ABSTRACT. This paper provides a systematic literature review of studies investigating ambient intelligence environments developing on behavioral predictive analytics, immersive and extended reality technologies, and virtual holographic objects. The analysis highlights that tactile sensing and virtual modeling technologies, spatio-temporal fusion algorithms, and digital twin simulation and machine learning-based image recognition tools enable immersive 3D worlds. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “fully connected metaverse” + “machine learning-based navigation and image processing computational algorithms,” “virtual immersive and multi-sensory extended reality technologies,” and “decision support and 3D virtual space networking tools.” As research published in 2022 was inspected, only 149 articles satisfied the eligibility criteria, and 28 sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: machine learning; navigation; image; virtual; immersive; sensor; extended reality; decision support; 3D metaverse

How to cite: Pera, A. (2022). “Machine Learning-based Navigation and Image Processing Computational Algorithms, Virtual Immersive and Multi-Sensory Extended Reality Technologies, and Decision Support and 3D Virtual Space Networking Tools in a Fully Connected Metaverse,” Smart Governance 1(3): 23–38. doi: 10.22381/sg1320222.

Received 26 June 2022 • Received in revised form 21 September 2022
Accepted 26 September 2022 • Available online 30 September 2022

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