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 ...

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 ...

Scientists complete largest wiring diagram and functional map of the brain to date The MICrONS Project is considered the most complicated neuroscience experiment ever attempted Date: April 9, 2025 ...

AI creates anti-cancer molecules for proteins that previously could not be targeted pharmacologically by leveraging quantum computing.

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 ...

It employs an autoencoder structure with diffusion transformer layers placed between the encoder and decoder. Oasis processes user keyboard inputs and generates gameplay in an autoregressive fashion.

Researchers at the University of Texas at Austin produced data-driven and hybrid data-physics based DIP models to analyze stuck pipe incidents at the Frontier Observatory for ...

To address these problems, we propose the Improved AutoEncoder with LSTM module and Kullback–Leibler divergence (IAE-LSTM-KL) model in this article. An LSTM network is added after the encoder to ...