Version 1
: Received: 1 November 2024 / Approved: 4 November 2024 / Online: 6 November 2024 (02:37:33 CET)
How to cite:
AFNAN, M. S. A.; Yzem, C.; Yuan, F.; Jinpeng, W. A Comprehensive Review of the Integration of Machine Learning into Blockchain Technology. Preprints2024, 2024110146. https://doi.org/10.20944/preprints202411.0146.v1
AFNAN, M. S. A.; Yzem, C.; Yuan, F.; Jinpeng, W. A Comprehensive Review of the Integration of Machine Learning into Blockchain Technology. Preprints 2024, 2024110146. https://doi.org/10.20944/preprints202411.0146.v1
AFNAN, M. S. A.; Yzem, C.; Yuan, F.; Jinpeng, W. A Comprehensive Review of the Integration of Machine Learning into Blockchain Technology. Preprints2024, 2024110146. https://doi.org/10.20944/preprints202411.0146.v1
APA Style
AFNAN, M. S. A., Yzem, C., Yuan, F., & Jinpeng, W. (2024). A Comprehensive Review of the Integration of Machine Learning into Blockchain Technology. Preprints. https://doi.org/10.20944/preprints202411.0146.v1
Chicago/Turabian Style
AFNAN, M. S. A., Fang Yuan and Wang Jinpeng. 2024 "A Comprehensive Review of the Integration of Machine Learning into Blockchain Technology" Preprints. https://doi.org/10.20944/preprints202411.0146.v1
Abstract
This review investigates the integration of machine learning (ML) and blockchain technology, highlighting their combined potential to drive transformation across sectors such as healthcare, finance, and supply chain management. ML offers data-driven insights through predictive analytics, automation, and intelligent decision-making, while blockchain provides a secure, decentralized framework with transparency and immutability. Together, these technologies present solutions to critical challenges, including fraud detection, operational inefficiencies, and data privacy, promising significant advancements across various industries. ML enhances diagnostics and treatment personalization in healthcare, while blockchain protects sensitive medical data. In finance, blockchain fosters transparency within decentralized finance (DeFi), while ML strengthens fraud detection and trading strategies. In supply chain management, blockchain ensures traceability, while ML optimizes logistics and demand forecasting. Despite their synergies, integrating ML with blockchain faces challenges, notably in scalability, computational efficiency, and privacy. This review critically examines recent advancements, practical use cases, and barriers to adoption, synthesizing strategies that can address these challenges to unlock the transformative potential of these emerging technologies.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
