Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Filip Nikolić"'
Publikováno v:
Metals, Vol 11, Iss 5, p 756 (2021)
This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. T
Externí odkaz:
https://doaj.org/article/6447d254bb1741e48a8de4e858c1c2d6
Autor:
Filip Nikolić
Publikováno v:
Zbornik radova Fakulteta tehničkih nauka u Novom Sadu. 37:1716-1719
Kako bi se omogućilo smanjenje potrošnje energije i code smell nepravilnosti koje se nalaze u nekoj mobilnoj aplikaciji kroz ovaj rad je prikazan pristup koji počinje definisanjem problematičnih delova koda koji se pojedinačno nazivaju code
Publikováno v:
International Journal of Metalcasting. 17:386-398
The present research deals with the detection of porosity defects in aluminum alloys using convolutional neural networks (CNNs). The goal of this research is to build a CNN model that can accurately predict porosity defects in light optical microscop
Publikováno v:
Metals
Volume 11
Issue 5
Metals, Vol 11, Iss 756, p 756 (2021)
Volume 11
Issue 5
Metals, Vol 11, Iss 756, p 756 (2021)
This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7d0683bf1329d967d745c3c4e350301
https://www.bib.irb.hr/1127638
https://www.bib.irb.hr/1127638
Purpose: Hypomagnesemia contributes to morbidity in a significant proportion of hospitalized and severely ill patients, but it could also have beneficial anticancer effects. Alimentary tract mucositis is a frequent complication of cytotoxic chemother
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=57a035e5b1ae::b946f77f1461e63fe68a4a9dcec83dc7
https://doi.org/10.21040/eom/2016.2.1
https://doi.org/10.21040/eom/2016.2.1