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Math. Comput. Appl. 2011, 16(1), 22-30; doi:10.3390/mca16010022

A Mineral Classification System with Multiple Artificial Neural Network Using K-Fold Cross Validation

1
Selçuk University, Department of Computer Engineering, Konya, Turkey
2
Selçuk University, Department of Electric-Electronics Engineering, Konya, Turkey
*
Authors to whom correspondence should be addressed.
Published: 1 April 2011
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Abstract

The aim of this study is to show the artificial neural network (ANN) on classification of mineral based on color values of pixels. Twenty two images were taken from the thin sections using a digital camera mounted on the microscope and transmitted to a computer. Images, under both plane-polarized and cross-polarized light, contain maximum intensity. To select training and test data, 5-fold-cross validation method was involved and multi layer perceptron neural network (MLPNN) with one hidden layer was employed for classification. The classification of mineral using ANN proved %93.86 accuracy for 400 data. In second study, for each of the 5 different mineral considered, 5 different network models were implemented. Size of data set was same with previous data. Each network model was differed from each other. Also 5-fold-cross validation method was involved to select data and MLPNN with one hidden layer was used. The classification accuracy of mineral using different ANN is %90.67; %96.16; %93.91; %92; %97.62 for quartz, muscovite, biotite, chlorite and opaque respectively.
Keywords: Thin section; Mineral; Microscope; Artificial Neural Network; Cross Validation Thin section; Mineral; Microscope; Artificial Neural Network; Cross Validation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Baykan, N.A.; Yılmaz, N. A Mineral Classification System with Multiple Artificial Neural Network Using K-Fold Cross Validation. Math. Comput. Appl. 2011, 16, 22-30.

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