Publications

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A
M. A. Ahajjam, Essayeh, C., Ghogho, M., et Kobbane, A., « On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets », in Conference Record - IEEE Instrumentation and Measurement Technology Conference, 2021, vol. 2021-May.
O. E. Aissaoui, Y. Madani, E. Alami El, Oughdir, L., Dakkak, A., et Y. Allioui, E., « Toward a hybrid machine learning approach for extracting and clustering learners’ behaviours in adaptive educational system », International Journal of Computing Science and Mathematics, vol. 12, p. 117-131, 2020.
L. Ajallouda, Fagroud, F. Z., Zellou, A., et Benlahmar, E. H., « K-means hac and fcm which clustering approach for arabic text? », in ACM International Conference Proceeding Series, 2020, p. 169-174.
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.
B
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. Benhar, Idri, A., et Hosni, M., « Impact of threshold values for filter-based univariate feature selection in heart disease classification », 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. 391-398.
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. Benhar, Hosni, M., et Idri, A., « Univariate and Multivariate Filter Feature Selection for Heart Disease Classification », Journal of Information Science and Engineering, vol. 38, p. 791-803, 2022.
B. Btissam et Driss, B., « Partially Connected Neural Networks for an Efficient Classification of Traffic Signs », in Conference of Open Innovation Association, FRUCT, 2021, vol. 2021-October, p. 16-23.
C
I. Chlioui, Idri, A., Abnane, I., et EZZAT, M., « Ensemble case based reasoning imputation in breast cancer classification », Journal of Information Science and Engineering, vol. 37, p. 1039-1051, 2021.
D
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
B. ElOuassif, Idri, A., Hosni, M., et Abran, A., « Classification techniques in breast cancer diagnosis: A systematic literature review », Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 9, p. 50-77, 2021.
M. Ezzarii, H. Ghazi, E., H. Ghazi, E., et F. Bouanani, E., « Epigenetic algorithm-based detection technique for network attacks », IEEE Access, vol. 8, 2020.
H
M. Hosni, García-Mateos, G., Carrillo-De-Gea, J. M., Idri, A., et Fernandez-Aleman, J. L., « A mapping study of ensemble classification methods in lung cancer decision support systems », Medical and Biological Engineering and Computing, vol. 58, p. 2177-2193, 2020.
I
A. Iazzi, Rziza, M., et Thami, R. O. Haj, « Fall detection system-based posture-recognition for indoor environments », Journal of Imaging, vol. 7, 2021.
K
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.
L
C. Lahmar et Idri, A., « Deep hybrid architectures for diabetic retinopathy classification », Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 2022.
M
E. S. Mrabtt, Lazaar, M., M. Achhab, A., et Omara, H., « Novel Convex Polyhedron Classifier for Sentiment Analysis », in Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020, 2020.
N
F. - Z. Nakach, Zerouaoui, H., et Idri, A., « Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification », Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13376 LNCS, p. 3-18, 2022.
O
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.
P
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.
R
Z. Rachidi, Chougdali, K., Kobbane, A., et Ben-Othman, J., « Network intrusion detection using Machine Learning approach », in ACM International Conference Proceeding Series, 2022, p. 13-17.
Z
H. Zerouaoui, Idri, A., Nakach, F. Z., et Hadri, R. E., « Breast Fine Needle Cytological Classification Using Deep Hybrid Architectures », Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12950 LNCS, p. 186-202, 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.

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