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The accuracy of the prediction models for surface roughness and micro hardness of denture teeth

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The accuracy of the prediction models for surface roughness and micro hardness of denture teeth.pdf (2.520Mb)
Date
2019-12-01
Author
Ozkan, Gulay
Ozkan, Pelin
DENİZ, ŞULE TUĞBA
Advisor
Type
Article
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Abstract
The paper aimed to compare the performance of artificial neural network (ANN) model with the results of in vitro experiments. For these experiments, maxillary molars of four different denture teeth were subjected to tea, coffee, cola, cherry juice, distilled water. Vickers microhardness and surface roughness values were measured. Subsequently, ANN model for the prediction of microhardness and surface roughness of different denture teeth were examined. A back-propagation ANN has been used to develop a model relating to the amount of microhardness and surface roughness. The independent variables of the model are distilled water, tea, filtered coffee, cola, cherry juice, time and denture teeth. Microhardness and surface roughness were chosen as the dependent variables. According to the results, a neural network architecture having one input layer with ten neurons, two hidden layers with six neurons, one output layer with two neurons and an epoch size of 48 gives better prediction. Prediction models for dental materials could also be supportive for in vitro studies.
Subject
Denture teeth
Microhardness
Neural networks
Prediction
Surface roughness
URI
http://hdl.handle.net/20.500.12645/19714
https://doi.org/10.4012/dmj.2018-014
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  • PubMed İndeksli Yayın Koleksiyonu [3669]
  • Scopus İndeksli Yayınlar Koleksiyonu [4451]
  • WoS İndeksli Yayınlar Koleksiyonu [5147]
  • Yayınlar - Eserler [9983]

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BEZMIALEM VAKIF UNIVERSITY

About us |Policies | Library | Contact us | Send Feedback | Sitemap | Admin

Bezmialem Vakıf Üniversitesi, Adnan Menderes Bulvarı Vatan Caddesi 34093 Fatih, İstanbul / TURKEY
Copyright © Bezmialem Vakıf Üniversitesi

Creative Commons Lisansı
Bezmialem Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

OpenAccess@BVU

Support by  UNIREPOS