Zobrazeno 1 - 10
of 44
pro vyhledávání: '"M. Elghamrawy"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy,
Externí odkaz:
https://doaj.org/article/c4bed0b8384c44d2bca3bf6206717c12
Publikováno v:
IEEE Access, Vol 11, Pp 65675-65688 (2023)
Real-time deep learning faces the challenge of balancing accuracy and time, especially in cybersecurity where intrusion detection is crucial. Traditional deep learning techniques have been insufficient in identifying network anomalies and intrusions.
Externí odkaz:
https://doaj.org/article/69cb6e31f54e452fa7b8356e1f22d6c6
Publikováno v:
IEEE Access, Vol 9, Pp 151476-151492 (2021)
Deep learning (DL) algorithms are crucial for predicting various diseases because they can analyze a large amount of healthcare data within a short prediction time. One of these diseases is cancer, which causes one out of six deaths worldwide. Many r
Externí odkaz:
https://doaj.org/article/8f3162fbbafd48d991f9f4387c2b7c87
Publikováno v:
Mansoura Engineering Journal. 48
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88aae674cc1b20fec62951c2e617a902
https://doi.org/10.58491/2735-4202.3025
https://doi.org/10.58491/2735-4202.3025
Publikováno v:
The Journal of Supercomputing. 78:5479-5506
Association rule mining (ARM) is a data mining technique to discover interesting associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM algorithm for compressing information in the tree structure. However, it tends
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0aaf0733f469afd7cf2f5f4d7c088914
https://doi.org/10.1007/s11227-021-04066-y
https://doi.org/10.1007/s11227-021-04066-y
Publikováno v:
International Journal of Imaging Systems and Technology
The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73a1c16bcc24979a06d31b03290e291c
https://doi.org/10.1002/ima.22644
https://doi.org/10.1002/ima.22644
Publikováno v:
The Journal of Supercomputing. 77:11187-11214
With the increasing daily production of data in recent years, indexing, storing and retrieving huge amounts of data have become a common problem, especially for multi-dimensional big data. Although R-tree has proved to be efficient for indexing multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fc9b437f9e0e31dd96ecd53c968a8b8
https://doi.org/10.1007/s11227-021-03718-3
https://doi.org/10.1007/s11227-021-03718-3
Autor:
Sally M. Elghamrawy
Publikováno v:
Future Generation Computer Systems. 109:479-487
Due to the exponential increase in the number of wireless devices and in data rates demands, the shortage of the free frequency spectrum resources becomes a critical problem. Cognitive Radio Network (CRN) is considered as an emerging technology that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d170375bbaf5d68a2bd7188876f43189
https://doi.org/10.1016/j.future.2018.08.022
https://doi.org/10.1016/j.future.2018.08.022
Publikováno v:
The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) ISBN: 9783031039171
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71bdd3409ce1feb5098adf8e2ccf101c
https://doi.org/10.1007/978-3-031-03918-8_4
https://doi.org/10.1007/978-3-031-03918-8_4
Publikováno v:
2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5be1225a4dfb64c4628f27d25e94c654
https://doi.org/10.1109/jac-ecc54461.2021.9691427
https://doi.org/10.1109/jac-ecc54461.2021.9691427