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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Main Authors: Hairuddin, Nurul Liyana, Yusuf, Lizawati Mi, Othman, Mohd Shahizan
Format: Article
Language:English
Published: Universiti Utara Malaysia 2020
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Online Access:http://repo.uum.edu.my/26943/1/JICT%2019%202%202020%20251-277.pdf
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spelling my.uum.repo.269432020-04-23T06:40:13Z http://repo.uum.edu.my/26943/ Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network Hairuddin, Nurul Liyana Yusuf, Lizawati Mi Othman, Mohd Shahizan QA76 Computer software 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. Universiti Utara Malaysia 2020-04 Article PeerReviewed application/pdf en http://repo.uum.edu.my/26943/1/JICT%2019%202%202020%20251-277.pdf Hairuddin, Nurul Liyana and Yusuf, Lizawati Mi and Othman, Mohd Shahizan (2020) Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network. Journal of Information and Communication Technology (JICT), 19 (2). pp. 251-277. ISSN 1675-414X http://jict.uum.edu.my/index.php/currentissues#a5
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Hairuddin, Nurul Liyana
Yusuf, Lizawati Mi
Othman, Mohd Shahizan
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 Hairuddin, Nurul Liyana
Yusuf, Lizawati Mi
Othman, Mohd Shahizan
author_facet Hairuddin, Nurul Liyana
Yusuf, Lizawati Mi
Othman, Mohd Shahizan
author_sort Hairuddin, Nurul Liyana
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 Universiti Utara Malaysia
publishDate 2020
url http://repo.uum.edu.my/26943/1/JICT%2019%202%202020%20251-277.pdf
http://repo.uum.edu.my/26943/
http://jict.uum.edu.my/index.php/currentissues#a5
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score 13.154949