Epidemiological breast cancer prediction by country: A novel machine learning approach.

Autor: EL HAJI, Hasna¹ (AUTHOR) hasna.elhaji@uir.ac.ma, SBIHI, Nada¹ (AUTHOR), GUERMAH, Bassma¹ (AUTHOR), SOUADKA, Amine² (AUTHOR), GHOGHO, Mounir¹ (AUTHOR)

Publikováno v: PLoS ONE. 8/14/2024, Vol. 19 Issue 8, p1-20. 20p.

Impact of traffic mix on caching performance in a content-centric network

Autor: Fricker, Christine, Robert, Philippe, Roberts, James, Sbihi, Nada

For a realistic traffic mix, we evaluate the hit rates attained in a two-layer cache hierarchy designed to reduce Internet bandwidth requirements. The model identifies four main types of content, web, file sharing, user generated content and video on

Externí odkaz: http://arxiv.org/abs/1202.0108

Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning–Based Models.

Autor: El Haji, Hasna, Souadka, Amine, Patel, Bhavik N., Sbihi, Nada, Ramasamy, Gokul, Patel, Bhavika K., Ghogho, Mounir, Banerjee, Imon

Publikováno v: JCO Clinical Cancer Informatics; 8/11/2023, Vol. 7, p1-14, 14p

Improving Machine Translation of Arabic Dialects through Multi-Task Learning

Autor: Moukafih, Youness, Sbihi, Nada, Ghogho, Mounir, Smaïli, Kamel

Publikováno v: 20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021
20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021, Dec 2021, MILAN/Virtual, Italy

International audience; Neural Machine Translation (NMT) systems have been shown to perform impressively on many language pairs compared to Statistical Machine Translation (SMT). However, these systems are data-intensive, which is problematic for the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::21a4397c2b2e88f0cc92cde3e3cb2bfb
https://hal.science/hal-03435996/file/Springer_Lecture_Notes_in_Computer_Science__4_.pdf

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