COMPARATIVE ANALYSIS OF BLOCKCHAIN-BASED VOTING SYSTEMS USING MACHINE LEARNING TECHNIQUES

Abstract

Integrating Blockchain technology into electronic voting systems promises to enhance security, transparency, and efficiency in electoral processes. However, the performance and reliability of these systems vary significantly, necessitating a comprehensive evaluation. This research conducts a comparative analysis of various Blockchain-based voting systems using machine learning techniques to assess their performance, security, and user-friendliness. Findings reveal significant variations in system efficiency, scalability, and robustness, with distinct correlations between Blockchain architecture and overall system performance. The study provides empirical insights into the capabilities and limitations of current Blockchain-based voting systems, emphasizing the critical role of machine learning in enhancing system analysis. Results offer valuable guidance for developing more secure, scalable, and user-friendly voting systems, paving the way for their broader adoption in democratic processes.