Development of an E-Voting Scheme through a Blockchain-Enabled Approach with Deep Hybrid Learning for Biometric Authentication

Developing a secure and transparent electronic voting (e-voting) scheme that maintains the fairness and privacy of traditional voting methods has long been a big challenge. In this research, a novel approach is presented that leverages deep hybrid learning techniques to enhance the biometric authentication system, combining various machine learning models such as Support Vector Machine, Random Forest, and K-Nearest Neighbors with deep learning architectures including VGG16, VGG19, Xception, and Inception. After authentication it extends to the design of a blockchain-based voting scheme, powered by smart contracts and NEAR wallet integration. This innovative model constructs a robust and secure blockchain-based e-voting solution. This research represents a significant step towards achieving a balance between the transparency and flexibility offered by electronic voting schemes while upholding the integrity and privacy crucial to the democratic process.