ABSTRACT. The purpose of this study is to examine artificial intelligence-based digital twin industrial metaverse and immersive 3D worlds for connected machines and objects. Throughout May 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence-based digital twin industrial metaverse” + “deep generative modeling and virtual machine remote monitoring tools,” “cyber-physical cognitive and synthetic data-based autonomous production systems,” and “neural machine and virtual plant floor networks.” As research published in 2022 and 2023 was inspected, only 175 articles satisfied the eligibility criteria, and 20 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Colandr, DistillerSR, Eppi-Reviewer, Nested Knowledge, ROBIS, and SluRp.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: deep generative modeling; virtual machine remote monitoring; cyber-physical cognitive and synthetic data-based autonomous production; neural machine and virtual plant floor; artificial intelligence; digital twin industrial metaverse

How to cite: Burcea, Ș. G., Ciurlău, C. F., Hurjui, M. C., Privitera, D., and Nistoroiu, B.-F. (2024). “Deep Generative Modeling and Virtual Machine Remote Monitoring Tools, Cyber-Physical Cognitive and Synthetic Data-based Autonomous Production Systems, and Neural Machine and Virtual Plant Floor Networks in Artificial Intelligence-based Digital Twin Industrial Metaverse,” Journal of Self-Governance and Management Economics 12(3): 39–53. doi: 10.22381/jsme12320243.

Received 15 June 2024 • Received in revised form 23 September 2024
Accepted 26 September 2024 • Available online 29 September 2024