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Identification Of Wood Defect Using Pattern Recognition Technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the num...

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Main Authors: Teo, Hong Chun, Ahmad, Sabrina, Hashim, Ummi Rabaah, Ngo, Hea Choon, Kanchymalay, Kasturi, Ismail, Nor Haslinda, Salahuddin, Lizawati
Format: Article
Language:English
Published: Universitas Ahmad Dahlan 2021
Online Access:http://eprints.utem.edu.my/id/eprint/25822/2/IDENTIFICATION%20OF%20WOOD%20DEFECT.PDF
http://eprints.utem.edu.my/id/eprint/25822/
https://ijain.org/index.php/IJAIN/article/view/588
https://doi.org/10.26555/ijain.v7i2.588
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spelling my.utem.eprints.258222022-05-05T12:51:02Z http://eprints.utem.edu.my/id/eprint/25822/ Identification Of Wood Defect Using Pattern Recognition Technique Teo, Hong Chun Ahmad, Sabrina Hashim, Ummi Rabaah Ngo, Hea Choon Kanchymalay, Kasturi Ismail, Nor Haslinda Salahuddin, Lizawati This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the Fmeasure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance Universitas Ahmad Dahlan 2021-07 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25822/2/IDENTIFICATION%20OF%20WOOD%20DEFECT.PDF Teo, Hong Chun and Ahmad, Sabrina and Hashim, Ummi Rabaah and Ngo, Hea Choon and Kanchymalay, Kasturi and Ismail, Nor Haslinda and Salahuddin, Lizawati (2021) Identification Of Wood Defect Using Pattern Recognition Technique. International Journal Of Advances In Intelligent Informatics, 7 (2). pp. 163-176. ISSN 2442-6571 https://ijain.org/index.php/IJAIN/article/view/588 https://doi.org/10.26555/ijain.v7i2.588
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the Fmeasure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance
format Article
author Teo, Hong Chun
Ahmad, Sabrina
Hashim, Ummi Rabaah
Ngo, Hea Choon
Kanchymalay, Kasturi
Ismail, Nor Haslinda
Salahuddin, Lizawati
spellingShingle Teo, Hong Chun
Ahmad, Sabrina
Hashim, Ummi Rabaah
Ngo, Hea Choon
Kanchymalay, Kasturi
Ismail, Nor Haslinda
Salahuddin, Lizawati
Identification Of Wood Defect Using Pattern Recognition Technique
author_facet Teo, Hong Chun
Ahmad, Sabrina
Hashim, Ummi Rabaah
Ngo, Hea Choon
Kanchymalay, Kasturi
Ismail, Nor Haslinda
Salahuddin, Lizawati
author_sort Teo, Hong Chun
title Identification Of Wood Defect Using Pattern Recognition Technique
title_short Identification Of Wood Defect Using Pattern Recognition Technique
title_full Identification Of Wood Defect Using Pattern Recognition Technique
title_fullStr Identification Of Wood Defect Using Pattern Recognition Technique
title_full_unstemmed Identification Of Wood Defect Using Pattern Recognition Technique
title_sort identification of wood defect using pattern recognition technique
publisher Universitas Ahmad Dahlan
publishDate 2021
url http://eprints.utem.edu.my/id/eprint/25822/2/IDENTIFICATION%20OF%20WOOD%20DEFECT.PDF
http://eprints.utem.edu.my/id/eprint/25822/
https://ijain.org/index.php/IJAIN/article/view/588
https://doi.org/10.26555/ijain.v7i2.588
_version_ 1732948767497256960
score 13.211869

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