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 ...

Therefore, this paper proposes a convolutional neural network based on attention mechanism and autoencoder improvement, namely, CBAM-AE-CRF. CBAM AE-CRF integrates the convolutional block attention ...

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have ...

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition Abstract: The self-regulated recognition of human activities from time-series ...

In particular, dropout was applied after each convolutional and LSTM layer using a dropout value of 0.5, and after dense layers using a dropout value of 0.25. Weight regularization was employed in all ...

autoencoder collaborate maximum-entropy-regularization spatio-temporal-modeling lstm-neural-network frame-interpolation video-super-resolution lstm-autoencoder maxent-models generative-ai ...