Abstract: Recent advancements in hyperspectral anomaly detection (HAD) utilizing deep learning have garnered significant attention due to their superior performance. However, most existing methods ...

Principal Data Engineer Rajesh Mattaparthi is using transformer-based AI to detect hidden faults in standby power generators ...

One seismometer is often not enough to reliably detect earthquakes or human activity such as underground nuclear tests.

A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

Demonstrating academic excellence and a strong interest in AI-related domains, seven students of Computer Science Engineering ...

By Dr. Richmond Atuahene1.0 Introduction/ BackgroundGlobalization and digitization are two major trends that will shape the future of nations. Despite the many challenges associated with adapting to ...

Google Cloud has expanded its enterprise cybersecurity push with AI Threat Defense, as the market shifts towards AI-native threat detection, contextual triage, and supervised remediation. The launch ...

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...

Abstract: The current fast proliferation of the Internet of Things (IoT) networks has made anomaly detection and security more difficult. Traditional methods are not able to detect hostile activities ...

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 ...