Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Mohammed Lamine Bouchouia"'
Autor:
Mohammed Lamine Bouchouia, Houda Labiod, Ons Jelassi, Jean-Philippe Monteuuis, Wafa Ben Jaballah, Jonathan Petit, Zonghua Zhang
Publikováno v:
Vehicular Communications. 41:100586
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1767d696b928d4073b1802913d73d18
https://doi.org/10.1016/j.vehcom.2023.100586
https://doi.org/10.1016/j.vehcom.2023.100586
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.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70f0ab8a161524c99804cc74aabdc3a3
https://doi.org/10.14722/autosec.2022.23014
https://doi.org/10.14722/autosec.2022.23014
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:
Computational Statistics & Data Analysis. 158:107191
A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic. Due to the regeneration, prediction is based on a daily modeling using a vector autoregressive model that combine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::334f45dec5302739049725eeb807b039
https://doi.org/10.1016/j.csda.2021.107191
https://doi.org/10.1016/j.csda.2021.107191