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A Study on Metaverse Realistic Content Education Platform Using Deep Learning

Authors
Lee, HyunsookYoum, Sekyoung
Issue Date
Jun-2023
Publisher
Springer Science and Business Media Deutschland GmbH
Keywords
Engineering Education; Artificial Intelligence Technologies; Educational Environment; Front End; Immersive; Machine Learning Agents; Metaverses; Quality Of Education; Resource Efficiencies; Specific Languages; Web Environment; Deep Learning
Citation
Lecture Notes in Electrical Engineering, v.1028 LNEE, pp 313 - 317
Pages
5
Indexed
SCOPUS
Journal Title
Lecture Notes in Electrical Engineering
Volume
1028 LNEE
Start Page
313
End Page
317
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/19385
DOI
10.1007/978-981-99-1252-0_41
ISSN
1876-1100
1876-1119
Abstract
Artificial intelligence technology combined with Metaverse Realistic Content (VR/AR Content) is a powerful combination that provides a diverse educational environment. Recent developments in artificial intelligence technology can enhance the quality of education without the help of assistants in immersive education and it can be seen as a field that can be developed in terms of resource efficiency and time saving by learning repetitive outcomes (success/failure). However, this combination has challenges. Currently, Unity’s Machine Learning Agent toolkit exists, but it has limitations for specific languages and specific environments and has additional learning challenges. In addition, it is difficult to realize that it is necessary to support various front-end frameworks and various hardware, and to support the entire combination. In addition, it is necessary to study connections between frameworks to support mobile devices and web environment. This problem has long been recognized in computer science and has been solved by compiler technology. In this paper, we propose a Framework Bridge Model based on LLVM IR (Low-level virtual machine intermediate representation) in VR/AR development environment, so that deep learning developers can choose the optimal combination of frameworks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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