Forging Fidelity: Leveraging Deep Learning for Establishing Trustworthy Historical Information within the Metaverse

Authors

  • Esraa Ahmed Ismael, Kiran K. V. D.

Keywords:

Metaverse, Deep Learning (DL), Natural Language Processing (NLP), Monte Carlo (MC), Markove Chain Monte Carlo (MCMC).

Abstract

Upon awakening from a captivating dream, one often finds themselves questioning the reality they inhabit, blurring the lines between fantasy and actuality. Whether the dream evokes joy or discomfort, its lingering impact is similar to the immersive nature of the Metaverse. Developers aspire to instill users with a profound sense of reality within this virtual realm, leveraging psychological subtleties to blur the boundaries between virtuality and lived experience. Through empirical trials, users have confirmed the Metaverse's ability to evoke sensations akin to real-life encounters. Thus, let us embark on a journey into the Metaverse, taking the opportunity to explore history and participate in its unfolding narrative, moment by moment.

This paper delves into the intersection of advanced Deep Learning methodologies, particularly Natural Language Processing (NLP), and historical storytelling within the metaverse. By leveraging Deep Learning techniques such as NLP, Markov Chain (MC), and Markov Chain Monte Carlo (MCMC) algorithms, our study seeks to revolutionize the understanding and dissemination of historical narratives. Through the fusion of AI and historical analysis, we aim to not only enhance historical comprehension but also address prevalent challenges like misinformation and the propagation of fabricated history. Our research attempts to bridge the gap between traditional historical scholarship and cutting-edge technology, envisioning a future where historical storytelling transcends conventional mediums to offer immersive and interactive experiences within virtual environments.

Furthermore, our work extends beyond mere theoretical exploration, laying the groundwork for practical applications in the metaverse. By harnessing the capabilities of Deep Learning, we aspire to reshape historical narratives into dynamic and engaging experiences that resonate with contemporary audiences. This approach not only fosters a deeper connection to the past but also serves as a bulwark against the distortion of historical truths. Looking ahead, we propose avenues for future research that involve the integration of user-generated content and collaboration with historians, fostering a collaborative approach to historical storytelling. Through these attempts, we envision a metaverse where historical narratives are not only authentic but also participatory, offering users the opportunity to engage with and contribute to the rich tapestry of human history.

Downloads

Download data is not yet available.

References

J. R. Jim, M. T. Hosain, M. F. Mridha, M. M. Kabir, and J. Shin, “Toward Trustworthy Metaverse: Advancements and Challenges,” IEEE Access, vol. 11, pp. 118318–118347, 2023, doi: 10.1109/ACCESS.2023.3326258.

J. Chen et al., “Multiagent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-Enabled Vehicular Metaverses With Trajectory Prediction,” IEEE Internet Things J, vol. 11, no. 1, pp. 70–83, Jan. 2024, doi: 10.1109/JIOT.2023.3296075.

X. Li, Y. Tian, P. Ye, H. Duan, and F. Y. Wang, “A Novel Scenarios Engineering Methodology for Foundation Models in Metaverse,” IEEE Trans Syst Man Cybern Syst, vol. 53, no. 4, pp. 2148–2159, Apr. 2023, doi: 10.1109/TSMC.2022.3228594.

H. Shi, G. Liu, K. Zhang, Z. Zhou, and J. Wang, “MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse,” IEEE Trans Syst Man Cybern Syst, vol. 53, no. 4, pp. 2107–2117, Apr. 2023, doi: 10.1109/TSMC.2022.3229213.

S. M. Park and Y. G. Kim, “A Metaverse: Taxonomy, Components, Applications, and Open Challenges,” IEEE Access, vol. 10, pp. 4209–4251, 2022, doi: 10.1109/ACCESS.2021.3140175.

X. S. Zhai, X. Y. Chu, M. Chen, J. Shen, and F. L. Lou, “Can Edu-Metaverse Reshape Virtual Teaching Community (VTC) to Promote Educational Equity? An Exploratory Study,” IEEE Transactions on Learning Technologies, vol. 16, no. 6, pp. 1130–1140, Dec. 2023, doi: 10.1109/TLT.2023.3276876.

Neeba N.V and C.V.Jawahar, “Recognition of Book by Verification and Retraining,” in 19th International Conference on Pattern Recognition : (ICPR 2008) ; Tampa, Florida, USA 8-11 December 2008., IEEE, 2008.

W. H. Park, N. M. F. Qureshi, and D. R. Shin, “An Effective 3D Text Recurrent Voting Generator for Metaverse,” IEEE Trans Affect Comput, vol. 14, no. 3, pp. 1766–1778, Jul. 2023, doi: 10.1109/TAFFC.2022.3216782.

V. B. de Souza, R. C. de Andrade, and T. C. da Silva, “NLP Applied to Systematic Review: Case Study for the Creation of a Golden Set for the Metaverse,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2023, pp. 431–449. doi: 10.1007/978-3-031-28076-4_32.

G. Atiyah, N. A. N. Faris, G. Rexhepi, and A. J. Qasim, “Integrating Ideal Characteristics of Chat-GPT Mechanisms into the Metaverse: Knowledge, Transparency, and Ethics,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2023, pp. 131–141. doi: 10.1007/978-3-031-51716-7_9.

F. De Felice, C. De Luca, S. Di Chiara, and A. Petrillo, “Physical and digital worlds: implications and opportunities of the metaverse,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 1744–1754. doi: 10.1016/j.procs.2022.12.374.

J. Gu, J. Wang, X. Guo, G. Liu, S. Qin, and Z. Bi, “A Metaverse-Based Teaching Building Evacuation Training System With Deep Reinforcement Learning,” IEEE Trans Syst Man Cybern Syst, vol. 53, no. 4, pp. 2209–2219, Apr. 2023, doi: 10.1109/TSMC.2022.3231299.

R. Hare and Y. Tang, “Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education,” IEEE Trans Syst Man Cybern Syst, vol. 53, no. 4, pp. 2047–2055, Apr. 2023, doi: 10.1109/TSMC.2022.3227919.

J. Kang et al., “Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach,” IEEE Internet Things J, 2024, doi: 10.1109/JIOT.2024.3360183.

]M. Zawish et al., “AI and 6G Into the Metaverse: Fundamentals, Challenges and Future Research Trends,” 2024, doi: 10.1109/OJCOMS.2023.3349465.

M. H. Rahman, M. A. S. Sejan, M. A. Aziz, J. I. Baik, D. S. Kim, and H. K. Song, “Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems,” IEEE Transactions on Green Communications and Networking, vol. 7, no. 3, pp. 1515–1527, Sep. 2023, doi: 10.1109/TGCN.2023.3237132.

R. Qin et al., “Web3-Based Decentralized Autonomous Organizations and Operations: Architectures, Models, and Mechanisms,” IEEE Trans Syst Man Cybern Syst, vol. 53, no. 4, pp. 2073–2082, Apr. 2023, doi: 10.1109/TSMC.2022.3228530.

J. Wang, S. Chen, Y. Liu, and R. Lau, “Intelligent Metaverse Scene Content Construction,” IEEE Access, vol. 11, pp. 76222–76241, 2023, doi: 10.1109/ACCESS.2023.3297873.

W. Yu, T. J. Chua, and J. Zhao, “User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks,” IEEE Trans Wirel Commun, 2023, doi: 10.1109/TWC.2023.3277226.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville, DEEP LEARNING. 2016.

H. Suga et al., “Study of Natural Language Dialog System for Avatar Communication in Metaverse,” in Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 589–591. doi: 10.1109/ISMAR-Adjunct60411.2023.00125.

D. Beck, L. Morgado, and P. O’Shea, “Educational Practices and Strategies with Immersive Learning Environments: Mapping of Reviews for Using the Metaverse,” IEEE Transactions on Learning Technologies, vol. 17, pp. 319–341, 2024, doi: 10.1109/TLT.2023.3243946.

Downloads

Published

24.03.2024

How to Cite

Kiran K. V. D., E. A. I. (2024). Forging Fidelity: Leveraging Deep Learning for Establishing Trustworthy Historical Information within the Metaverse. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2005–2015. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5666

Issue

Section

Research Article