There are 1 repository under anomaly-detection-models topic.
Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.
Detect anomalies in network traffic data using Federated Machine Learning technique.
Transformer-based Models for Unsupervised Anomaly Segmentation in Brain MR Images
EfficientNetV2 based PaDiM
Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution
I was unfortunate to contract COVID-19 during the second wave in India. Time-series graphs, denoting the caseload were omnipresent in this period. I found that time series analysis resonated with me since it used mathematical equations to understand and give meaning to perpetual events. Under the guidance of Professor Supratim Biswas, at IIT Bombay
Applications of AI for Anomaly Detection: Instructor-led training from the NVIDIA Deep Learning Institute (DLI)
Undergrad paper on Anomaly-based Network Intrusion Detection Systems
[work-in-progress] Convolutional neural network for anomaly detection on large road networks
Online anomaly detectors suitable for the detection of point and contextual anomalies on time series data streams.
This is the complementary repository for "Payment fraud identification by means of business process anomaly detection", the master thesis of Keyvan Amiri Elyasi submitted at the Junior Professorship of Management Analytics, University of Mannheim (July 2022)
Report on Anomaly Detection Methods for Data Streams
Progetto per l'esame del corso di Fondamenti di Intelligenza Artificiale
Built a few anomaly detection models to determine the anomalies from the data