A Comprehensive Review of Machine Learning Applications in Blockchain

EasyChair Preprint 12402

Abstract

Blockchain technology and machine learning are two rapidly evolving fields that have garnered significant interest in recent years. While blockchain technology has gained popularity due to its decentralized and secure nature, machine learning has emerged as a powerful tool for data analysis and prediction. The intersection of these two fields has led to a range of exciting applications, from fraud detection and smart contracts to supply chain management and healthcare.

This paper provides a comprehensive review of the applications of machine learning in blockchain technology. The paper begins with an introduction to blockchain technology and machine learning, followed by a background section that explores the relationship between the two fields. The methodology section describes the search strategy used to identify relevant literature, and the results section presents an overview of the key findings from the review.

The discussion section examines the challenges and limitations of using machine learning in blockchain, including issues related to privacy, scalability, and data quality. Despite these challenges, the potential benefits of combining machine learning and blockchain are significant, and this paper highlights several promising areas for future research.

In conclusion, this paper provides a timely and comprehensive review of the applications of machine learning in blockchain technology. By exploring the current state of the field and identifying key challenges and opportunities, this paper aims to stimulate further research and innovation in this exciting area of study.

Keyphrases: AI, Blockchain, machine learning, smart contracts

@booklet{EasyChair:12402,
  author    = {Ali Eric and Nasim James},
  title     = {A Comprehensive Review of Machine Learning Applications in Blockchain},
  howpublished = {EasyChair Preprint 12402},
  year      = {EasyChair, 2024}}