Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Ahmad Hassanat"'
Publikováno v:
Jordanian Journal of Computers and Information Technology, Vol 10, Iss 3, Pp 231-246 (2024)
Genetic algorithms (GAs) are search algorithms based on population genetics and natural selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid con
Externí odkaz:
https://doaj.org/article/ddbaceeb953a4d8c8f4564fbde8b77de
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3151 (2024)
The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final opti
Externí odkaz:
https://doaj.org/article/f4b0d52d951146afa4b57fd5847b84fd
Autor:
Ahmad HASSANAT
Publikováno v:
Advances in Distributed Computing and Artificial Intelligence Journal, Vol 7, Iss 3, Pp 15-30 (2018)
Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the tim
Externí odkaz:
https://doaj.org/article/8eb04b4175e64e9f96b8c88659e144a2
Publikováno v:
Computers, Vol 8, Iss 4, p 84 (2019)
In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted us
Externí odkaz:
https://doaj.org/article/dfa240b2e27c49e1887811f4d988b3fb
Publikováno v:
2022 International Conference on Emerging Trends in Computing and Engineering Applications (ETCEA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05ef125a1d07a53066a21e359f923bac
https://doi.org/10.1109/etcea57049.2022.10009844
https://doi.org/10.1109/etcea57049.2022.10009844
Autor:
Ahmad Hassanat, Abeer Albustanji, Tarawneh, Ahmad S., Malek Alrashidi, Hani Alharbi, Mohammed Alanazi, Mansoor Alghamdi, Alkhazi, Ibrahim S., Surya Prasath, V. B.
Publikováno v:
Web of Science
Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to ext
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c32877d8f74529dfad2b2457400f847b
http://arxiv.org/abs/2111.01930
http://arxiv.org/abs/2111.01930
Autor:
Janusz V. Kozubal, Ahmad Hassanat, Ahmad S. Tarawneh, Roman J. Wróblewski, Hubert Anysz, Jónatas Valença, Eduardo Júlio
Publikováno v:
Construction and Building Materials. 359:129296
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::00f66f1fb2891a8f21134bb96ce81f52
https://doi.org/10.1016/j.conbuildmat.2022.129296
https://doi.org/10.1016/j.conbuildmat.2022.129296
Publikováno v:
Scopus-Elsevier
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a73a62b3764242890e92ac2f10086a8
Publikováno v:
Annual International Conference on Information Technology & Applications; 2016, p42-51, 10p