Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology

A pre-condition for identifying infectious disease and understanding the ecology of a species is by gender classification of the trabecular bone of an animal. Therefore, accurate gender classification on skeletal remains of nonhuman is essential for the research of nonhuman population. The tradition...

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Main Author:	Sahadun, Nur Afiqah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/53697/25/NurAfiqahSahadunMFC2014.pdf
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spelling	my-utm-ep.536972020-09-02T01:09:07Z Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology 2014-09 Sahadun, Nur Afiqah QA75 Electronic computers. Computer science A pre-condition for identifying infectious disease and understanding the ecology of a species is by gender classification of the trabecular bone of an animal. Therefore, accurate gender classification on skeletal remains of nonhuman is essential for the research of nonhuman population. The traditional method of classifying gender by comparative skeletal anatomy by atlas has raised issues with regard to accurate classification and challenge in management of data to identify optimum features and interpretation optimum features in a simple way. In this research all these three issues were addressed by using a process model developed specifically for gender classification. This research used two computational intelligence models, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results of simulations of both models were compared and ANN performed better than SVM. To improve the accuracy of ANN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected ANN classification model. The model is called PSO-ANN and has been developed by MATLAB and WEKA tools platform. Samples were taken from Ryan and Shaw collection. This sample contains proximal femur and proximal humerus. Comparisons of the performance measurement namely the percentage of the classification accuracy, sensitivity and specificity of the model were performed. The results showed that the ability of PSO-ANN in classifying gender outperforming the SVM and ANN model by acquiring 100% accuracy, sensitivity and specificity. Apart from that, the optimum features of the gender classification are extracted and translated into more understandable explanations using Decision Tree and compare the differences and similarities with the original features. These findings have shown that the proposed PSO-ANN is capable of successfully solving three issues in the existing method in gender classification. 2014-09 Thesis http://eprints.utm.my/id/eprint/53697/ http://eprints.utm.my/id/eprint/53697/25/NurAfiqahSahadunMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85235 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Sahadun, Nur Afiqah Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
description	A pre-condition for identifying infectious disease and understanding the ecology of a species is by gender classification of the trabecular bone of an animal. Therefore, accurate gender classification on skeletal remains of nonhuman is essential for the research of nonhuman population. The traditional method of classifying gender by comparative skeletal anatomy by atlas has raised issues with regard to accurate classification and challenge in management of data to identify optimum features and interpretation optimum features in a simple way. In this research all these three issues were addressed by using a process model developed specifically for gender classification. This research used two computational intelligence models, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results of simulations of both models were compared and ANN performed better than SVM. To improve the accuracy of ANN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected ANN classification model. The model is called PSO-ANN and has been developed by MATLAB and WEKA tools platform. Samples were taken from Ryan and Shaw collection. This sample contains proximal femur and proximal humerus. Comparisons of the performance measurement namely the percentage of the classification accuracy, sensitivity and specificity of the model were performed. The results showed that the ability of PSO-ANN in classifying gender outperforming the SVM and ANN model by acquiring 100% accuracy, sensitivity and specificity. Apart from that, the optimum features of the gender classification are extracted and translated into more understandable explanations using Decision Tree and compare the differences and similarities with the original features. These findings have shown that the proposed PSO-ANN is capable of successfully solving three issues in the existing method in gender classification.
format	Thesis
qualification_level	Master's degree
author	Sahadun, Nur Afiqah
author_facet	Sahadun, Nur Afiqah
author_sort	Sahadun, Nur Afiqah
title	Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
title_short	Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
title_full	Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
title_fullStr	Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
title_full_unstemmed	Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
title_sort	hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/53697/25/NurAfiqahSahadunMFC2014.pdf
_version_	1747817615160508416

Hybrid particle swarm optimization-artificial neural network gender classifier for trabecular bone morphology

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