Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Aziz Ettouhami"'
Autor:
Khadija El Miloudi, Aziz Ettouhami
Publikováno v:
Journal of Engineering, Vol 2020 (2020)
Externí odkaz:
https://doaj.org/article/5931f55ab95d4382bc2265bdd990e47b
Autor:
Khadija El Miloudi, Aziz Ettouhami
Publikováno v:
Journal of Engineering, Vol 2018 (2018)
We propose a new formal model of UML use case diagram using Z notation to address some of its shortcomings. UML use case diagram has therefore become commonly used to structure functional requirements and the greatest challenge facing the software de
Externí odkaz:
https://doaj.org/article/4fd45ae5aae74c81ae34d9235c407768
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 6, Iss 1 (2013)
The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters.
Externí odkaz:
https://doaj.org/article/7ef9af76f90744c5869d984ec07af39c
Publikováno v:
Journal of Intelligent Systems, Vol 29, Iss 1, Pp 1028-1042 (2018)
This paper presents a robust, dynamic, and unsupervised fuzzy learning algorithm (RDUFL) that aims to cluster a set of data samples with the ability to detect outliers and assign the numbers of clusters automatically. It consists of three main stages
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47d1d7a81028a6e2133bc249756c3c34
https://doi.org/10.1515/jisys-2018-0030
https://doi.org/10.1515/jisys-2018-0030
Publikováno v:
Journal of Intelligent Systems, Vol 29, Iss 1, Pp 529-539 (2018)
Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::316a0b1ea025a28fbae19c1f2c6de83b
https://doaj.org/article/c05e455f432a4763b1771fe508871d43
https://doaj.org/article/c05e455f432a4763b1771fe508871d43
Autor:
Aziz Ettouhami, Khadija El Miloudi
Publikováno v:
Journal of Engineering, Vol 2020 (2020)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::477137014ea49f05fdd416532e822c72
https://doi.org/10.1155/2020/9285297
https://doi.org/10.1155/2020/9285297
Publikováno v:
Ouenniche, J, Uvalle Perez, O J & Ettouhami, A 2018, ' A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction ', Management Decision . https://doi.org/10.1108/MD-04-2018-0397
PurposeNowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.Desig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d35cd6353d42d669773ac0fe4577347a
https://www.pure.ed.ac.uk/ws/files/75797252/OuennichePerezMD2018ANewEDAS.pdf
https://www.pure.ed.ac.uk/ws/files/75797252/OuennichePerezMD2018ANewEDAS.pdf
Publikováno v:
Applied Mathematical Sciences. 8:147-156
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4add9b2892053be59753425d6c43c9db
https://doi.org/10.12988/ams.2014.311651
https://doi.org/10.12988/ams.2014.311651
Publikováno v:
IAENG Transactions on Engineering Sciences.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92cea216289d390e66e1ee47c0131b63
https://doi.org/10.1142/9789813142725_0011
https://doi.org/10.1142/9789813142725_0011
Publikováno v:
International Journal of Automation and Computing. 9:616-626
Recently, genetic algorithms (GAs) have been applied to multi-modal dynamic optimization (MDO). In this kind of optimization, an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c1de730d20c0e10b819e92f68b30e7b
https://doi.org/10.1007/s11633-012-0687-4
https://doi.org/10.1007/s11633-012-0687-4