GitHub

Energy Fault Detector - Autoencoder-based Fault Detection for the Future Energy System

Energy Fault Detector is an open-source Python package for automated anomaly detection in operational data from renewable energy systems and power grids. It uses autoencoder-based normal-behaviour ...
GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
Scientific Research Publishing

Variational Auto-Encoder and Speeded-Up Robust Features Hybrid Model for Anomaly Detection ...

Jomo Kenyatta University of Agriculture and Technology, Juja, Kiambu County, Kenya. Where KL denotes the Kullback-Leibler divergence, and p(z) is a prior distribution over the latent space (typically ...
IEEE

Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection

Abstract: 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 ...
IEEE

LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data

Abstract: Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional ...
Frontiers

Subject-invariant feature learning for mTBI identification using LSTM-based variational ...

Developing models for identifying mild traumatic brain injury (mTBI) has often been challenging due to large variations in data from subjects, resulting in difficulties for the mTBI-identification ...
Frontiers

Abnormal ECG detection based on an adversarial autoencoder

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to ...