Machine learning approaches are a powerful way to address this challenge, but they are usually tailored to only work on one specific sensor. This work addresses the challenge of transferability of ...

1 College of Information Engineering, Xinchuang Software Industry Base, Yancheng Teachers University, Yancheng, China. 2 Yancheng Agricultural College, Yancheng, China. Convolutional auto-encoders ...

Out of these three, the innovations in network architecture ... process only the visible parts. Moreover, the transformer decoder part in the masked autoencoder is replaced with a single ConvNeXt ...

First, they only ... Table 2 (Sun et al., 2009). We reconstruct these regions to increase the attention to these regions and hence, increase the performance of disease classification. In the proposed ...

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and ...

This article will endeavor to demystify autoencoders, explaining the architecture of autoencoders and their applications. Autoencoders are neural networks. Neural networks are composed of multiple ...