scholarly journals Use of Artificial Neural Network in Determination of Shade, Light Curing Unit, and Composite Parameters’ Effect on Bottom/Top Vickers Hardness Ratio of Composites

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hacer Deniz Arısu ◽  
Evrim Eligüzeloglu Dalkilic ◽  
Fehime Alkan ◽  
Sebnem Erol ◽  
Mine Betul Uctasli ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vickers Hardness ◽  
Light Emitting Diode ◽  
Significance Level ◽  
Significant Difference ◽  
Light Curing ◽  
Composite Parameter ◽  
Artificial Neural

Objective.To assess the influence of light emitting diode (LED) and quartz tungsten halogen (QTH) light curing unit (LCU) on the bottom/top (B/T) Vickers Hardness Number (VHN) ratio of different composites with different shades and determination of the most significant effect on B/T VHN ratio of composites by shade, light curing unit, and composite parameters using artificial neural network.Method.Three composite resin materials [Clearfil Majesty Esthetic (CME), Tetric N Ceram (TNC), and Tetric Evo Ceram (TEC)] in different shades (HO, A2, B2, Bleach L, Bleach M) were used. The composites were polymerized with three different LED LCUs (Elipar S10, Bluephase 20i, Valo) and halogen LCU (Hilux). Vickers hardness measurements were made at a load of 100 g for 10 sec on the top and bottom surfaces and B/T VHN ratio calculated. The data were statistically analyzed with three-way ANOVA and Tukey test at a significance level of 0.05. The obtained measurements and data were then fed to a neural network to establish the correlation between the inputs and outputs.Results.There were no significant differences between the B/T VHN ratio of LCUs for the HO and B shades of CME (p>0.05), but there were significant differences between the B/T VHN ratio of LCUs for shade A2 (p<0.05). No significant difference was determined between the B/T VHN ratio of LCUs for all shades of TNC (p>0.05). For TEC, there was no significant difference between the B/T VHN ratio of halogen and LED LCUs (p>0.05), but a significant difference was determined among the LED LCUs (p<0.05). The artificial neural network results showed that a combination of the curing light and composite parameter had the most significant effect on the B/T VHN ratio of composites. Shade has the lowest effect on the B/T VHN ratio of composites.Conclusion.The B/T VHN ratio values of different resin-based composite materials may vary depending on the light curing device. In addition, the artificial neural network results showed that the LCU and composite parameter had the most significant effect on the B/T VHN ratio of the composites. Shade has the lowest effect on the B/T VHN ratio of composites.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Determination of <i>Plasmodium</i> Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network

2014 ◽  
Vol 04 (02) ◽  
pp. 78-88 ◽  
Author(s):  
Lucy Gitonga ◽  
Daniel Maitethia Memeu ◽  
Kenneth Amiga Kaduki ◽  
Mjomba Allen Christopher Kale ◽  
Njogu Samson Muriuki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Life Stages ◽  
Blood Smears ◽  
Artificial Neural ◽  
Thin Blood Smears
Sign in / Sign up

Export Citation Format

Share Document