Blockchain-based University Election System with Biometric Authentication and AI-driven Anomaly Detection

This project presents a Blockchain-based University Election System enhanced with biometric authentication and AIdriven anomaly detection to address the challenges of security, transparency, and reliability in student elections. The proposedsystem verifies voter identity through fingerprint and facial recognition, ensuring that only eligible students participate andeliminating risks of impersonation, duplicate, or proxy voting. Each vote is encrypted and immutably recorded on the blockchainledger, preventing tampering, deletion, or manipulation while creating a transparent and verifiable audit trail. Smart contractsgovern the election process by automating voter eligibility checks, enforcing the one-student-one-vote policy, scheduling theelection, and instantly counting and publishing results without human intervention. The integration of AI adds another layer ofprotection by continuously analyzing voting behaviors, biometric data, and transaction patterns to detect anomalies, suspicioustrends, or fraudulent activities in real time. This holistic approach reduces manual errors, enhances accountability, and buildsstudent confidence in the election process through verifiable and tamper-proof outcomes. Additionally, the system is designed tobe user-friendly, scalable, and cost-effective, making it adaptable not only for universities but also for larger institutions andgovernment-level elections in the future. By combining blockchain, biometrics, and AI, this project demonstrates a secure,intelligent, and modern framework for conducting elections with integrity and efficiency.