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Performance of Hyperparameters in Prediction with Deep Neural Networks of Electrical Properties of Nano-Coated Glasses Nano-Kaplamali Camlarin Elektriksel zelliklerinin Derin Sinir Aglari Ile Tahmininde Hiper Parametrelerin Performansi

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dc.contributor.authorKORKMAZ, HAYRİYE
dc.contributor.authorsYenginer H., Eraslan S., Gundogdu B., KORKMAZ H.
dc.date.accessioned2023-12-18T07:42:18Z
dc.date.available2023-12-18T07:42:18Z
dc.date.issued2023-01-01
dc.description.abstractNano-coated glasses are widely used in different fields such as industry, transportation and architectural structures. After these glasses are obtained by various thin film coating applications, they are subjected to the heat treatment process (tempering, bending, etc.), which is considered as a secondary process. Both of these two-stage processes lead to changes in the electrical and mechanical properties of the final product. While electrical properties such as transmittance, coated surface reflection and uncoated surface reflection values obtained as a result of the first stage can be calculated by analytical methods; the chaotic nature of the second stage does not allow these parameters to be calculated with similar methods. Therefore, in this study, a multi-input-multi-output deep neural network structure was designed for the estimation of the electrical properties of the nano-coated glass type produced for commercial use in architectural fields. Moreover, The dataset with 64 different coating types was augmented by adding noise technique and the performances of the hyperparameters in prediction success were compared. The performance of the network structure was measured by the mean absolute error, mean squared error, and coefficient of determination metrics. The designed network structure was tested on 16 samples and according to the results obtained, it was observed that the best performance was achieved with the Adadelta learning algorithm and ReLU activation function on the augmented data set.
dc.identifier.citationYenginer H., Eraslan S., Gundogdu B., KORKMAZ H., \"Performance of Hyperparameters in Prediction with Deep Neural Networks of Electrical Properties of Nano-Coated Glasses Nano-Kaplamali Camlarin Elektriksel zelliklerinin Derin Sinir Aglari Ile Tahmininde Hiper Parametrelerin Performansi\", 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, Türkiye, 11 - 13 Ekim 2023
dc.identifier.doi10.1109/asyu58738.2023.10296595
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85178317960&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/295708
dc.language.isoeng
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectTıp
dc.subjectSosyal ve Beşeri Bilimler
dc.subjectSosyoloji
dc.subjectKütüphanecilik
dc.subjectTarımsal Bilimler
dc.subjectZiraat
dc.subjectTarım Makineleri
dc.subjectTarımda Enerji
dc.subjectBiyoyakıt Teknolojisi
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectSağlık Bilimleri
dc.subjectTemel Tıp Bilimleri
dc.subjectBiyoistatistik ve Tıp Bilişimi
dc.subjectMühendislik ve Teknoloji
dc.subjectMedicine
dc.subjectSocial Sciences and Humanities
dc.subjectSociology
dc.subjectLibrary Sciences
dc.subjectAgricultural Sciences
dc.subjectAgriculture
dc.subjectFarm Machinery
dc.subjectEnergy in Agriculture
dc.subjectBiofuels Technology
dc.subjectComputer Sciences
dc.subjectalgorithms
dc.subjectHealth Sciences
dc.subjectFundamental Medical Sciences
dc.subjectBiostatistics and Medical Informatics
dc.subjectEngineering and Technology
dc.subjectKlinik Tıp (MED)
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectSosyal Bilimler (SOC)
dc.subjectKlinik Tıp
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik
dc.subjectSosyal Bilimler Genel
dc.subjectTIBBİ BİLİŞİM
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectENERJİ VE YAKITLAR
dc.subjectBİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ
dc.subjectClinical Medicine (MED)
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectSocial Sciences (SOC)
dc.subjectCLINICAL MEDICINE
dc.subjectCOMPUTER SCIENCE
dc.subjectENGINEERING
dc.subjectSOCIAL SCIENCES, GENERAL
dc.subjectMEDICAL INFORMATICS
dc.subjectCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
dc.subjectENERGY & FUELS
dc.subjectINFORMATION SCIENCE & LIBRARY SCIENCE
dc.subjectYapay Zeka
dc.subjectFizik Bilimleri
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectBilgisayarla Görme ve Örüntü Tanıma
dc.subjectBilgi Sistemleri ve Yönetimi
dc.subjectSosyal Bilimler ve Beşeri Bilimler
dc.subjectEnerji Mühendisliği ve Güç Teknolojisi
dc.subjectTıbbi Bilişim
dc.subjectArtificial Intelligence
dc.subjectPhysical Sciences
dc.subjectComputer Science Applications
dc.subjectComputer Vision and Pattern Recognition
dc.subjectInformation Systems and Management
dc.subjectSocial Sciences & Humanities
dc.subjectEnergy Engineering and Power Technology
dc.subjectHealth Informatics
dc.subjectdeep neural networks
dc.subjectmachine learning
dc.subjectthin film coating
dc.titlePerformance of Hyperparameters in Prediction with Deep Neural Networks of Electrical Properties of Nano-Coated Glasses Nano-Kaplamali Camlarin Elektriksel zelliklerinin Derin Sinir Aglari Ile Tahmininde Hiper Parametrelerin Performansi
dc.typeconferenceObject
dspace.entity.typePublication
local.avesis.ide04d4ce7-78a4-48a8-a684-1eb632d4a3a2
local.indexed.atSCOPUS
relation.isAuthorOfPublication67640187-0464-4e01-af59-5797b1c49144
relation.isAuthorOfPublication.latestForDiscovery67640187-0464-4e01-af59-5797b1c49144

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