Complementarily, another framework integrates a heterogeneous graph attention network with a deep sparse autoencoder to extract both semantic and functional similarities from complex datasets.

[4] CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network. Scientific Reports (2024).

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors ...

The integration of electromyography (EMG) signals into biometric recognition has garnered significant attention due to their potential for highly secure and reliable identification. Unlike ...

Adaptive beamforming plays an increasingly vital role in various applications, including radar, sonar, telecommunications, and many other related fields. In this paper, we present an adaptive ...

Deep Sparse Representation-based Classificatio (DSRC) is a transductive classification model based on sparse representations. DSRC is based on convolutional autoencoders. In particular, its network ...

Purpose: To evaluate the diagnostic accuracy of a deep learning autoencoder-based model utilizing regions of interest (ROI) from optical coherence tomography (OCT) texture enface images for detecting ...