Zobrazeno 1 - 10
of 307
pro vyhledávání: '"Turki, Turki"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our
Externí odkaz:
https://doaj.org/article/53eed5d0c9684c328055d36e17907b7d
Autor:
Almutaani, Mansour1 (AUTHOR), Turki, Turki1 (AUTHOR) tturki@kau.edu.sa, Taguchi, Y.-H.2 (AUTHOR)
Publikováno v:
Scientific Reports. 11/6/2024, Vol. 14 Issue 1, p1-17. 17p.
Autor:
Sumaya Alghamdi, Turki Turki
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect and do no
Externí odkaz:
https://doaj.org/article/781676be39fe4c7e8ec734bfe6e6da46
Autor:
Turki, Turki1 (AUTHOR) shabib0003@stu.kau.edu.sa, Al Habib, Sarah1,2 (AUTHOR), Taguchi, Y-h.3 (AUTHOR) tag@granular.com
Publikováno v:
Mathematics (2227-7390). May2024, Vol. 12 Issue 10, p1573. 16p.
Autor:
Turki, Turki1 (AUTHOR) tturki@kau.edu.sa, Taguchi, Y-h.2 (AUTHOR) tturki@kau.edu.sa
Publikováno v:
Mathematics (2227-7390). May2024, Vol. 12 Issue 10, p1536. 27p.
Autor:
Turki, Turki, Taguchi, Y-h.
Publikováno v:
In Engineering Applications of Artificial Intelligence September 2023 124
Autor:
Alghamdi, Sumaya1,2 (AUTHOR), Turki, Turki1 (AUTHOR) tturki@kau.edu.sa
Publikováno v:
Scientific Reports. 2/23/2024, Vol. 14 Issue 1, p1-16. 16p.
Autor:
Taguchi, Y.-H., Turki, Turki
Publikováno v:
In Genomics March 2023 115(2)
Autor:
Turki, Turki, Taguchi, Y-h.
Publikováno v:
In Gene 15 February 2023 853
Autor:
Y-h. Taguchi, Turki Turki
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In
Externí odkaz:
https://doaj.org/article/b56f2023d54a4d6d9f89a800aeb113a8