Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Jalal, Laassiri"'
Autor:
Thérence Nibareke, Jalal Laassiri
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-18 (2020)
Abstract Introduction Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increasing. The tools and
Externí odkaz:
https://doaj.org/article/cced16a888da4ee1ba5257d74e9ffde8
Publikováno v:
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 1, September 2011, 149-154
This paper present a survey and discussion of the Reference Model for Open Distributed Processing (RM-ODP) viewpoints; oriented approaches to requirements engineering viewpoint and a presentation of new work in the application wireless mobile phone,
Externí odkaz:
http://arxiv.org/abs/1204.6729
Publikováno v:
Journal of Big Data, Vol 5, Iss 1, Pp 1-17 (2018)
Abstract In this paper, we present a statistical model performed on the basis of a patient dataset. This model predicts efficiently the brain disease risk. Multiple regression was used to build the statistical model. The least squares estimation prob
Externí odkaz:
https://doaj.org/article/4fb7646f128d4c2f8c15a5304ff728be
Autor:
Jalal Laassiri, Thérence Nibareke
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-18 (2020)
Introduction Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increasing. The tools and models ha
Publikováno v:
Distributed Sensing and Intelligent Systems ISBN: 9783030642570
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ea888e5bc39164a91c1215e1fb2b99b
https://doi.org/10.1007/978-3-030-64258-7_1
https://doi.org/10.1007/978-3-030-64258-7_1
Autor:
Majda El Mariouli, Jalal Laassiri
Publikováno v:
Distributed Sensing and Intelligent Systems ISBN: 9783030642570
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ef4d4ced21ca0f7615adf758a3e8f53
https://doi.org/10.1007/978-3-030-64258-7_19
https://doi.org/10.1007/978-3-030-64258-7_19
Publikováno v:
Internet of Everything and Big Data ISBN: 9781003038412
Internet of Everything and Big Data
Internet of Everything and Big Data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25175ad3437ccddd4d69df6d6d3893af
https://doi.org/10.1201/9781003038412-6
https://doi.org/10.1201/9781003038412-6
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030366735
Cloud computing provides on-demand services over the Internet using methods that manages the amount of virtual storage. The key features of cloud computing are that user has no expensive IT infrastructure and the cost of their services is lower. Toda
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abae5caf22878ba7bb7e778eaaf90913
https://doi.org/10.1007/978-3-030-36674-2_51
https://doi.org/10.1007/978-3-030-36674-2_51
Publikováno v:
Applied and Numerical Harmonic Analysis ISBN: 9783030352011
We introduce a new framework of local and adaptive manifold embedding for Gaussian regression. The proposed method, which can be generalized on any bounded domain in \(\mathbb {R}^n\), is used to construct a smooth vector field from line integral on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c578f88aedfd7c93e9ca46a9781e0ee
https://doi.org/10.1007/978-3-030-35202-8_5
https://doi.org/10.1007/978-3-030-35202-8_5