DeepAnT [36], a convolutional autoencoder that forecasts future time points and flags deviations as anomalies, has been effectively used for IoT monitoring across multiple deployments. Kara et al. [46 ...

Furthermore, they encounter challenges like over-smoothing and the inability to capture deep correlations. To overcome these limitations, a novel deep space-time generative graph convolutional ...

2. The Overall Framework The stacked convolutional autoencoder with fusion selection kernel attention mechanism is an unsupervised deep network that generates advanced feature representations.

I would like to kindly request if you could consider adding a training pipeline for the DCAE (Deep Compression Autoencoder). This addition could greatly benefit the research community and streamline ...

To implement feature extraction and noise reduction of vibration signals, this article proposes a novel network, that is, deep morphological shrinkage convolutional autoencoder (DMSCAE) for gearbox ...

Convolutional autoencoder, domain adaptation, and shallow classifiers. We first separately applies NMF on MIMIC and CHOA data for feature dimensionality reduction, then used two separate CAE models to ...

In this paper, through the experimental comparison of multi-layer perceptron, convolutional neural network, and the proposed convolutional autoencoder, we find that the constructed convolutional ...