This paper presents a comprehensive analysis of the security and privacy challenges in the Metaverse, introducing a novel framework for evaluating and addressing these emerging threats. Our research makes three key contributions: (1) a systematic classification of Metaverse-specific security vulnerabilities across interconnected virtual and physical environments, (2) a framework for assessing privacy risks in AR/VR-enabled social interactions, and (3) targeted solutions for securing blockchain-based digital assets and identity management in the Metaverse. Our analysis highlights how traditional cybersecurity approaches must evolve to address the unique challenges posed by the fusion of physi... More >

This systematic review and meta-analysis explores the integration of artificial intelligence (AI) technologies into forensic odontology from an intelligent systems perspective, with particular emphasis on enhancing identification accuracy, pattern recognition capabilities, and workflow efficiency. Traditional dental identification methods rely heavily on manual comparison of charts and radiographs, which are time-consuming and susceptible to human bias. Recent advancements in machine learning algorithms, deep learning-based image recognition networks, and intelligent decision-support systems have demonstrated significant potential in automating critical tasks such as bite-mark analysis, dent... More >