Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...
Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Abstract: The automatic segmentation of liver tumors plays an important role in the diagnosis and treatment of liver cancer. While deep convolutional neural network (DCNN) models are widely used for ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
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