VoteChain: An Integrated Web-Based Framework for Decentralized Institutional Elections with Department-Specific Granular Access Control

Abstract

Student elections in academic institutions are essential for promoting leadership, participation, and democratic values among students. However, traditional voting methods, whether manual or basic digital systems, often face issues such as identity fraud, duplicate voting, lack of transparency, and data manipulation. These challenges reduce trust and affect the reliability of election outcomes. To address these limitations, this paper presents VoteChain, a secure and intelligent digital voting platform designed for student council elections. The system integrates Artificial Intelligence (AI), Machine Learning (ML), and a block chain-inspired ledger to ensure a reliable and tamper-resistant voting process. AI-based facial verification is used to authenticate voters and enforce the “one student, one vote” principle. Additionally, machine learning techniques monitor voting activities in real time to detect suspicious behavior. Votes are encrypted and stored in an immutable ledger, ensuring data integrity and security. The system also includes a user-friendly dashboard for monitoring participation and generating results instantly. Experimental results demonstrate improved security, efficiency, and transparency, making VoteChain a robust solution for modern digital elections.