However, previous studies did not systematically synthesize their diagnostic accuracy. Objective: To quantitatively explore the diagnostic efficacy of deep learning (DL) and radiomics for extracranial ...

Abstract: Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much ...

Security Operation Centers (SOC) continuously monitor system logs to detect suspicious activities such as brute-force attacks, unauthorized access, or privilege abuse. However, the large volume and ...

Liver cancer, including hepatocellular carcinoma (HCC), is a leading cause of cancer-related deaths globally, emphasizing the need for accurate and early detection methods. LiverCompactNet classifies ...

ABSTRACT: The accelerating sophistication of cyberattacks poses unprecedented challenges for national security, critical infrastructures, and global digital resilience. Traditional signature-based ...

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?

ABSTRACT: Healthcare cyberattacks rise as attackers leverage system and human vulnerabilities. The existing security systems are technology-driven as they focus on system resilience, but they neglect ...

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