[CIKM 2021] A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning".

Anomaly detection method that incorporates multi-scale features to sparse coding

Semi-supervised anomaly detection method

Detects anomalous resting heart rate from smartwatch data.

This project provides a time series anomaly detection algorithm based on the dynamic threshold generation model.

Several examples of anomaly detection algorithms for time series data.

An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.

Uses LSTM-based autoencoders to detect abnormal resting heart rate during the coronavirus (SARS-CoV-2) infectious period using the wearables data.

The paper "Deep Graph Level Anomaly Detection with Contrastive Learning" has been accepted by Scientific Reports Journal.

OCR to detect and recognize dot-matrix text written with inkjet-printed on medical PVC bag

Anomaly detection algorithm for time series based on the dynamic threshold generation model

an end to end anomaly intrusion base on deep learn

Intelligent SAP Financial Integrity Monitor (POC): Hybrid AI/ML (IF, LOF, AE) & rules-based anomaly detection on SAP FAGLFLEXA data using Python/Streamlit