1. Zhi Zhang, Sheng-hua Zhong, Ahmed Fares, Yan Liu, Detecting abnormality with separated foreground and background: Mutual Generative Adversarial Networks
for video abnormal event detection, Computer Vision and Image Understanding, Volume 219, 2022, 103416, ISSN 1077-3142, https://doi.org/10.1016/j.cviu.2022.103416. 2. Jingxu Lin, Sheng-hua Zhong, Ahmed Fares, Deep hierarchical LSTM networks
with attention for video summarization, Computers & Electrical Engineering,
Volume 97, 2022, 107618, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2021.107618. 3. Sheng-Hua Zhong, Jingxu Lin, Jianglin Lu, Ahmed Fares, and Tongwei Ren. 2022. Deep Semantic and Attentive Network for Unsupervised Video Summarization. ACM Trans. Multimedia Comput. Commun. Appl. 18, 2, Article 55 (May 2022), 21 pages. DOI:https://doi.org/10.1145/3477538. Impact Factor 3.144 4. J. Jiang, A. FARES and S. Zhong, ”A Brain-Media Deep Framework Towards Seeing Imaginations Inside Brains,” in IEEE Transactions on Multimedia. doi: 10.1109/TMM.2020.2999183, Impact Factor 8.182 5. Fares, A., Zhong, S. & Jiang, J. EEG-based image classification via a region-level stacked bi-directional deep learning framework. BMC Med Inform Decis Mak 19, 268 (2019). https://doi.org/10.1186/s12911-019-0967-9, Impact Factor 2.745 7. J. Jiang, A. Fares and S. Zhong, ”A Context-Supported Deep Learning Framework for Multimodal Brain Imaging Classification,” in IEEE Transactions on Human- Machine Systems. doi: 10.1109/THMS.2019.2904615. Impact Factor 3.374 8. H.Prendinger,K.Gajananan,A.BayoumyZaki,A.Fares,R.Molenaar,D.Urbano, H. van Lint, and W. Gomaa, Tokyo virtual living lab: Designing smart cities based on the 3d internet, Internet Computing, IEEE, vol. 17, pp. 3038, Nov 2013. Impact Factor 3.513 9.Ahmed, Marwa M., Amal M. Sayed, Ghada M. Khafagy, Inas T. El Sayed, Yasmine S. Elkholy, Ahmed H. Fares, Marwa D. Hasan et al. "Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model." Journal of Primary Care & Community Health 13 (2022): 21501319221113544. 10. Ahmed, M. M., Sayed, A. M., El Abd, D., El Sayed, I. T., Elkholy, Y. S., Fares, A. H., & Fares, S. (2022). Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals. Journal of International Medical Research, 50(7), 03000605221109392. 11. Fares, Ahmed H., Mohamed I. Sharawy, and Hala H. Zayed. ”Intrusion Detection: Supervised Machine Learning.” Journal of Computing Science and Engineering 5.4 (2011): 305-313.

1. A. Fares, S. Zhong and J. Jiang ”Brain-media: A Dual Conditioned and Lateralization Supported GAN (DCLS-GAN) towards Visualization of Image-evoked Brain Activities”, 2020 ACM International Conference on Multimedia, Class A. 2. S.Zhong,A.FaresandJ.Jiang”AnAttentional-LSTMforImprovedClassification of Brain Activities Evoked by Images”, 2019 ACM International Conference on Multimedia, Class A. 3. A. Fares, S. Zhong and J. Jiang, ”Region level Bi-directional Deep Learning Framework for EEG-based Image Classification,” 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 368-373. doi: 10.1109/BIBM.2018.8621152. 4. M. Nawar, A. Fares and A. Al-Sammak, ”Rainbow Deep Reinforcement Learning Agent for Improved Solution of the Traffic Congestion,” 2019 7th International Japan-Africa Conference on Electronics, Communications, and Computations, (JAC-ECC), Alexandria, Egypt, 2019, pp. 80-83. 5. M. Nawar, A. Fares and A. Al-Sammak, ”SCMA: A Sparse Cooperative Multi- Agent Framework for Adaptive Traffic Signal Control,” 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 2019, pp. 293-300, doi: 10.1109/ICCES48960.2019.9068144. 6. Fares and W. Gomaa, Multi-agent reinforcement learning control for ramp me- tering, in Progress in Systems Engineering (H. Selvaraj, D. Zydek, and G. Chmaj, eds.), vol. 330 of Advances in Intelligent Systems and Computing, pp. 167173, Springer International Publishing, 2015. 7. Fares and W. Gomaa, Freeway ramp-metering control based on reinforcement learning, in Control Automation (ICCA), 11th IEEE International Conference on, pp. 12261231, June 2014. 8. Fares, A., A Khamis, M., & Gomaa, W. (2022). MARL-FWC: Optimal coordination of freeway traffic control measures. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 643-656). Springer, Cham.