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ABSTRACT
Introduction: Firstly The introduction sets the stage by highlighting the growing importance of fraud prevention in blockchain transactions. It elucidates the potential threats and challenges that necessitate the utilization of advanced machine learning techniques.
Literature Review: A thorough exploration of existing literature reviews establishes the current state of research in blockchain fraud detection. Notable advancements, limitations, and gaps in knowledge become apparent, creating a foundation for the study’s objectives.
Methodology: This section outlines the rigorous methodology employed in evaluating machine learning algorithms. Rigorous comparisons ensure the reliability of the findings.
Selected Machine Learning Algorithms: Various algorithms, including Random Forest, Support Vector Machines, and Neural Networks, are individually dissected. Their specific strengths, weaknesses, and applications in the context of fraud detection are explored, allowing for an insightful comparative analysis.
Experimental Results: Findings from extensive experiments are presented, showcasing the performance of each algorithm in terms of accuracy, precision, recall, and F1-score. Real-world applicability and computational efficiency are also considered, providing a holistic evaluation.
Discussion: thus The discussion delves into the implications of the results, emphasizing the significance of selecting an algorithm that balances accuracy with computational efficiency. So Insights into the adaptability of these algorithms to dynamic blockchain environments are discussed, offering practical considerations for implementation.
Conclusion: Thus The study concludes by summarizing key findings and recommending the most effective machine learning algorithms for fraud detection in blockchain. Implications for future research and potential enhancements to current methodologies are also discussed.
