Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1075
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Örnek, Mustafa Nevzat | - |
dc.contributor.author | Örnek, Humar Kahramanlı | - |
dc.date.accessioned | 2021-12-13T10:34:39Z | - |
dc.date.available | 2021-12-13T10:34:39Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2193-4126 | - |
dc.identifier.issn | 2193-4134 | - |
dc.identifier.uri | https://doi.org/10.1007/s11694-021-00923-9 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1075 | - |
dc.description.abstract | In this paper, a deep learning approach to predict carrots volume according to the physical properties was designed. A total of 464 carrots were used for volume prediction. The used carrots were taken from Kasinhani, Konya. First, the data was produced. For this, the length, the diameters with 5 cm intervals, and the volume of each carrot were measured and recorded. The measurements were done using a steel ruler, a vernier caliper, and a glass graduated cylinder. Two deep learning methods: DFN and LSTM were developed to predict carrot volume. The developed systems were implemented with the Keras library for Python. Statistical measures such as Root Mean Squared Error, Mean Absolute Error, and R-2 were used to determine the predicting accuracy of the system. Both methods produced very close values. DFN and LSTM networks achieved 0.9765 and 0.9766 R-2, respectively. RMSE values were 0.0312 for both models. The results obtained showed that both DFN and LSTM are successful and applicable to this task. | en_US |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER | en_US |
dc.relation.ispartof | JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Carrots physical properties | en_US |
dc.subject | Deep neural network | en_US |
dc.subject | Deep feedforward networks | en_US |
dc.subject | Stochastic gradient descent | en_US |
dc.subject | Recurrent neural networks | en_US |
dc.subject | Long short-term memory | en_US |
dc.title | Developing a deep neural network model for predicting carrots volume | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s11694-021-00923-9 | - |
dc.identifier.scopus | 2-s2.0-85105191193 | en_US |
dc.department | Meslek Yüksekokulları, Teknik Bilimler Meslek Yüksekokulu, Makine ve Metal Teknolojileri Bölümü | en_US |
dc.authorid | ORNEK, Mustafa Nevzat/0000-0002-7478-3728 | - |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 3471 | en_US |
dc.identifier.endpage | 3479 | en_US |
dc.identifier.wos | WOS:000642837400006 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 56031369800 | - |
dc.authorscopusid | 23097443100 | - |
dc.identifier.scopusquality | Q2 | - |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 07. 16. Department of Machinery and Metal Technologies | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections Teknik Bilimler Meslek Yüksekokulu Koleskiyonu WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Örnek-KahramanlıÖrnek2021_Article_DevelopingADeepNeuralNetworkMo.pdf Until 2030-01-01 | 1.33 MB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
5
checked on Jul 27, 2024
WEB OF SCIENCETM
Citations
12
checked on Jul 27, 2024
Page view(s)
120
checked on Jul 22, 2024
Download(s)
8
checked on Jul 22, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.