ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore cognitive artificial intelligence and machine learning-based predictive algorithms, digital twin and distributed sensing technologies, and simulation modeling and spatial data visualization tools. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the industrial metaverse” + “virtual modeling and immersive holographic imaging technologies,” “cloud-based digital twin manufacturing and visual perceptive systems,” and “bio-inspired computational intelligence and context awareness algorithms.” As research published between 2022 and 2023 was inspected, only 140 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 26 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: virtual modeling; immersive holographic imaging technologies; cloud-based digital twin manufacturing; visual perceptive systems; bio-inspired computational intelligence; context awareness algorithms; industrial metaverse

How to cite: Atkinson, D. (2023). “Virtual Modeling and Immersive Holographic Imaging Technologies, Cloud-based Digital Twin Manufacturing and Visual Perceptive Systems, and Bio-inspired Computational Intelligence and Context Awareness Algorithms in the Industrial Metaverse,” Journal of Self-Governance and Management Economics 11(1): 73–88. doi: 10.22381/jsme11120235.

Received 18 January 2023 • Received in revised form 24 March 2023
Accepted 27 March 2023 • Available online 30 March 2023

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