Introduction This project focuses on fault detection in turbofan engines using the NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset, specifically the DS02 subset. The system ...

The methodology integrates supervised (Random Forest), unsupervised (Isolation Forest), and deep learning (LSTM autoencoder) techniques, leveraging federated learning for edge deployment and ...

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

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 ...

This research investigates the application of a novel LSTM Convolutional Autoencoder (LSTM-CAE) framework for video anomaly detection, utilizing the UCSD Anomaly Detection Dataset. Traditional methods ...

In this paper, we present StepEncog, a convolutional LSTM autoencoder model trained on fMRI voxels. The model can predict the entire brain volume rather than a small subset of voxels, as presented in ...