Multi-Modal Synthetic Data Fusion and Analysis, Virtual Immersive and Cognitive Neuro-Engineering Technologies, and Bio-inspired Computational Intelligence and Deep Learning Algorithms in the Industrial Metaverse
Susan Aldridge1, Petris Geambazi2, and Bogdan Alexandru2ABSTRACT. The aim of this systematic review is to synthesize and analyze immersive and interactive technologies, asset maintenance simulations, and real-time data-based digital twins in the industrial metaverse. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout August 2022, with search terms including “the industrial metaverse” + “multi-modal synthetic data fusion and analysis,” “virtual immersive and cognitive neuro-engineering technologies,” and “bio-inspired computational intelligence and deep learning algorithms.” As we analyzed research published in 2022, only 151 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 20, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41
Keywords: multi-modal synthetic data fusion and analysis; virtual immersive and cognitive neuro-engineering technologies; bio-inspired computational intelligence; deep learning algorithms; industrial metaverse
How to cite: Aldridge, S., Geambazi, P., and Alexandru, B. (2022). “Multi-Modal Synthetic Data Fusion and Analysis, Virtual Immersive and Cognitive Neuro-Engineering Technologies, and Bio-inspired Computational Intelligence and Deep Learning Algorithms in the Industrial Metaverse,” Journal of Self-Governance and Management Economics 10(4): 22–36. doi: 10.22381/jsme10420222.
Received 26 September 2022 • Received in revised form 25 December 2022
Accepted 27 December 2022 • Available online 30 December 2022