Sexual Dimorphism Prediction of Darevskia bithynica (Méhely 1909) from Northwestern Anatolia, Turkey by Using Artificial Neural Network

Abstract

The aim of the study is to predict the gender of Darevskia bithynica by using a feed-forward back-propagation artificial neural network (ANN). Nine morphological characters were used as an input parameters of the model. The gender type male or female is the output parameter. The total number of data is 115. In order to train, validate and test the ANN model 70%, 15% and 15% of the total data are randomly selected, respectively. The regression coefficient (R) values are evaluated as prediction performance. The network’s layer with tangent sigmoid activation functions predicts the lizard gender with R values as 0.98, 0.97 and 0.96 for training, testing and all data, respectively. The mean square error (MSE) values for training and testing data are calculated as 0.0145 and 0.0161, respectively. The obtained results satisfactorily confirm the high ability of the ANNs in predicting the gender of Darevskia bithynica.

Keywords

• Artificial neural network,sexual dimorphism,Darevskia bithynica,Northwestern Anatolia

References

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