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Abdul Wahab, O., Rjoub, G., Bentahar, J., & Cohen, R. (2022). Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems. Information Sciences, 601, 189-206. Lien externe
Abdul Wahab, O., Cohen, R., Bentahar, J., Otrok, H., Mourad, A., & Rjoub, G. (2020). An endorsement-based trust bootstrapping approach for newcomer cloud services. Information Sciences, 527, 159-175. Lien externe
Bataineh, A. S., Bentahar, J., Mizouni, R., Abdul Wahab, O., Rjoub, G., & El Barachi, M. (2022). Cloud computing as a platform for monetizing data services: A two-sided game business model. IEEE Transactions on Network and Service Management, 19(2), 1336-1350. Lien externe
Bataineh, A. S., Bentahar, J., Abdul Wahab, O., Mizouni, R., & Rjoub, G. (décembre 2020). A game-based secure trading of big data and IoT services: blockchain as a two-sided market [Communication écrite]. Service-Oriented Computing, Dubai, United Arab Emirates. Non disponible
Rjoub, G., Bentahar, J., Abdul Wahab, O., Mizouni, R., Song, A., Cohen, R., Otrok, H., & Mourad, A. (2023). A Survey on Explainable Artificial Intelligence for Cybersecurity. IEEE Transactions on Network and Service Management, 20(4), 5115-5140. Lien externe
Rjoub, G., Bentahar, J., & Abdul Wahab, O. (juin 2023). Explainable Trust-aware Selection of Autonomous Vehicles Using LIME for One-Shot Federated Learning [Communication écrite]. International Wireless Communications and Mobile Computing (IWCMC 2023), Marrakesh, Morocco. Lien externe
Rjoub, G., Abdul Wahab, O., Bentahar, J., Cohen, R., & Bataineh, A. S. (2022). Trust-augmented deep reinforcement learning for federated learning client selection. Information Systems Frontiers. Lien externe
Rjoub, G., Abdul Wahab, O., Bentahar, J., & Bataineh, A. (2022). Trust-driven reinforcement selection strategy for federated learning on IoT devices. Computing. Lien externe
Rjoub, G., Bentahar, J., Abdul Wahab, O., & Saleh Bataineh, A. (2021). Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems. Concurrency and Computation: Practice and Experience, 33(23), e5919-e5919. Lien externe
Rjoub, G., Abdul Wahab, O., Bentahar, J., & Bataineh, A. S. (août 2021). Improving autonomous vehicles safety in snow weather using federated YOLO CNN learning [Communication écrite]. 17th International Conference on Mobile Web and Intelligent Information Systems. Non disponible
Rjoub, G., Bentahar, J., & Abdul Wahab, O. (2020). BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments. Future Generation Computer Systems, 110, 1079-1097. Lien externe
Rjoub, G., Abdul Wahab, O., Bentahar, J., & Bataineh, A. (décembre 2020). A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT devices [Communication écrite]. Service-Oriented Computing, Dubai, United Arab Emirates. Non disponible
Rjoub, G., Bentahar, J., Abdul Wahab, O., & Bataineh, A. (août 2019). Deep smart scheduling: A deep learning approach for automated big data scheduling over the cloud [Communication écrite]. 7th International Conference on Future Internet of Things and Cloud (FiCloud 2019), Istanbul, Turkey. Lien externe
