Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network

Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network

Age estimation is used in the field of forensic anthropology’s studies to assists in the identification of individual’s identity. The age estimation using traditional method was unique and applicable for a certain population only. The focus on this study is the measurement of left hand bone to estim...

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Main Author: Nor Anis Nabila, Saari
Format: Undergraduates Project Papers
Language:English
Published: 2019
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/27084/
http://umpir.ump.edu.my/id/eprint/27084/
http://umpir.ump.edu.my/id/eprint/27084/1/Age%20estimation%20based%20on%20length%20of%20left%20hand%20bone%20in%20African%20American%20children%20below%2018%20years%20old.pdf
id ump-27084
recordtype eprints
spelling ump-270842019-12-23T03:47:18Z http://umpir.ump.edu.my/id/eprint/27084/ Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network Nor Anis Nabila, Saari QA75 Electronic computers. Computer science QA76 Computer software Age estimation is used in the field of forensic anthropology’s studies to assists in the identification of individual’s identity. The age estimation using traditional method was unique and applicable for a certain population only. The focus on this study is the measurement of left hand bone to estimate age using mathematical method of Multiple Linear Regression and also the soft computing models of Artificial Neural Network (ANN) that can contribute to another alternative models instead of using the traditional model of Greulich and Pyle (GP) model and Tanner and Whitehouse (TW) model that is based on the expert of anthropology’ s experience which may lead to various of result of age estimation. The regression models were carried out on X-ray images of the left hand in African American dataset from new-born to 18 years old. All the nineteen bones of the left hand were measured manually using Photo Pos, Power of Software Company Ltd which is the free photo editor that will creating a line on the each of left-hand bones. For Artificial Neural Network to produce a better result in prediction of age, hidden neuron network in ANN is manipulated as suggested by Zain et al. using Encog Workbench tools version 3.3.0. The value of R-square and mean square error (MSE) of proposed model been calculated as performance measurement for compare with benchmark of age. Based on result produced by these models, mean square error produced by ANN model are 1.775 and 2.487 for both male and female, respectively. To conclude, ANN is reliable to estimate the age based on length of the left hand. 2019-01 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27084/1/Age%20estimation%20based%20on%20length%20of%20left%20hand%20bone%20in%20African%20American%20children%20below%2018%20years%20old.pdf Nor Anis Nabila, Saari (2019) Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Nor Anis Nabila, Saari
Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
description Age estimation is used in the field of forensic anthropology’s studies to assists in the identification of individual’s identity. The age estimation using traditional method was unique and applicable for a certain population only. The focus on this study is the measurement of left hand bone to estimate age using mathematical method of Multiple Linear Regression and also the soft computing models of Artificial Neural Network (ANN) that can contribute to another alternative models instead of using the traditional model of Greulich and Pyle (GP) model and Tanner and Whitehouse (TW) model that is based on the expert of anthropology’ s experience which may lead to various of result of age estimation. The regression models were carried out on X-ray images of the left hand in African American dataset from new-born to 18 years old. All the nineteen bones of the left hand were measured manually using Photo Pos, Power of Software Company Ltd which is the free photo editor that will creating a line on the each of left-hand bones. For Artificial Neural Network to produce a better result in prediction of age, hidden neuron network in ANN is manipulated as suggested by Zain et al. using Encog Workbench tools version 3.3.0. The value of R-square and mean square error (MSE) of proposed model been calculated as performance measurement for compare with benchmark of age. Based on result produced by these models, mean square error produced by ANN model are 1.775 and 2.487 for both male and female, respectively. To conclude, ANN is reliable to estimate the age based on length of the left hand.
format Undergraduates Project Papers
author Nor Anis Nabila, Saari
author_facet Nor Anis Nabila, Saari
author_sort Nor Anis Nabila, Saari
title Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
title_short Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
title_full Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
title_fullStr Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
title_full_unstemmed Age estimation based on length of left hand bone in African American children below 18 years old using Artificial Neural Network
title_sort age estimation based on length of left hand bone in african american children below 18 years old using artificial neural network
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/27084/
http://umpir.ump.edu.my/id/eprint/27084/
http://umpir.ump.edu.my/id/eprint/27084/1/Age%20estimation%20based%20on%20length%20of%20left%20hand%20bone%20in%20African%20American%20children%20below%2018%20years%20old.pdf
first_indexed 2023-09-18T22:42:29Z
last_indexed 2023-09-18T22:42:29Z
_version_ 1777417007694610432

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