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@article{196562,
        author = {Shraddha Satish Kalyankar and Siddhi Ankush Salunkhe and Sakshi Nitin Gatkal and Sangram Ramkrishna Rode and Prof. P.S Salve},
        title = {Online Examination Proctoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2970-2972},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196562},
        abstract = {Online examinations have become increasingly popular, but maintaining fairness and preventing cheating remains a major challenge. This paper proposes an AI-based online examination proctoring system that uses deep learning and computer vision techniques to monitor student behavior during exams.The system uses a hybrid approach combining Convolutional Neural Networks (CNN), face detection, and behavioral analysis to detect suspicious activities such as multiple faces, absence from screen, and unusual head movements. The model is trained using real-time video input and alerts are generated for potential malpractice. Experimental results show high accuracy and efficiency in detecting anomalies, making the system reliable for remote examinations.},
        keywords = {},
        month = {April},
        }