Current - Issue

1. M. Masud, A. G. Alqahtani, S. M. M. Rahman, and M. S. Hossain, "Smart Online Exam Proctoring Assist for Cheating Detection,"
Computers and Electrical Engineering, vol. 101, pp. 108–120, 2022.
2. M. A. Al-Airaji, H. T. Hazim, and A. S. Abdullah, "Automated Cheating Detection in Examination Halls Using Video Surveillance and
Deep Learning," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 5, pp. 115–123, 2022.
3. Y. Li, X. Zhang, and H. Wang, "A Multi-Index Examination Cheating Detection Method Based on Neural Networks," IEEE Access,
vol. 7, pp. 156789–156799, 2019.
4. "Detection of Exam Cheating through Surveillance using Face Detection with AI," International Journal of Innovative Research in Science,
Engineering and Technology (IJIRSET), 2025.
5. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016.
6. G. Jocher, A. Chaurasia, and the Ultralytics Team, "YOLOv8: State-of-the-Art Real-Time Object Detection," Ultralytics, 2023.
7. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
8. G. Bradski, "The OpenCV Library," Dr. Dobb's Journal of Software Tools, 2000.
9. Raspberry Pi Foundation, "Raspberry Pi Pico Documentation," 2023.
10. Espressif Systems, "ESP32-CAM Technical Reference Manual," 2023.
11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural
Information Processing Systems (NeurIPS), vol. 25, pp. 1097–1105, 2012.
12. R. Girshick, "Fast R-CNN," Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448, 2015.
13. J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, 2015.
14. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal Loss for Dense Object Detection," Proceedings of the IEEE International
Conference on Computer Vision (ICCV), pp. 2980–2988, 2017.
15. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge,"
International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010.
16. A. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," arXiv:1704.04861, 2017.17. J. Deng et al., "ImageNet: A Large-Scale Hierarchical Image Database," Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), pp. 248–255, 2009.
18. M. Abadi et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems," Google Research, 2016.
19. Raspberry Pi Foundation, Getting Started with Raspberry Pi Pico, Raspberry Pi Press, Cambridge, UK, 2021.
20. Espressif Systems, ESP32 Technical Reference Manual, Version 4.9, Shanghai, China, 2023.
21. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, 2008.
22. A. Rosebrock, Practical Python and OpenCV, PyImageSearch, 2021.
23. U. D. of Education, "Artificial Intelligence in Education: Challenges and Opportunities," U.S. Department of Education, Washington, DC,
USA, 2023.
24. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), pp. 886–893, 2005.
25. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.