International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning Based Intelligent Online Exam Monitoring System |
|---|---|
| ABSTRACT | The rapid expansion of online learning post-2020 has made remote examinations essential but vulnerable to cheating like identity fraud and unauthorized aids, with 40-50% irregularity rates undermining trust. Deep learning- based monitoring systems counter this using CNNs for facial recognition, gaze tracking, pose estimation, and multimodal analysis of webcam video (30 FPS), audio, and screen logs. These provide scalable, real-time anomaly detection with >95% accuracy, tiered alerts, and tamper-proof logs while balancing privacy via ephemeral data. Advantages include cost savings, consistency over human proctors, and compatibility across devices under variable networks. |
| AUTHOR | HARSHITA GC, KAVANA KN, MADHURA SA, NIKHILESHWARI U, VAISHNAVI CS UG Students, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312146 |
| pdf/146_Deep Learning Based Intelligent Online Exam Monitoring System.pdf | |
| KEYWORDS | |
| References | [1] Istiak Ahmad, Fahad AlQurashi, Ehab Abozinadah, A Novel Deep Learning-based Online Proctoring System using Face Recognition, Eye Blinking, and Object Detection Techniques, https://thesai.org/Publications/ViewPaper?Volume=12&Issue=10&Code=I JACSA&SerialNo=94 [1] [2] Abel Thomas, Ayisha AA, George Thomas, Meenakshy R, Dini Davis, Online Exam proctoring System-A Review in Deep learning, https://ijrpr.com/uploads/V4ISSUE12/IJRPR20744.pdf [2] [3] Raksha Puthran, Anusha Prashanth Shetty, An Online Exam Proctoring System Using The GMP-DCNN Approach for the Education Sector, https://ijisae.org/index.php/IJISAE/article/view/6184 [3] [4] Dr. V. Nivedita, S. S. Karthikeiyhan, A. Tarun, A. Easwersamy, Enhanced AI Proctoring using Deep learning for Online Exam Monitoring, https://ijirt.org/publishedpaper/IJIRT177156_PAPER.pdf [4] [5] Sangeeta Lamba, Dr. Neelam Sharma, Deep Learning-Based Multimodal Cheating Detection in Online Proctored Exams, https://journal.esrgroups.org/jes/article/view/7480 [5] [6] S. Essahraui et al, Deep Learning Models for Detecting Cheating in Online Exam Proctoring Systems, https://www.techscience.com/cmc/online/detail/23970 [6] [7] Anonymous. "Online exam proctoring application using-AI." International Journal of Scientific Research and Applications (2025). https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-1440.pdf. Uses computer vision for real-time eye/hand movement detection with 91.4% F1-score. [7] [8] Anonymous. "Automated Detection of Suspicious Behavior During Online Exams Using AI." CEUR Workshop Proceedings, Vol-4036 (2025). https://ceur-ws.org/Vol 4036/Paper10.pdf. Combines AI/image processing for multi- method anomaly detection. [8] |
