Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Nermeen Gamal Rezk"'
Publikováno v:
Bioengineering, Vol 11, Iss 10, p 1016 (2024)
Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the “black boxes”, or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since i
Externí odkaz:
https://doaj.org/article/add4a20c88de4d2f871d6e0768213b22
Autor:
Nermeen Gamal Rezk, Abdel-Fattah Attia, Mohamed A. El-Rashidy, Ayman El-Sayed, Ezz El-Din Hemdan
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-21 (2022)
Abstract In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no e
Externí odkaz:
https://doaj.org/article/ffcdd5d2a9234e03952f0532ea39c120
Autor:
Nermeen Gamal Rezk, Ezz El-Din Hemdan, Abdel-Fattah Attia, Ayman El-Sayed, Mohamed A. El-Rashidy
Publikováno v:
Multimedia Tools and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc7728077ccff52d9cf96a9fac704052
https://doi.org/10.1007/s11042-023-15470-2
https://doi.org/10.1007/s11042-023-15470-2
Autor:
Ayman El-Sayed, Abdel-Fattah Attia, Ezz El-Din Hemdan, Mohamed A. El-Rashidy, Nermeen Gamal Rezk
Publikováno v:
Multimedia Tools and Applications. 80:773-797
This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farmi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f5d43d0cbbdf32f1aa07ac6e25e8c60
https://doi.org/10.1007/s11042-020-09740-6
https://doi.org/10.1007/s11042-020-09740-6
Publikováno v:
2016 11th International Conference on Computer Engineering & Systems (ICCES).
The main objective of the work is to improve the clustering efficiency and performance when we deal with very big datasets. This paper aims to improve the quality of XML data clustering by exploiting more features extracted from source schemas. In pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2be67749d32c5350962cd3593aad18e1
https://doi.org/10.1109/icces.2016.7821981
https://doi.org/10.1109/icces.2016.7821981