Abstract: Anomaly detection for time-series data has been viewed widely in many practical applications and caused lots of research interests. A popular solution based on deep learning techniques is ...

Abstract: This study focuses on the anomaly detection problem in Network Security Situational Awareness (NSSA). We systematically review traditional approaches and recent advancements based on Machine ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

A complete end-to-end Streaming Data Analytics (SDA) project that generates real-time weather data, applies SDA filters (Moving Average, EWMA), detects anomalies using Isolation Forest, and visualizes ...

Information and communication technology (ICT) is crucial for maintaining efficient communications, enhancing processes, and enabling digital transformation. As ICT becomes increasingly significant in ...

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 ...

The Python team at Microsoft is continuing its overhaul of environment management in Visual Studio Code, with the August 2025 release advancing the controlled rollout of the new Python Environments ...

Introduction: Recent advances in artificial intelligence have created opportunities for medical anomaly detection through multimodal learning frameworks. However, traditional systems struggle to ...

TDAAD is a Python package for unsupervised anomaly detection in multivariate time series using Topological Data Analysis (TDA). Website and documentation: https://irt-systemx.github.io/tdaad/ ...