Dung Tien Nguyen

TL;DR: This study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.

TL;DR: A survey of algorithms used to create deepfakes and, more importantly, methods proposed to detectDeepfake in the literature to date is presented and extensive discussions on challenges, research trends and directions related to deepfake technologies are presented.

TL;DR: In this article, the authors report and analyze genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models.

TL;DR: Analysis of genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in proteinsecondary structure and relative solvent accessibility.

TL;DR: The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes, which provides evidences strongly supporting scientific hypotheses that bats and pangsolins are probable hosts for the COVID -19 virus.