The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

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This project implements a real-time anomaly detection system using an LSTM Autoencoder to analyze continuous data streams. The system is designed to detect unusual patterns, such as exceptional values ...

Keywords: protein system, conformational space, variational autoencoder, molecular dynamics, deep learning Citation: Tian H, Jiang X, Trozzi F, Xiao S, Larson EC and Tao P (2021) Explore Protein ...

LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder.

The aim of this work was to determine whether the kinematics of lower limb trajectories during walking could be reliably extrapolated using LSTM autoencoder neural networks. It was hypothesised that ...