Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Hamza, Alami"'
Publikováno v:
IEEE Access, Vol 12, Pp 172027-172045 (2024)
Network Intrusion Detection Systems (NIDS) play a crucial role in ensuring cybersecurity across various digital infrastructures. However, traditional NIDS face significant challenges, including high computational and storage costs, as well as privacy
Externí odkaz:
https://doaj.org/article/2458e3c55a9b4ed7a0d461f3c79f232b
Autor:
Hamza Alami, Abdelkader El Mahdaouy, Abdessamad Benlahbib, Noureddine En-Nahnahi, Ismail Berrada, Said El Alaoui Ouatik
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 8, Pp 101709- (2023)
As of late, various deep learning techniques and methods have shown their superiority to feature-based and shallow learning techniques in the field of open-domain question–answering systems (OpenQAS). However, only a few works adopted these techniq
Externí odkaz:
https://doaj.org/article/bf0d2829c7cf48af8d05633e85cc70d3
Publikováno v:
In Journal of King Saud University - Computer and Information Sciences October 2022 34(9):6583-6594
Publikováno v:
In Journal of King Saud University - Computer and Information Sciences June 2022 34(6) Part B:3758-3765
Publikováno v:
IEEE Access, Vol 10, Pp 124766-124776 (2022)
Federated learning (FL) has been proposed as a machine learning approach to collaboratively learn a shared prediction model. Although, during FL training, only a subset of workers participate in each round, existing approaches introduce model bias wh
Externí odkaz:
https://doaj.org/article/85a8ddad70754a92ab7edb744a6cae9e
Publikováno v:
In Journal of King Saud University - Computer and Information Sciences February 2021 33(2):218-224
Autor:
Nabil Burmani, Hamza Alami, Said Lafkiar, Mohamed Zouitni, Mohammed Taleb, Noureddine En Nahnahi
Publikováno v:
2022 International Conference on Intelligent Systems and Computer Vision (ISCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::296e8e21f2dd946ba4d6c421d3d4f2b6
https://doi.org/10.1109/iscv54655.2022.9806127
https://doi.org/10.1109/iscv54655.2022.9806127
Publikováno v:
SemEval@ACL/IJCNLP
Scopus-Elsevier
Scopus-Elsevier
Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The first solution, which follows a supervised approach, is based on SpanBERT
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e45fa3ad2e96c4f02ef6edbdd81d79f
https://doi.org/10.18653/v1/2021.semeval-1.116
https://doi.org/10.18653/v1/2021.semeval-1.116
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030366520
Question Answering Systems (QASs) aim to provide a precise answer to questions written in natural language. Passages extraction is a challenging task that affects directly the performance of any QAS. In this paper, we propose a passages extraction me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55b16857132ef6c967de383dcf290d8a
https://doi.org/10.1007/978-3-030-36653-7_23
https://doi.org/10.1007/978-3-030-36653-7_23
Publikováno v:
Scopus-Elsevier
SemEval@COLING
SemEval@COLING
AraBERT is an Arabic version of the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) model. The latter has achieved good performance in a variety of Natural Language Processing (NLP) tasks. In this paper, we propose an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76056109e70e041f0523ce4e09b8d0d5
http://www.scopus.com/inward/record.url?eid=2-s2.0-85123935005&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85123935005&partnerID=MN8TOARS