Neural-Based Secured Decentralized E Voting Framework using Blur Image Broadcasting

Abstract

One of the fundamental rights in the modern democracy is voting. Much research has been done to strengthen the voting process and security. The new and safe Neural-Based Secured Decentralised E-Voting Framework employing Blur Image Broadcasting tackles the major issues with standard electronic voting techniques. Neural networks with blurred image broadcasting protected voter confidentiality, ballot integrity, & system security. Therefore, a novel Zebra-based GoogleNet Elliptic Curve (ZbGEC) is provided to upgrade the decentralized e-voting via blur image broadcasting in this study. It authenticates and broadcasts the voter’s information safely to the blockchain technology. It additionally demonstrates time consumption, memory usage, and design cost. Only authorized users can view and alter encrypted and decrypted votes via neural networks. This encryption protects voter anonymity and ballot manipulation. Furthermore, blur image streaming obscures voter ballot selections, improving voter privacy. The decentralized design spreads voting over numerous nodes; removing the centralized Spread structure strengthens the system against manipulation and cyber-attacks. Notably, decentralized e-voting time consumption and response time were minimized to an efficient 2 seconds and 5 seconds. The proposed system's design cost was economical at $30, while memory usage was optimized to 300 MB, representing a significant improvement over traditional methods. Neural-based security, decentralized structures, and blurred image streaming produce a reliable e-voting system. This architecture improves security, privacy, openness, and scalability over electronic voting systems. The Neural-Based Secured Decentralised EVoting Framework utilizing Blur Image Broadcasting might make voting safer, more transparent, and inclusive.