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C
R. Zarnoufi et Abik, M., « Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media », Communications in Computer and Information Science, vol. 1577 CCIS, p. 223-235, 2022.
A. Porebski, Alimoussa, M., et Vandenbroucke, N., « Comparison of color imaging vs. hyperspectral imaging for texture classification », Pattern Recognition Letters, vol. 161, p. 115-121, 2022.
B. Karbal et Romadi, R., « A Comparison of Different Machine Learning Algorithms for Intrusion Detection », Communications in Computer and Information Science, vol. 1264, p. 157-169, 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.
B. Bousarhane et Bouzidi, D., « Convolutional Neural Networks for Traffic Signs Recognition », Communications in Computer and Information Science, vol. 1264, p. 73-91, 2020.
D
H. Benhar, Idri, A., et Fernández-Alemán, J. L., « Data preprocessing for heart disease classification: A systematic literature review. », Computer Methods and Programs in Biomedicine, vol. 195, 2020.
H. Benbriqa, Abnane, I., Idri, A., et Tabiti, K., « Deep and Ensemble Learning Based Land Use and Land Cover Classification », Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12951 LNCS, p. 588-604, 2021.
H. Zerouaoui et Idri, A., « Deep hybrid architectures for binary classification of medical breast cancer images », Biomedical Signal Processing and Control, vol. 71, 2022.
C. Lahmar et Idri, A., « Deep hybrid architectures for diabetic retinopathy classification », Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 2022.
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.
A. Abouaomar, Mlika, Z., Filali, A., Cherkaoui, S., et Kobbane, A., « A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks », in Proceedings - Conference on Local Computer Networks, LCN, 2021, vol. 2021-October, p. 273-280.
U. Bayram et Benhiba, L., « Determining a Person's Suicide Risk by Voting on the Short-Term History of Tweets for the CLPsych 2021 Shared Task », in Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021, 2021, p. 81-86.
J. Elhachmi, « Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio-based internet of things », IET Networks, vol. 11, p. 207-220, 2022.
A. Filali, Mlika, Z., Cherkaoui, S., et Kobbane, A., « Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services », IEEE Transactions on Network Science and Engineering, vol. 9, p. 2174-2187, 2022.
E
K. A. AlAfandy, Omara, H., El-Sayed, H. S., Baz, M., Lazaar, M., Faragallah, O. S., et M. Achhab, A., « Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM », Computers, Materials and Continua, vol. 72, p. 739-753, 2022.
M. A. Ahajjam, Licea, D. B., Ghogho, M., et Kobbane, A., « Electric Power Quality Disturbances Classification based on Temporal-Spectral Images and Deep Convolutional Neural Networks », in 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, 2020, p. 1701-1706.
O. Bourja, Maach, A., Zannouti, Z., Derrouz, H., Mekhzoum, H., Abdelali, H. A., Thami, R. O. H., et Bourzeix, F., « End-to-End Car Make and Model Classification using Compound Scaling and Transfer Learning », International Journal of Advanced Computer Science and Applications, vol. 13, p. 994-1001, 2022.
H. Derrouz, Cabri, A., H. Abdelali, A., R. Thami, O. Haj, Bourzeix, F., Rovetta, S., et Masulli, F., « End-to-end quantum-inspired method for vehicle classification based on video stream », Neural Computing and Applications, vol. 34, p. 5561-5576, 2022.
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.
M. A. Ahajjam, D. Licea, B., Ghogho, M., et Kobbane, A., « Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting », Applied Energy, vol. 326, 2022.
N. Boudad, Ezzahid, S., Faizi, R., et Thami, R. O. H., « Exploring the Use of Word Embedding and Deep Learning in Arabic Sentiment Analysis », Advances in Intelligent Systems and Computing, vol. 1105 AISC, p. 243-253, 2020.
G
N. Ounasser, Rhanoui, M., Mikram, M., et Asri, B. E., « Generative and Autoencoder Models for Large-Scale Mutivariate Unsupervised Anomaly Detection », Smart Innovation, Systems and Technologies, vol. 237, p. 45-58, 2022.
H
A. E. Ouardi, Rhanoui, M., Benlarabi, A., et Asri, B. E., « Hybrid Prototypical Networks Augmented by a Non-linear Classifier », in International Conference on Enterprise Information Systems, ICEIS - Proceedings, 2021, vol. 1, p. 790-794.
I
B. Sabiri, Asri, B., et Rhanoui, M., « Impact of Hyperparameters on the Generative Adversarial Networks Behavior », in International Conference on Enterprise Information Systems, ICEIS - Proceedings, 2022, vol. 1, p. 428-438.
K
L. Ajallouda, Fagroud, F. Z., Zellou, A., et Lahmar, E. B., « KP-USE: An Unsupervised Approach for Key-Phrases Extraction from Documents », International Journal of Advanced Computer Science and Applications, vol. 13, p. 283-289, 2022.

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