The model used for this task was an LSTM Autoencoder. LSTM is a Neural Network capable of modeling short and long term dependanceies in data, therefore its use for time series data is justified. The ...

Intelligent condition monitoring and anomaly detection approaches have become a crucial key for improving safety and reliability of Renewable Energy Systems (RES). However, many challenges arise when ...

Abstract: Electrocardiogram (ECG) signals are central to cardiac health assessment but interpreting them accurately requires expertise. Traditional methods often lack interpretability, posing ...

Anomaly detection, or outlier detection, is the identification of data points, observations, or events that do not conform to expected patterns of a given group. Anomalies or outliers occur very ...

Authors: Sayan Hazra & Sankalpa Chowdhury LSTM autoencoder based anomaly detection using Keras and Tensorflow backend. Here in this project we have done a comparative study between Simple LSTM Network ...