Unlocking Tron's Power: Exploring Its Potential Applications


  • Meenu




Digital ecosystem, Blockchain, Tron, Defi, Tron power, Decentralized technology


This research paper delves into the multifaceted potential of the Tron blockchain platform across various industries and use cases. Tron has emerged as a prominent player in the blockchain space, offering high throughput, scalability, and smart contract functionality. This paper examines the diverse applications of Tron, ranging from decentralized finance (DeFi) and gaming to content distribution and supply chain management. Drawing from case studies, industry insights, and expert analysis, the paper explores how Tron's unique features and capabilities can revolutionize existing systems and create new opportunities for innovation. By uncovering Tron's power and versatility, this paper contributes to a deeper understanding of its role in shaping the future of decentralized technologies and digital ecosystems.


A. Singla and M. Gupta, “9NFTMANIA: BRIDGING NFT ART AND DIGITAL CURRENCY WITH 9NM TOKENS”, SJMBT, vol. 2, no. 1, pp. 1–6, Feb. 2024.

M. Gupta, “Love in the Blockchain: Unique NFT Gifts for Lovers”, SJMBT, vol. 2, no. 1, pp. 7–12, Feb. 2024.

Singla, A., & Gupta, M. (2024). Shaping the Digital Renaissance: The Impact of Glamorous NFT Collections. Scientific Journal of Metaverse and Blockchain Technologies, 2(1), 13–17. https://doi.org/10.36676/sjmbt.v2.i1.03

M. M. Bailey, “Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis,” pp. 1–12, 2022, [Online]. Available: http://arxiv.org/abs/2210.05760.

M. S. Akter, H. Shahriar, N. Ahmed, and A. Cuzzocrea, “Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data,” Proc. - 2022 IEEE Int. Conf. Big Data, Big Data 2022, pp. 5646–5655, 2022, doi 10.1109/BigData55660.2022.10020249.

B. Wei, J. Li, A. Gupta, H. Umair, A. Vovor, and N. Durzynski, “Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning,” 2021, [Online]. Available: http://arxiv.org/abs/2108.03305.

A. Wadhawan and A. Aggarwal, “Towards Emotion Recognition in Hindi-English Code-Mixed Data: A Transformer Based Approach,” WASSA 2021 - Work. Comput.Approaches to Subj. Sentim.Soc.Media Anal. Proc. 11th Work., pp. 195–202, 2021.https://aclanthology.org/2021.wassa-1.21.

A. K. Chanda, Efficacy of BERT embeddings on predicting disaster from Twitter data, vol. 1, no. 1. Association for Computing Machinery, 2021.[Online]. Available: http://arxiv.org/abs/2108.10698.

N. A. Azeez, S. O. Idiakose, C. J. Onyema, and C. Van Der Vyver, “Cyberbullying Detection in Social Networks: Artificial Intelligence Approach,” J. Cyber Secure. Mobil., vol. 10, no. 4, pp. 745–774, 2021, doi: 10.13052/jcsm2245-1439.1046.

D. Antonakaki, P. Fragopoulou, and S. Ioannidis, “A survey of Twitter research: Data model, graph structure, sentiment analysis, and attacks,” Expert Syst. Appl., vol. 164, no. September 2020, p. 114006, 2021, doi: 10.1016/j.eswa.2020.114006.

K. N. Alamet al., “Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data,” Comput.Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/4321131.

C. Zhang, B. Wilkinson, A. Ganesan, and T. Oates, “Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter,” 2019, doi: 10.1145/3306195.3306199.

V. Sivasangari, A. K. Mohan, K. Suthendran, and M. Sethumadhavan, “Isolating rumors using sentiment analysis,” J. Cyber Secure.Mobil., vol. 7, no. 1, pp. 181–200, 2018, doi: 10.13052/jcsm2245-1439.7113.

Gupta, M., Gupta, D., & Duggal, A. (2023). NFT Culture: A New Era. Scientific Journal of Metaverse and Blockchain Technologies, 1(1), 57–62. https://doi.org/10.36676/sjmbt.v1i1.08

M. Gupta, “Reviewing the Relationship Between Blockchain and NFT With World Famous NFT Market Places”, SJMBT, vol. 1, no. 1, pp. 1–8, Dec. 2023.

R. Gupta, M. Gupta, and D. Gupta, “Role of Liquidity Pool in Stabilizing Value of Token”, SJMBT, vol. 1, no. 1, pp. 9–17, Dec. 2023.

M. GUPTA and D. Gupta, “Investigating Role of Blockchain in Making your Greetings Valuable”, URR, vol. 10, no. 4, pp. 69–74, Dec. 2023.

R. Issalh, A. Gupta, and M. Gupta, “PI NETWORK : A REVOLUTION”, SJMBT, vol. 1, no. 1, pp. 18–27, Dec. 2023.

A. Duggal, M. Gupta, and D. Gupta, “SIGNIFICANCE OF NFT AVTAARS IN METAVERSE AND THEIR PROMOTION: CASE STUDY”, SJMBT, vol. 1, no. 1, pp. 28–36, Dec. 2023.

M. Gupta, “Say No to Speculation in Crypto market during NFT trades: Technical and Financial Guidelines”, SJMBT, vol. 1, no. 1, pp. 37–42, Dec. 2023.

A. Singla, M. Singla, and M. Gupta, “Unpacking the Impact of Bitcoin Halving on the Crypto Market: Benefits and Limitations”, SJMBT, vol. 1, no. 1, pp. 43–50, Dec. 2023.


D. Gupta and S. Gupta, “Exploring world famous NFT Scripts: A Global Discovery”, SJMBT, vol. 1, no. 1, pp. 63–71, Dec. 2023.

M. Gupta, “Integration of IoT and Blockchain for user Authentication”, SJMBT, vol. 1, no. 1, pp. 72–84, Dec. 2023.

A. Singla and M. Gupta, “Investigating Deep learning models for NFT classification : A Review”, SJMBT, vol. 1, no. 1, pp. 91–98, Dec. 2023.

Issalh, R., Gupta, D., & Gupta, M. (2023). RESEARCHER ECONOMY: A REVOLUTION BY 9NFTMANIA FOR PRESENT ALPHA MALE. Scientific Journal of Metaverse and Blockchain Technologies, 1(1), 99–104. https://doi.org/10.36676/sjmbt.v1i1.13

R. Jangra, “Reviewing the Optimized Mechanism for Deep Learning Based Bot Detection to Evaluate Genuine Crypto Assets”, SJMBT, vol. 1, no. 1, pp. 105–113, Dec. 2023.



DOI: 10.36676/sjmbt.v2.i1.07
Published: 2024-02-29

How to Cite

Meenu. (2024). Unlocking Tron’s Power: Exploring Its Potential Applications. Scientific Journal of Metaverse and Blockchain Technologies, 2(1), 44–52. https://doi.org/10.36676/sjmbt.v2.i1.07



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