A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

• Main Authors: Satyendra Nath Mandal, Pritam Ghosh, Nanigopal Shit, Dilip Kumar Hajra, Santanu Banik
• Format: Article
• Language: English
• Published: World Scientific Publishing 2021-02-01
• Series: Vietnam Journal of Computer Science
• Subjects: artificial neural network; training algorithm; image database; segmentation; statistical components; color components
• Online Access: http://www.worldscientific.com/doi/epdf/10.1142/S2196888821500068
• ISSN: 2196-8888; 2196-8896

Various training algorithms are used in artificial neural networks for updating the weights during training the network. But, the selection of the appropriate training algorithm is dependent on the input–output mapping of dataset for which the network is constructed. In this paper, a framework has been proposed consisting of five modules to select the optimal training algorithm for predicting pig breeds from their images. The individual pig images from five pig-breeds have been captured using inbuilt camera of mobile phone and the contour of pig has been segmented from each captured image by HUE-based segmentation algorithm. In Statistical Parameter and Color Component retrieval module, parameters like entropy, standard deviation, variance, mean, median, and mode and color properties like hue, saturation, value (HSV) extracted from the content of each segmented image. Values of all extracted parameters have been transferred into Training Algorithm Selection Module. In this module, a fitting neural network with different numbers of hidden neurons has been executed by feeding all extracted values from pig images for mapping their breeds. Ten training algorithms have been applied on the same extracted dataset separately for five epochs each keeping other network parameters constants. The mean square error (MSE) and correlation coefficient (R) for the validation set have been calculated after adjustment of weights and biases in each connection of the neurons. One training algorithm among 10 and its suitable number of hidden neurons has been selected based on comparative analysis for getting lower MSE and higher R in the validation set. Then, the fitting network with selected training algorithm has been run on the same extracted datasets until the stopping condition is reached. Then the test set images are fed into the network and the network output has been categorized to class which has been assigned to each breed of pig in Breed Prediction Module. The proposed framework has been able to predict breeds with 96.00% accuracy, achieved by the trial with 50 images of the test set. It may be concluded that the Neuro Statistic Neural Network model may be used for breed prediction of pigs by using images of individual pigs.