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 ...

Awurum, N.P. (2025) Next-Generation Cyber Defense: AI-Powered Predictive Analytics for National Security and Threat Resilience. Open Access Library Journal, 12, 1-17. doi: 10.4236/oalib.1114210 .

The rapid evolution of large language models is transforming industries, catalyzing advances in content generation, search, ...

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

Abstract: Autoencoder (AE) is extensively utilized in Hyperspectral anomaly detection (HAD) tasks owing to the robust feature extraction and image reconstruction capabilities. However, AE lacks ...

1 Analytics Department, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India 2 Department of Data Science, School of Computer Science and Engineering ...

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 ...

NVIDIA introduces NV-Tesseract-AD, a sophisticated model enhancing anomaly detection through diffusion modeling, curriculum learning, and adaptive thresholds, aiming to tackle complex industrial ...

James is a published author with multiple pop-history and science books to his name. He specializes in history, space, strange science, and anything out of the ordinary.View full profile James is a ...

James is a published author with multiple pop-history and science books to his name. He specializes in history, space, strange science, and anything out of the ordinary.View full profile James is a ...