Identification Of Wood Defect Using Pattern Recognition Technique

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

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	Automated Vision Inspection, Defect Identification, Neural Network, Classification Performance, Epoch
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	05 May 2022 12:51
Last Modified:	05 May 2022 12:51
URI:	http://eprints.utem.edu.my/id/eprint/25822
Statistic Details:	View Download Statistic

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