Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Ons Jelassi"'
Autor:
Mohammed Lamine Bouchouia, Houda Labiod, Ons Jelassi, Jean-Philippe Monteuuis, Wafa Ben Jaballah, Jonathan Petit, Zonghua Zhang
Publikováno v:
Vehicular Communications. 41:100586
Autor:
Mohammed Lamine Bouchouia, Jean-Philippe Monteuuis, Houda Labiod, Ons Jelassi, Wafa Ben Jaballah, Jonathan Petit
Publikováno v:
Proceedings Fourth International Workshop on Automotive and Autonomous Vehicle Security.
Autor:
Houda Labiod, Ons Jelassi, Mohammed Lamine Bouchouia, Jean Philippe Monteuuis, Jonathan Petit, Wafa Ben Jaballah
Publikováno v:
Research Challenges in Information Science ISBN: 9783030750176
RCIS
RCIS
This paper proposes a novel approach to apply machine learning techniques to data collected from emerging cooperative intelligent transportation systems (C-ITS) using Vehicle-to-Vehicle (V2V) broadcast communications. Our approach considers temporal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e192feb8af58f4672431130930cdd44
https://doi.org/10.1007/978-3-030-75018-3_30
https://doi.org/10.1007/978-3-030-75018-3_30
Publikováno v:
ICAIIC
2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
ICAIIC 2020: 2nd International Conference on Artificial Intelligence in Information and Communication
ICAIIC 2020: 2nd International Conference on Artificial Intelligence in Information and Communication, Feb 2020, Fukuoka, Japan. pp.494-500, ⟨10.1109/ICAIIC48513.2020.9065280⟩
2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
ICAIIC 2020: 2nd International Conference on Artificial Intelligence in Information and Communication
ICAIIC 2020: 2nd International Conference on Artificial Intelligence in Information and Communication, Feb 2020, Fukuoka, Japan. pp.494-500, ⟨10.1109/ICAIIC48513.2020.9065280⟩
International audience; Wireless connectivity evolution increased the volume of acquired available data in different Internet of Things based industries. Data quality and processing time are the most challenging issues for successful data analytic al
Publikováno v:
ECML PKDD 2019-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ECML PKDD 2019-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2019, Würzburg, Germany
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461461
ECML/PKDD (2)
ECML PKDD 2019-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2019, Würzburg, Germany
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461461
ECML/PKDD (2)
The development of cluster computing frameworks has allowed practitioners to scale out various statistical estimation and machine learning algorithms with minimal programming effort. This is especially true for machine learning problems whose objecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9735bc9db56ddc97923d0ce820066242
https://inria.hal.science/hal-02166428/document
https://inria.hal.science/hal-02166428/document
Publikováno v:
Recent advances in Nonparametric Statistics, ISNPS 2018, Avignon, Springer Verlag, Berlin.
Recent advances in Nonparametric Statistics, ISNPS 2018, Avignon, Springer Verlag, Berlin., 2018
Springer Proceedings in Mathematics & Statistics ISBN: 9783319969404
Nonparametric Statistics 3rd ISNPS, Avignon, France, June 2016
4. Conference for the International Society of Nonparametric Statistics
4. Conference for the International Society of Nonparametric Statistics, Jun 2016, Avignon, France. ⟨10.1007/978-3-319-96941-1_13⟩
Recent advances in Nonparametric Statistics, ISNPS 2018, Avignon, Springer Verlag, Berlin., 2018
Springer Proceedings in Mathematics & Statistics ISBN: 9783319969404
Nonparametric Statistics 3rd ISNPS, Avignon, France, June 2016
4. Conference for the International Society of Nonparametric Statistics
4. Conference for the International Society of Nonparametric Statistics, Jun 2016, Avignon, France. ⟨10.1007/978-3-319-96941-1_13⟩
The goal of this contribution is to develop subsampling methods in the framework of big data and to show their feasibility in a simulation study. We argue that using different subsampling distributions with different subsampling sizes brings a lot of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::494b1cd2a1303a96c6a5b24f1de3da47
https://hal.archives-ouvertes.fr/hal-01707500
https://hal.archives-ouvertes.fr/hal-01707500
Publikováno v:
INNS Conference on Big Data
INNS Conference on Big Data (INNS-BigData 2015)
INNS Conference on Big Data (INNS-BigData 2015), Aug 2015, San Francisco, United States
INNS Conference on Big Data (INNS-BigData 2015)
INNS Conference on Big Data (INNS-BigData 2015), Aug 2015, San Francisco, United States
Various appealing ideas have been recently proposed in the statistical literature to scale-up machine learning techniques and solve predictive/inferential problems from “Big Datasets”. Beyond the massively parallelized and distributed approaches
Autor:
Ons Jelassi, Olivier Paul
Publikováno v:
Annales Des Télécommunications. 62:1388-1400
Packet classification is a central function in filtering systems such as firewalls or intrusion detection mechanisms. Several mechanisms for fast packet classification have been proposed. But, existing algorithms are not always scalable to large filt
Autor:
O. Paul, Ons Jelassi
Publikováno v:
2006 Workshop on High Performance Switching and Routing.
Packet classification is a central function in several network applications such as firewalls and QoS-enhanced routers. Several schemes were proposed for fast packet classification, but few ones support incremental updates. In this paper, we present
Autor:
AMANULLAH, MOHAMED AHZAM1 mamanullah@deakin.edu.au, LOKE, SENG W.1 seng.loke@deakin.edu.au, CHHETRI, MOHAN BARUWAL2 mohan.baruwalchhetri@data61.csiro.au, DOSS, ROBIN1 robin.doss@deakin.edu.au
Publikováno v:
ACM Computing Surveys. Jan2024, Vol. 56 Issue 1, p1-38. 38p.