AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...

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, ...

5.1 RQ1: How does our proposed anomaly detection model perform compared to the baselines? 5.2 RQ2: How much does the sequential and temporal information within log sequences affect anomaly detection?

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Abstract: This study explores AI-powered anomaly detection to secure academic digital library access via Virtual Private Networks (VPNs). A three-model framework One-Class SVM, Isolation Forest, and ...

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 ...

SAN DIEGO — Darwin, a python involved in the "Read to a Reptile" program at the Ocean Beach Library was found on Friday after disappearing two days ago. "I was really getting very worried. I wasn't ...

Cisco introduced Splunk Enterprise Security Essentials Edition and Splunk Enterprise Security Premier Edition, providing customers two agentic AI-powered SecOps options that unify security workflows ...

Magnetic data boundary detection is a key technology in potential field data processing, providing an effective basis for the division of geological units and fault structures. It holds significant ...

Intrusion detection systems (IDS) and anomaly detection techniques are critical components of modern cybersecurity, enabling the identification of malicious activities and system irregularities in ...