1. Ali, L., Manzoor, N., Masood, H. A., & Abbas, A. (2024). Nanotechnology-Enabled Approaches to Mitigating Abiotic Stresses in Agricultural Crops. In Molecular Dynamics of Plant Stress and its Management (pp. 621-650). Singapore: Springer Nature Singapore.
2. Asep, H. S. (2019, July). A design of continuous user verification for online exam proctoring on M-learning. In 2019 international conference on electrical engineering and informatics (ICEEI) (pp. 284-289). IEEE.
3. Erdem, B., & Karabatak, M. (2025). Cheating Detection in Online Exams Using Deep Learning and Machine Learning. Applied Sciences (2076-3417), 15(1).
4. Fatima, S., Jennings, N. R., & Wooldridge, M. (2024). Learning to resolve social dilemmas: a survey. Journal of Artificial Intelligence Research, 79, 895-969.
5. Hu, Z., Jing, Y., Wu, G., & Wang, H. (2024). Multi-Perspective Adaptive Paperless Examination Cheating Detection System Based on Image Recognition. Applied Sciences, 14(10), 4048.
6. Saleem, B., Ahmed, M., Zahra, M., Hassan, F., Iqbal, M. A., & Muhammad, Z. (2024). A survey of cybersecurity laws, regulations, and policies in technologically advanced nations: A case study of Pakistan to bridge the gap. International Cybersecurity Law Review, 5(4), 533-561.
7. Singh, T., Nair, R. R., Babu, T., & Duraisamy, P. (2024). Enhancing academic integrity in online assessments: Introducing an effective online exam proctoring model using yolo. Procedia Computer Science, 235, 1399-1408.
8. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint, arXiv:1804.02767.
9. Vinyals, O., Blundell, C., Lillicrap, T., & Wierstra, D. (2016). Matching networks for one shot learning. Advances in neural information processing systems, 29.
10. Winiecki, E., Pawlicki, M., Pawlicka, A., Kozik, R., & Choraś, M. (2025, April). Evaluation of Selected Few-Shot Learning Methods in Network Intrusion Detection. In International Conference on Advanced Information Networking and Applications (pp. 10-20). Cham: Springer Nature Switzerland.
11. Zeng, W. (2024). Image data augmentation techniques based on deep learning: A survey. Mathematical Biosciences and Engineering, 21(6), 6190-6224.
