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@article{177156,
        author = {Karthikeiyhan S S and Tarun A and Eswarsamy A and Dr. V. Nivedita},
        title = {Enhanced AI Proctoring using Deep learning for Online Exam Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {684-689},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177156},
        abstract = {This project is a two-phase initiative aimed at strengthening examination security through the application of cutting-edge technologies. An AI-driven examination monitoring system is developed using deep learning algorithms, designed to detect and flag suspicious behaviour in real time during offline examinations. The system incorporates advanced facial recognition and behavioural analytics to enhance the integrity of academic assessments. In the second phase, the project introduces a metal detector-based security system at examination hall entrances to identify and restrict the entry of unauthorized electronic devices. By combining intelligent surveillance, this comprehensive approach addresses multiple vectors of examination malpractice. The overarching goal of the project is to deploy AI-powered solutions for bolstering fraud detection and safeguarding examination procedures. Leveraging biometric authentication and deep learning frameworks, the initiative not only enhances examination integrity but also contributes to broader applications in secure identity verification governance in the digital},
        keywords = {AI Proctoring, Deep Learning, Online Exam Monitoring, Cheating Detection, Facial Recognition, Automated.},
        month = {May},
        }