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

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 throug...

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Bibliographic Details
Published in:Journal of self-governance and management economics 2022-01, Vol.10 (4), p.24-38
Main Authors: Aldridge, Susan, Geambazi, Petris, Alexandru, Bogdan
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Augmented reality
Bibliometrics
Biometrics
Blockchain
Business Economy / Management
Cognition & reasoning
Computational linguistics
Data mining
Data processing
Deep learning
Digital twins
Engineering
Haptics
ICT Information and Communications Technologies
Industrial research
Infrastructure
Intelligence
Interactive computer systems
Language processing
Layout
Learning
Literature reviews
Machine learning
Mapping
Multimodality
Natural language interfaces
Natural language processing
Physics
Quality assessment
Real-time control
Real-time systems
Role models
Sensors
Simulation
Simulation methods
Software
Systematic review
Twins
United Kingdom
Virtual reality
Virtual worlds
Vision systems
Visualization
Visualization (Computers)
Online Access:Get full text
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Description
Summary: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.
ISSN:2329-4175
2377-0996
DOI:10.22381/jsme10420222