PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties

This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affec...

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Main Authors: Sahadun, Nur Afiqah, Ali, Nor Azizah, Haron, Habibollah
Format: Article
Language:English
Published: Penerbit UTM Press 2019
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Online Access:http://eprints.utm.my/id/eprint/85229/1/NorAzizahAli2019_PSO-FuzzyNNTechniquesinGenderClassification.pdf
http://eprints.utm.my/id/eprint/85229/
https://dx.doi.org/10.11113/ijic.v9n1.215
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spelling my.utm.852292020-03-17T08:10:59Z http://eprints.utm.my/id/eprint/85229/ PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties Sahadun, Nur Afiqah Ali, Nor Azizah Haron, Habibollah QA75 Electronic computers. Computer science This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric dataset and Ryan and Shaw Dataset (RSD) as a benchmark for the identification policy. To improve the accuracy of FuzzyNN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected FuzzyNN classification model. The model is called PSO-FuzzyNN and has been developed by MATLAB and WEKA tools platform. Comparisons of the performance measurement namely the percentage of the classification accuracy of the model were performed. The result show potential the proposed PSO-FuzzyNN method demonstrates the capability to the obtained highest accuracy of identification. Penerbit UTM Press 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/85229/1/NorAzizahAli2019_PSO-FuzzyNNTechniquesinGenderClassification.pdf Sahadun, Nur Afiqah and Ali, Nor Azizah and Haron, Habibollah (2019) PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties. International Journal of Innovative Computing, 9 (1). pp. 15-18. ISSN 2180-4370 https://dx.doi.org/10.11113/ijic.v9n1.215 DOI:10.11113/ijic.v9n1.215
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sahadun, Nur Afiqah
Ali, Nor Azizah
Haron, Habibollah
PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
description This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric dataset and Ryan and Shaw Dataset (RSD) as a benchmark for the identification policy. To improve the accuracy of FuzzyNN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected FuzzyNN classification model. The model is called PSO-FuzzyNN and has been developed by MATLAB and WEKA tools platform. Comparisons of the performance measurement namely the percentage of the classification accuracy of the model were performed. The result show potential the proposed PSO-FuzzyNN method demonstrates the capability to the obtained highest accuracy of identification.
format Article
author Sahadun, Nur Afiqah
Ali, Nor Azizah
Haron, Habibollah
author_facet Sahadun, Nur Afiqah
Ali, Nor Azizah
Haron, Habibollah
author_sort Sahadun, Nur Afiqah
title PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
title_short PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
title_full PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
title_fullStr PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
title_full_unstemmed PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
title_sort pso-fuzzynn techniques in gender classification based on bovine bone morphology properties
publisher Penerbit UTM Press
publishDate 2019
url http://eprints.utm.my/id/eprint/85229/1/NorAzizahAli2019_PSO-FuzzyNNTechniquesinGenderClassification.pdf
http://eprints.utm.my/id/eprint/85229/
https://dx.doi.org/10.11113/ijic.v9n1.215
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score 13.222552