CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...
Introduction: This study aimed to develop an automated approach for assessing upper limb (UL) motor impairment severity in stroke patients using a deep learning framework applied to resting-state ...
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 ...
A few years ago, I followed the life of a cheese curd, from milk on a dairy farm to fresh cheese in Ellsworth, Wis., to deep-fried glory at the Fair. On the Fairgrounds, though, there are plenty of ...
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AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks
Abstract: Graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have demonstrated remarkable success in modeling graph-structured data across diverse applications. A critical ...
Abstract: Recent advances in deep learning have significantly improved hyperspectral image (HSI) classification. However, deep learning models for HSI classification typically rely on one-hot labels, ...
Introduction: Mild cognitive impairment (MCI), often linked to early neurodegeneration, is associated with subtle disruptions in brain connectivity. In this paper, the applicability of persistent ...
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