Determination of the Diameter Distribution of Single-Wall Carbon Nanotubes from the Raman G-Band Using an Artificial Neural Network

2005 ◽  
Vol 5 (2) ◽  
pp. 204-208 ◽  
Author(s):  
A. Kukovecz ◽  
M. Smolik ◽  
S. Bokova ◽  
H. Kataura ◽  
Y. Achiba ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Nanotubes ◽  
Artificial Neural Network ◽  
Diameter Distribution ◽  
Single Wall Carbon Nanotubes ◽  
Single Wall Carbon ◽  
Artificial Neural

PARAMETRIC STUDY ON THE SYNTHESIS OF SINGLE WALL CARBON NANOTUBE BY GAS ARC-DISCHARGE METHOD WITH MULTIPLE LINEAR REGRESSIONS AND ARTIFICIAL NEURAL NETWORK

2009 ◽  
Vol 08 (03) ◽  
pp. 243-249
Author(s):  
RAZIEH JABARI SERESHT ◽  
MOHSEN JAHANSHAHI ◽  
MASOUD YAZDANI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Nanotube ◽  
Linear Model ◽  
Arc Discharge ◽  
Single Wall Carbon Nanotubes ◽  
Ann Model ◽  
Single Wall Carbon ◽  
Artificial Neural ◽  
Discharge Method

Carbon nanotube, a new form of carbon element. The arc-discharge method is one of the most efficient techniques for this purpose. Arc-discharge is very simple and has merit to make massive and elongated single wall carbon nanotubes (SWCNTs). Synthesis of SWCNTs are affected with some parameters. In this paper, a predictive model has performed for prediction diameter of SWCNTs based on these factors. In this study, a linear model were established by subjecting the transformed values of independent variables to multiple linear regression (MLR) analysis. In order to examine the correctness of predicted linear model, a predictive artificial neural network (ANN) model was utilized. The optimized models developed by both ANN and regression were validated by comparing the results by a paired t test. These findings demonstrate that ANN provides more accurate prediction and is quite useful in the optimization of diameter SWCNT when compared with the MLR analysis method.

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.

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