Biography
Dr. Rita Zgheib is an assistant professor at the Department of Computer Engineering and Computational Science, Canadian University Dubai, UAE. She has 5+ years of academic and research experience in different university institutions in France and the UAE. Her research interests include - disease and epidemic detection, healthcare applications, IoT architecture, ontologies, semantic reasoning, Machine Learning, and software engineering. She has authored and co-authored several papers with experts from different countries (France, Italy, Norway, UAE) in international conferences and journals. She is a member of the Institute of Electrical and Electronics Engineers (IEEE) and an active member of International Program Committees, Technical Program Committees, and Advisory Committees of several academics conferences.
Dr. Zgheib holds a Ph.D. in Computer Science specialty in artificial intelligence from Paul Sabatier Toulouse University, France, where her dissertation explored novel semantic architecture for IoT healthcare applications. She also obtained an MSc Computer Science specialty in Information and Communication Systems from the University of Toulouse, France.
Academic Publications
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Abualrous, R., Zouzou, H., Zgheib, R., Hasan, A., Hijazi, B., & Kermani, A. (2025). Fairness-aware intelligent reinforcement (FAIR): An AI-powered hospital scheduling framework. Information, 16(12), Article 1039. https://doi.org/10.3390/info16121039
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Suleiman, S., Elsenousy, Y., El Naggar, R., Ramadan, M., Zgheib, R., & Kamalov, F. (2025). Ensemble System for Cybersecurity Threat Detection. In Smart Innovation Systems and Technologies (Vol. 431). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-1210-9_7
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Adithya, J., Mortezaei, P., Aljawawde, K., Mohamed, M., Zgheib, R., & Mohamed, T. (2025). Automated Crime Detection in CCTV Footage Using Machine Learning. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 236). https://doi.org/10.1007/978-981-96-0451-7_13
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Kanaparthi, Y., Shaikh, A. K., Khan, I. I., & Zgheib, R. (2025). Optimizing agricultural practices through integrated IoT and ML solutions. In Communications in Computer and Information Science: Vol. 2243. Proceedings of the 2nd International Conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024 (pp. 89–103). Springer. https://doi.org/10.1007/978-3-031-81369-6_8
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Alrous, R. A., Zgheib, R., Mashnouq, A., Menon, P., Tamimi, R. A., & Takshe, A. (2024). Smart air-quality detection using regression models. Proceedings of the 15th International Conference on Information and Communication Systems (ICICS 2024), 13–15 August 2024, Irbid, Jordan. IEEE. https://doi.org/10.1109/ICICS63486.2024.10638296
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Al-Jaghoub, J.N. et al. (2024). Deep Learning for Preventing Botnet Attacks on IoT. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2023 2023. Lecture Notes in Computer Science, vol 14542. Springer, Cham. https://doi.org/10.1007/978-3-031-60994-7_4
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Hijazi, B., Al-Daker, Y., Mahmoud, Y., Ismail, H., Zgheib, R., & Kamalov, F. (2024). The effect of imbalanced data on machine learning algorithms. InLecture Notes in Networks and Systems, Vol. 23 (pp. 887–897). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-7710-5_69
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Butt, A. H., Khan, Z., Khan, A., Ghazanfar, H., Zgheib, R., & Kamalov, F. (2024). Performance of sampling methods on imbalanced data: Comparative analysis. Advances in Science and Engineering Technology International Conferences, ASET 2024 (pp. 1-6). IEEE. https://doi.org/10.1109/ASET60340.2024.10708760
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Zgheib, R., Kristiansen, S., Conchon, E., Plageman, T., Goebel, V., & Bastide, R. (2023). A scalable semantic framework for IoT healthcare applications. Journal of Ambient Intelligence and Humanized Computing, 14(5), 4883 - 4901. https://doi.org/10.1007/s12652-020-02136-2
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Zgheib, R., Chahbandarian, G., Kamalov, F., Messiry, H. E., & Al-Gindy, A. (2023). Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19. Neurocomputing, 528, 160-177. https://doi.org/10.1016/j.neucom.2023.01.007
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Haldorai, A., Ramu, A., Mohanram, S. & Zgheib, R. (2023). Preface. In 4th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing : BDCC 2021, (pp. v-vi). Springer Cham. https://doi.org/10.1007/978-3-031-07654-1
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De Nicola, A., Zgheib, R., & Taglino, F. (2022). Toward a knowledge graph for medical diagnosis: issues and usage scenarios. In Semantic Models in IoT and Ehealth Applications (pp. 129-142). Academic Press. https://doi.org/10.1016/B978-0-32-391773-5.00013-3
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Al Mehairi, A., Zgheib, R., Abdellatif, T.M., & Conchon, E. (2022). Cyber Security Strategies While Safeguarding Information Systems in Public/Private Sectors. In F. Ortiz-Rodríguez, S. Tiwari, MA Sicilia, & A. Nikiforova (Eds), Electronic Governance with Emerging Technologies. EGETC 2022. Communications in Computer and Information Science, 1666 (pp. 49-63) . Springer, Cham. https://doi.org/10.1007/978-3-031-22950-3_5
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Zgheib, R., Chahbandarian, G., Kamalov, F., & Labban, O. E. (2021). Neural networks architecture for COVID-19 early detection. 2021 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/ISNCC52172.2021.9615883
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Zgheib, R., Kamalov, F., Chahbandarian, G., & Labban, O. E. (2021). Diagnosing COVID-19 on limited data: A comparative study of machine learning methods. In DS Huang, KH Jo, J. Li, V. Gribova & A. Hussain (Eds.), Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, Vol. 12837. (pp. 616–627). Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_52
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Academic Contributions
Selected publications
- Zgheib, Rita, Stein Kristiansen, Emmanuel Conchon, Thomas Plageman, Vera Goebel, and Rémi Bastide. "A scalable semantic framework for IoT healthcare applications." Journal of Ambient Intelligence and Humanized Computing (2020): 1-19.
- Zgheib, Rita, Firuz Kamalov, Ghazar Chahbandarian, and Osman El Labban. "Diagnosing COVID-19 on Limited Data: A Comparative Study of Machine Learning Methods." In International Conference on Intelligent Computing, pp. 616-627. Springer, Cham, 2021.
- Kamalov, F., Moussa, S., Zgheib, R., & Mashaal, O. (2020, December). Feature selection for intrusion detection systems. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 265-269). IEEE.
- Zgheib, Rita, Emmanuel Conchon, and Rémi Bastide. "Semantic Middleware Architectures for IoT Healthcare Applications." Enhanced Living Environments. Springer, Cham, 2019. 263-294.
- Zgheib, Rita. SeMoM, a semantic middleware for IoT healthcare applications. Diss. Université Paul Sabatier-Toulouse III, 2017.
- Zgheib Rita, DeNicola Antonio, Villani MariaLuisa, Conchon Emmanuel, Bastide Rémi. "A Flexible Architecture for Cognitive Sensing of Activities in Ambient Assisted Living". WETICE 2017 (Enabling Technologies: Infrastructure for Collaborative Enterprises) IEEE 26th International Conference.
- Zgheib Rita, Conchon Emmanuel, and Bastide Rémi. "Engineering IoT Healthcare Applications: Towards a Semantic Data Driven Sustainable Architecture." eHealth 360°. Springer International Publishing, 2017. 407-418.
- Faux, Francis, Rémi Bastide, Nathalie Souf, and Rita Zgheib. "Smartphone-Based Collaborative System for Wounds Tracking."8th International Conference on e-Health, Telemedicine and Social Medicine (eTELEMED 2016), colocated with other events part of DigitalWorld 2016. [More Information]
- Zgheib Rita, Bastide Rémi, and Conchon Emmanuel. "A Semantic Web-of-Things Architecture for Monitoring the Risk of Bedsores." Computational Science and Computational Intelligence (CSCI), 2015 International Conference on. IEEE, 2015.