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

Semi-supervised anomaly detection method

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

Detects anomalous resting heart rate from smartwatch data.

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

The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

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.

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

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

DecompositionUMAP: A multi-scale framework for pattern analysis and anomoly detection

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

an end to end anomaly intrusion base on deep learn

Advanced anomaly detection system using graph neural networks and time series analysis to identify fraudulent transactions, money laundering patterns, and market manipulation in real-time financial data streams.

anomaly detection in mobility data

Advanced anomaly detection using topological data analysis and manifold learning.

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

Preprint + minimal, reproducible code/data for the Structure–Randomness Transfer Theorem (SRTT); headless runner, HTML report, CI, Pages.