 | Monter d'un niveau |
Abdul Wahab, O. (2022). Intrusion detection in the IoT under data and concept drifts: Online deep learning approach. IEEE Internet of Things Journal, 9(20), 19706-19716. Lien externe
Abdul Wahab, O. (2022). An optimized K-means clustering approach on top of MapReduce. Dans Peter, J. D., Fernandes, S. L., & Alavi, A. H. (édit.), Disruptive Technologies for Big Data and Cloud Applications (Vol. 905, 19-25). Lien externe
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
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
Farhat, P., Arisdakessian, S., Abdul Wahab, O., Mourad, A., & Ould-Slimane, H. (mai 2022). Machine learning based container placement in on-demand clustered fogs [Communication écrite]. International Wireless Communications and Mobile Computing (IWCMC 2022), Dubrovnik, Croatia. 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., 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
