Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

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, ...
IEEE

Pattern Learning for Trajectory Anomaly Detection: Recent Developments and Emerging Challenges

Trajectory anomaly detection is becoming increasingly important in various fields such as smart cities, autonomous vehicles, and video surveillance. In recent years, pattern learning-based trajectory ...
Scientific Research Publishing

Li, M., Shu, M. and Lu, T. (2024) Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks. Journal of Industrial Engineering and Applied Science, 2, 77-85.

ABSTRACT: 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 ...
Semiconductor Engineering

New Frontiers In Fault Detection And Classification

IC manufacturers are increasingly relying on intelligent data processing to prevent downtime, improve yields, and reduce scrap. They are integrating that with fault detection and classification (FDC) ...
Frontiers

Anomaly Detection in Adolescent Physical Activity Patterns to Assess Health and Behavioral Risks

College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China The final, formatted version of the article will be published soon. This study proposes a ...
Clean Tech

The Link Between Emissions Patterns and Early Threat Detection: Latest Research

Tracking emissions is essential for identifying potential risks, including public health concerns relating to greenhouse gases, industrial safety hazards, and ecosystem instability. Following advances ...
GitHub

NodeSentry: Effective Node-Level Anomaly Detection in HPC Systems via Coarse-Grained Clustering and Fine-Grained Model Sharing

The framework encompasses two principal phases: the offline model training phase and the online anomaly detection phase. During the offline model training phase, we first preprocess the raw MTS. We ...
Cloud Security Alliance

Strengthening Cybersecurity with Real-Time Vulnerability Analysis and Anomaly Detection

In the fast-changing digital world, cybersecurity threats create substantial risks for organizations globally. Proactive defense is vital to counter these challenges. Real-time vulnerability detection ...
IEEE

System Log Anomaly Detection with Noise-Contrastive Learning and Pattern Feature

Abstract: System logs play a critical role in identifying security threats such as network attacks, unauthorized access, and system vulnerabilities. Recent research has focused on extracting sequences ...
Crowdfund Insider

EBA CLEARING Announces RT1 and STEP2 Participants to Adopt Fraud Pattern and Anomaly Detection Verification of Payee Solution

EBA CLEARING announced that over 55 RT1 and STEP2 Participants across 14 countries are preparing to adopt its Fraud Pattern and Anomaly Detection (FPAD) Verification of Payee (VOP) solution to deliver ...
Forbes

AI Applications In Fraud Detection In The Banking Industry

Kinil Doshi is a Senior VP at Citibank and a fintech expert in banking compliance and risk management with two decades of experience. In this article, I want to explore AI applications in fraud ...