Open Access
Issue
BIO Web Conf.
Volume 86, 2024
International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
Article Number 01117
Number of page(s) 13
DOI https://doi.org/10.1051/bioconf/20248601117
Published online 12 January 2024
  • D. Chaum, “Untraceable electronic main, return addresses, and digital pseudonyms,” in Communications of the ACM, vol. 24, no. 2, pp. 84-88, February 1981 [CrossRef] [Google Scholar]
  • F. Tschorsch and B. Scheuermann, “Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies,” in IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 2084-2123, thirdquarter 2016, doi: 10.1109/COMST.2016.2535718. [CrossRef] [Google Scholar]
  • V. Kumar T, S. Santhi, K. G. Shanthi and G. M, “Cryptocurrency Price Prediction using LSTM and Recurrent Neural Networks,” 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2023, pp. 1-5, doi: 10.1109/ICAAIC56838.2023.10141048. [Google Scholar]
  • Karthik Vikram, Nikhil Sivaraman and P. Balamurugan, “Crypto Currency Market Price Prediction Using Data Science Process”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321–9653; IC Value: 45.98; SJ Impact Factor: 7.538, vol. 10, 2022. [Google Scholar]
  • Emmanuel Pintelasl, Ioannis E. Livieris, Stavros Stavroyiannis, Theodore Kotsilieris and Panagiotis Pintelas, “Investigating the Problem of Cryptocurrency Price Prediction: A Deep Learning Approach”, IFIP International Federation for Information Processing 2020. [Google Scholar]
  • Sina E. Charandabi and Kamyar Kamyar, “Survey of Cryptocurrency Volatility Prediction Literature Using Artificial Neural Networks”, Business and Economic Research ISSN 2162–48602022, vol. 12, no.1. [Google Scholar]
  • Prabhakar, P. K. (2020). Bacterial siderophores and their potential applications: a review. Current Molecular Pharmacology, 13(4), 295-305. [CrossRef] [PubMed] [Google Scholar]
  • Agis Politis, Katerina Doka and Nectarios Koziris, “Ether Price Prediction Using Advanced Deep Learning Models”, 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). [Google Scholar]
  • Jiavunluo, “Bitcoin price prediction in the time of COVID-19”, 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID). [Google Scholar]
  • Mohammad J. Hamayel and Amani Yousef Owda, “A Novel Cryptocurrency Price Prediction Model Using GRU. LSTM and bi-LSTM Machine Learning Algorithms”, AI, vol. 2, no. 4, pp. 477-496, 2021. [CrossRef] [Google Scholar]
  • Giuseppe Antonio Pierro, Henrique Rocha, Roberto Tonelli and Stéphane Ducasse, “Are the gas prices oracle reliable? a case study using the ethgasstation”, Proceedings of the IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE), pp. 1-8, 2020. [Google Scholar]
  • L. Zeng et al., “Characterizing Ethereum’s Mining Power Decentralization at a Deeper Level,” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, Vancouver, BC, Canada, 2021, pp. 1-10, doi: 10.1109/INFOCOM42981.2021.9488812. [Google Scholar]
  • S. S. Kushwaha, S. Joshi, D. Singh, M. Kaur and H. -N. Lee, “Ethereum Smart Contract Analysis Tools: A Systematic Review,” in IEEE Access, vol. 10, pp. 57037-57062, 2022, doi: 10.1109/ACCESS.2022.3169902. [CrossRef] [Google Scholar]
  • A. Konagari, H. P. Kusuma, S. Chetharasi, R. Kuchipudi, P. R. Babu and T. S. Murthy, “NFT Marketplace for Blockchain based Digital Assets using ERC-721 Token Standard,” 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2023, pp. 1394-1398, doi: 10.1109/ICSCSS57650.2023.10169350. [Google Scholar]
  • M. Cortes-Goicoechea, L. Franceschini and L. Bautista-Gomez, “Resource Analysis of Ethereum 2.0 Clients,” 2021 3rd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), Paris, France, 2021, pp. 1-8, doi: 10.1109/BRAINS52497.2021.9569812. [Google Scholar]
  • Z. Ouyang, J. Shao and Y. Zeng, “PoW and PoS and Related Applications,” 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), Changchun, China, 2021, pp. 59-62, doi: 10.1109/EIECS53707.2021.9588080. [Google Scholar]
  • B. Casella and L. Paletto, “Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting,” 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Dubai, United Arab Emirates, 2023, pp. 1-4, doi: 10.1109/ICBC56567.2023.10174989. [Google Scholar]
  • M. Bez, G. Fornari and T. Vardanega, “The scalability challenge of ethereum: An initial quantitative analysis,” 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), San Francisco, CA, USA, 2019, pp. 167-176, doi: 10.1109/SOSE.2019.00031. [Google Scholar]
  • M. Samin-Al-Wasee, P. S. Kundu, I. Mahzabeen, T. Tamim and G. R. Alam, “Time-Series Forecasting of Ethereum Price Using Long Short-Term Memory (LSTM) Networks,” 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, 2022, pp. 1-6, doi: 10.1109/ICEET56468.2022.10007377. [Google Scholar]
  • D. K. Tejaswi, H. Chauhan, T. J. Lakshmi, R. Swetha and N. N. Sri, “Investigation of Ethereum Price Trends using Machine learning and Deep Learning Algorithms,” 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2022, pp. 1-5, doi: 10.1109/CONIT55038.2022.9848000. [Google Scholar]
  • https://ethereum.org/en/developers/docs/consensusmechanisms/pow/mining/ https://www.investopedia.com/terms/p/proof-work.asp [Google Scholar]
  • Prabhakar, Pranav Kumar, et al. “Natural SIRT1 modifiers as promising therapeutic agents for improving diabetic wound healing.” Phytomedicine 76: 153252, (2020). [CrossRef] [PubMed] [Google Scholar]
  • A. K. Bhuyan, D. A. Naik, S. Sharma, A. Gehlot, A. Jafersadhiq and D. Kapila, “The Forecasting About Bitcoin and Other Digital Currency Markets: The Effects of Data Mining and Other Emerging Technologies,” 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 988-992, doi: 10.1109/ICACITE57410.2023.10183141. [Google Scholar]
  • K. R. Rao, M. L. Prasad, G. R. Kumar, R. Natchadalingam, M. M. Hussain and P. C. S. Reddy, “TimeSeries Cryptocurrency Forecasting Using Ensemble Deep Learning,” 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India, 2023, pp. 1446-1451, doi: 10.1109/ICCPCT58313.2023.10245083. [Google Scholar]
  • V. Veeraiah, V. Suthar, A. Y. Reddy, O. Dahiya, M. Azam and M. Kumbhkar, “Evaluation of Block-Chain Transaction Accuracy using Neural Network Model,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 233-238, doi: 10.1109/ICACITE53722.2022.9823465. [Google Scholar]
  • M. Saraswat, N. Kaur, Y. Singh Bisht, G. S. Reddy, M. Al-Taee and M. B. Alazzam, “The Use of Deep Learning and Blockchain for Predictive Analytics in Financial Management,” 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 11-15, doi: 10.1109/ICACITE57410.2023.10182503 [Google Scholar]
  • Coingecko:https://s3.amazonaws.com/assets.coingecko.com/app/public/ckeditor_assets/pictures/4416/content_demand_and_supply.jpg [Google Scholar]
  • Proof-of-Work Implementation :https://cointelegraph.com/storage/uploads/view/f419f334124a1e6ae4f67c8f7a1e64f1.jpg [Google Scholar]
  • Zuniga, E. W. V., Ranieri, C. M., Zhao, L., Ueyama, J., Zhu, Y.-t., & Ji, D. (2023). “Maximizing portfolio profitability during a cryptocurrency downtrend: A Bitcoin Blockchain transaction-based approach.” Procedia Computer Science, 222, 539-548. DOI: https://doi.org/10.1016/j.procs.2023.08.192. [CrossRef] [Google Scholar]
  • Rico-Peña, J. J., Arguedas-Sanz, R., López-Martin, C. (2023). “Models used to characterise blockchain features. A systematic literature review and bibliometric analysis.” Technovation, 123, 102711. DOI: https://doi.org/10.1016/j.technovation.2023.102711. [CrossRef] [Google Scholar]
  • Miglani, A., Kumar, N. (2021). “Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review.” Computer Communications, 178, 37-63. DOI: https://doi.org/10.1016/j.comcom.2021.07.009. [CrossRef] [Google Scholar]
  • Bothra, P., Karmakar, R., Bhattacharya, S., De, S. (2023). “How can applications of blockchain and artificial intelligence improve performance of the Internet of Things? – A survey.” Computer Networks, 224, 109634. DOI: https://doi.org/10.1016/j.comnet.2023.109634. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.