Author Affiliations
[1] [2] Assistant Professor, Department of Computer Science and Engineering, Er. Perumal Manimekalai College of Engineering, Hosur.
[3] Associate Professor, Department of Computer Science and Engineering, Er. Perumal Manimekalai College of Engineering, Hosur.
[4] [5] [6] Student, Department of Computer Science and Engineering, Er. Perumal Manimekalai College of Engineering, Hosur.
Abstract
The rapid advancement of digital technologies has significantly transformed the education sector, leading to the widespread adoption of online learning and assessment systems. However, ensuring academic integrity in remote examinations remains a critical challenge due to the lack of direct supervision. This paper presents a Smart Online Proctoring and Exam Integrity System that leverages artificial intelligence and computer vision techniques to monitor candidates during online examinations. The system uses real-time video analysis, facial recognition, gaze tracking, and behavior detection to identify suspicious activities such as multiple face presence, frequent head movements, and unauthorized screen usage. By automating the invigilation process, the system reduces human effort while improving accuracy and scalability. The proposed solution also generates detailed reports containing violation logs and risk scores, enabling administrators to make informed decisions. Overall, the system provides a secure, reliable, and efficient framework for conducting online examinations while maintaining academic integrity.
Keywords: Online Examination, Proctoring System, Artificial Intelligence, Computer Vision, Face Detection, Behavior Analysis, Academic Integrity.
How to Cite This Article
S. Suganya, A. Pamila, N. Shunmuga Karpagam, G. Vikash, Kerthivaasan R. S, Thakkasila Arun Basha (2026). CardioXAI: An Explainable Stacked Ensemble Framework for Cardiovascular Disease Risk Prediction. International Journal of Innovative Research in Multidisciplinary Education & Technology (IJIRMET), 11(3).