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@article{195540,
        author = {Umamaheswararao Mogili},
        title = {NextGen: A Secure Real-Time Student Voting Web Application for Digital Campus Elections},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {354-359},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195540},
        abstract = {The integrity of democratic processes within academic institutions is frequently compromised by outdated voting methodologies. Traditional systems, whether paper-based or rudimentary digital forms, lack robust identity verification and transparent auditing, leading to issues such as proxy voting and administrative manipulation. This paper presents "NextGen," a high-fidelity web-based voting application specifically engineered to modernize these processes. The core contribution of this work is a dual-layered security framework that first implements a biometric authentication gatekeeper utilizing Convolutional Neural Networks (CNN). Unlike traditional facial recognition that relies on static image matching, our CNN model extracts 128-dimensional facial embeddings, providing high accuracy even under varying environmental conditions and preventing "photo-spoofing" through integrated liveness detection. Furthermore, we address the "trust deficit" in digital counting by implementing a hashed-ledger architecture where every vote cast is cryptographically linked to the previous one using SHA-256 hashing, creating a tamper-evident audit trail similar to blockchain technology. Our longitudinal study across a campus deployment of over 2,000 students reveals an authentication accuracy of 98.5% and an average end-to-end voting latency of less than 30 seconds. The system demonstrated complete resilience against database-level manipulation during "Red Team" stress testing, providing real-time result visualization through an interactive dashboard to foster a culture of transparency and increase student engagement.},
        keywords = {Online Voting System, Biometric Authentication, CNN, Deep Learning, Block chain lite, Information Security, SHA-256, Computer Vision.},
        month = {April},
        }