Publications

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2022
M. Z. Wadghiri, Idri, A., T. Idrissi, E., et Hakkoum, H., « Ensemble blood glucose prediction in diabetes mellitus: A review », Computers in Biology and Medicine, vol. 147, 2022.
B. Sabiri, Asri, B. E., et Rhanoui, M., « Mechanism of Overfitting Avoidance Techniques for Training Deep Neural Networks », in International Conference on Enterprise Information Systems, ICEIS - Proceedings, 2022, vol. 1, p. 418-427.
H. Hammouchi, Nejjari, N., Mezzour, G., Ghogho, M., et Benbrahim, H., « STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk », IEEE Transactions on Dependable and Secure Computing, 2022.
2021
A. Houmz, Mezzour, G., Zkik, K., Ghogho, M., et Benbrahim, H., « Detecting the impact of software vulnerability on attacks: A case study of network telescope scans », Journal of Network and Computer Applications, vol. 195, 2021.
M. Z. Wadghiri, Idri, A., et Idrissi, T. E., « Ensemble Regression for Blood Glucose Prediction », Advances in Intelligent Systems and Computing, vol. 1365 AIST, p. 544-554, 2021.
E. T. Idrissi et Idri, A., « Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction », Communications in Computer and Information Science, vol. 1400 CCIS, p. 347-365, 2021.
Y. N. Malek, Najib, M., Bakhouya, M., et Essaaidi, M., « Multivariate deep learning approach for electric vehicle speed forecasting », Big Data Mining and Analytics, vol. 4, p. 56-64, 2021.
I. Gryech, Ben-Aboud, Y., Ghogho, M., et Kobbane, A., « On spatial prediction of urban air pollution », in 2021 17th International Conference on Intelligent Environments, IE 2021 - Proceedings, 2021.
S. Lahrichi, Rhanoui, M., Mikram, M., et Asri, B. E., « Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction », in Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021, 2021, p. 322-328.
2020
Z. Berradi, Lazaar, M., Mahboub, O., et Omara, H., « A comprehensive review of artificial intelligence techniques in financial market », in Colloquium in Information Science and Technology, CIST, 2020, vol. 2020-June, p. 367-371.
A. F. Gad et F. Jarmouni, E., « Crowd Density Estimation Method Using Reduced Features Set », in 2020 International Conference on Electrical and Information Technologies, ICEIT 2020, 2020.
E. T. Idrissi et Idri, A., « Deep Learning for Blood Glucose Prediction: CNN vs LSTM », Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12250 LNCS, p. 379-393, 2020.
E. A. Fazziki, Y. Alami, E. Madani El, O. Aissaoui, E., Y. Allioui, E., et Benbrahim, M., « Improving Collaborative Filtering Approach by Leveraging Opposite Users », Advances in Intelligent Systems and Computing, vol. 1102 AISC, p. 136-145, 2020.
I. Gryech, Ghogho, M., Elhammouti, H., Sbihi, N., et Kobbane, A., « Machine learning for air quality prediction using meteorological and traffic related features », Journal of Ambient Intelligence and Smart Environments, vol. 12, p. 379-391, 2020.
E. O. Aissaoui, Y. Madani, E. Alami El, Oughdir, L., Dakkak, A., et Y. Allioui, E., « Mining Learners’ Behaviors: An Approach Based on Educational Data Mining Techniques », Advances in Intelligent Systems and Computing, vol. 1076, p. 655-670, 2020.
E. O. Aissaoui, Y. Madani, E. Alami El, Oughdir, L., Dakkak, A., et Y. Allioui, E., « A Multiple Linear Regression-Based Approach to Predict Student Performance », Advances in Intelligent Systems and Computing, vol. 1102 AISC, p. 9-23, 2020.
S. Elmidaoui, Cheikhi, L., Idri, A., et Abran, A., « Predicting software maintainability using ensemble techniques and stacked generalization », in CEUR Workshop Proceedings, 2020, vol. 2725.
A. Filali, Mlika, Z., Cherkaoui, S., et Kobbane, A., « Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications », IEEE Transactions on Vehicular Technology, vol. 69, p. 15947-15963, 2020.
E. T. Idrissi, Idri, A., Kadi, I., et Bakkoury, Z., « Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: A comparative study », in HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, 2020, p. 337-344.

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