• Nguyen Tien Dat Faculty of Information Technology, Hung Yen University of Technology and Education
  • Nguyen Truong Phuc Faculty of Information Technology, Hung Yen University of Technology and Education
  • Pham Minh Chuan Faculty of Information Technology, Hung Yen University of Technology and Education
Keywords: Anomaly detection, machine learning, deep learning, Yolo, Recurrent Neural Network (RNN), OpenPose


The phenomenon of academic dishonesty during examinations constitutes a grave concern, exerting adverse implications on the educational standard. Despite an extensive body of research addressing this issue, the practical application of machine learning technology remains significantly constrained. Consequently, the imperative to deploy machine learning mechanisms for the identification and mitigation of irregularities and fraudulent activities becomes paramount. This study endeavors to employ sophisticated deep learning models, including Yolov3 (You Only Look Once), OpenPose, RNN, among others, for the purpose of discerning behavioral patterns exhibited by examination candidates. Empirical findings reveal a commendable model accuracy, approximately reaching 83 %. This research investigation serves as a foundational framework for the development of an automated anomaly detection system within educational settings. Such a system is poised to alleviate the burden on examination supervisors, optimize examination room management, and ultimately enhance the overall educational quality.


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How to Cite
Nguyen Tien Dat, Nguyen Truong Phuc, & Pham Minh Chuan. (2023). APPLICATION OF MACHINE LEARNING IN IMAGE RECOGNITION TO DETECT SOME ABNORMALITIES IN THE EXAMINATION ROOMS. UTEHY Journal of Science and Technology, 40, 27-32. Retrieved from