Background Coronary artery disease (CAD) is linked to an increased risk of mild cognitive impairment (MCI). Effective and ...

The field of medical diagnostics and clinical practice is being transformed by the rapid advancement of deep learning ...

The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the ...

such as masked autoencoder (MAE) reconstruction and contrastive learning, offer a promising solution by reducing reliance on labeled data. Nonetheless, transformer-based MAEs are computationally ...

Using the time series signal of pipeline cracks as the original dataset, local features of the original dataset are extracted through one-dimensional convolutional neural network, and the global ...

This repository contains the official implementation of the paper "CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning ... feature extraction from ...

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In a study published in Nature Communications, researchers at the University of Wisconsin–Madison introduced a deep learning ...

A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the ...