Computer Vision–Based Malpractice Detection System
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51163-9_11
Reference14 articles.
1. C.S. Indi, V. Pritham, V. Acharya, K. Prakasha, Detection of malpractice in E-exams by head pose and gaze estimation. Int. J. Emerg. Technol. Learn. 16 (2021). https://doi.org/10.3991/ijet.v16i08.15995
2. Z.T. Hossain, P. Roy, R. Nasir, S. Nawsheen, M.I. Hossain, Automated online exam proctoring system using computer vision and hybrid ml classifier, in 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON), (2021), pp. 14–17. https://doi.org/10.1109/RAAICON54709.2021.9929456
3. P. AnnanNaidu, S.R. Pappu, D. Priyanka, V. Yamuna, Experimental study on machine learning and neuroquantology. 20(11), 7197–7210 (2022). https://doi.org/10.14704/nq.2022.20.11.nq66714
4. M. Adil, R. Simon, S.K. Khatri, Automated invigilation system for detection of suspicious activities during examination, in 2019 Amity International Conference on Artificial Intelligence (AICAI), (2019), pp. 361–366. https://doi.org/10.1109/AICAI.2019.8701263
5. R. Kulkarni, Real time automated invigilator in classroom monitoring using computer vision (April 8, 2019). 2nd International Conference on Advances in Science & Technology (ICAST) 2019 on 8th, 9th April 2019 by KJ Somaiya Institute of Engineering & Information Technology, Mumbai, India. Available at SSRN: https://ssrn.com/abstract=3367715 or https://doi.org/10.2139/ssrn.3367715
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