GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK

In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classific...

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Autores principales:	Nurul Liyana Hairuddin, Lizawati Mi Yusuf, Mohd Shahizan Othman
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	back propagation neural network metaheuristic algorithms optimization gender classification Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/c8e7e61fa27b497b9e13c80efaa95d78
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spelling	oai:doaj.org-article:c8e7e61fa27b497b9e13c80efaa95d782021-11-11T03:34:39ZGENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK10.32890/jict2020.19.2.51675-414X2180-3862https://doaj.org/article/c8e7e61fa27b497b9e13c80efaa95d782020-03-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.2.5https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists. Nurul Liyana HairuddinLizawati Mi Yusuf Mohd Shahizan OthmanUUM Pressarticleback propagation neural networkmetaheuristic algorithmsoptimizationgender classificationInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 2, Pp 251-277 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	back propagation neural network metaheuristic algorithms optimization gender classification Information technology T58.5-58.64
spellingShingle	back propagation neural network metaheuristic algorithms optimization gender classification Information technology T58.5-58.64 Nurul Liyana Hairuddin Lizawati Mi Yusuf Mohd Shahizan Othman GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
description	In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists.
format	article
author	Nurul Liyana Hairuddin Lizawati Mi Yusuf Mohd Shahizan Othman
author_facet	Nurul Liyana Hairuddin Lizawati Mi Yusuf Mohd Shahizan Othman
author_sort	Nurul Liyana Hairuddin
title	GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
title_short	GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
title_full	GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
title_fullStr	GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
title_full_unstemmed	GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK
title_sort	gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/c8e7e61fa27b497b9e13c80efaa95d78
work_keys_str_mv	AT nurulliyanahairuddin genderclassificationonskeletalremainsefficiencyofmetaheuristicalgorithmmethodandoptimizedbackpropagationneuralnetwork AT lizawatimiyusuf genderclassificationonskeletalremainsefficiencyofmetaheuristicalgorithmmethodandoptimizedbackpropagationneuralnetwork AT mohdshahizanothman genderclassificationonskeletalremainsefficiencyofmetaheuristicalgorithmmethodandoptimizedbackpropagationneuralnetwork
_version_	1718439627897962496

GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK

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