Mr. Abbas RAMMAL

Faculty of Science

Chargé de cours

Engineering and Technology, Sciences

A. Rammal, K. Ezukwoke, A. Hoayek, and M. Batton-Hubert, “Unsupervised approach for an optimal representation of the latent space of a failure analysis dataset,” The Journal of Supercomputing, vol. 80, pp. 5923–5949, 2024.  doi: 10.1007/s11227-023-05634-0.


A. Rammal, K. Ezukwoke, A. Hoayek, and M. Batton-Hubert, “Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach,” Scientific Reports, vol. 13, pp. 4934, 2023.  doi: 10.1038/s41598-023-30769-8.

A. Rammal, R. Assaf, A. Goupil, M. Kacim, and V. Vrabie, “Machine learning techniques on homological persistence features for prostate cancer diagnosis,” BMC Bioinformatics, vol. 23, 2022. doi: 10.1186/s12859-022-04992-5.

E. Yammine and A. Rammal, “Path analysis to assess socio-economic and mitigation measuredeterminants for daily coronavirus infections,” International Journal of Environmental Research and Public Health (IJERPH), vol. 18, p. 10 071, 2021. doi: 10.3390/ijerph181910071.

A. Rammal, E. Perrin, V. Vrabie, I. Bertrand, and B. Chabbert, “Classification of lignocellulosic biomass by weighted-covariance factor fuzzy c-means clustering of mid-infrared and nearinfrared spectra,” Journal of Chemometrics, vol. 31, p. 2865, 2017. doi: 10.1002/cem.2865.

A. Rammal, E. Perrin, V. Vrabie, and H. Fenniri, “Selection of discriminant midinfrared wavenumbers by combining a naïve bayesian classifier and a genetic algorithm: Application to the evaluation of lignocellulosic biomass biodegradation,” Mathematical Biosciences, vol. 289, pp. 153–161, 2017.  doi:
10.1016/j.mbs.2017.05.002.