Classification of Agarwood using ANN

An artifical neural network (ANN) has been modeled for the classification of Agarwood region. The target regions were from Melaka, Pagoh, Super Pagoh, Ulu Tembeling and Indonesia. The data analysis using Principal Component Analysis (PCA) was done to find significant input selection from 32 sensors...

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Main Authors:	M. S., Najib, N. A., Mohd Ali, M. N., Mat Arip, M., Abd Jalil, M. N., Taib
Format:	Article
Language:	English
Published:	2012
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/6889/ http://umpir.ump.edu.my/id/eprint/6889/1/Classification_of_Agarwood_using_ANN.pdf

id	ump-6889
recordtype	eprints
spelling	ump-68892018-04-19T01:18:18Z http://umpir.ump.edu.my/id/eprint/6889/ Classification of Agarwood using ANN M. S., Najib N. A., Mohd Ali M. N., Mat Arip M., Abd Jalil M. N., Taib TK Electrical engineering. Electronics Nuclear engineering An artifical neural network (ANN) has been modeled for the classification of Agarwood region. The target regions were from Melaka, Pagoh, Super Pagoh, Ulu Tembeling and Indonesia. The data analysis using Principal Component Analysis (PCA) was done to find significant input selection from 32 sensors of the E-nose and to recognize pattern variations from different number of Agarwood samples as inputs to ANN training. The network developed based on three layers feed forward network and the back propagation learning algorithm was used in executing the network training. Five input neurons, two hidden layer sizes and one output neurons were found to be the optimized combination for the network. The experimental results reveal that the proposed method is effective and significant to the classification of Agarwood region. 2012-06 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6889/1/Classification_of_Agarwood_using_ANN.pdf M. S., Najib and N. A., Mohd Ali and M. N., Mat Arip and M., Abd Jalil and M. N., Taib (2012) Classification of Agarwood using ANN. International Journal of Electrical and electronic Systems Research, 5. pp. 20-34.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering M. S., Najib N. A., Mohd Ali M. N., Mat Arip M., Abd Jalil M. N., Taib Classification of Agarwood using ANN
description	An artifical neural network (ANN) has been modeled for the classification of Agarwood region. The target regions were from Melaka, Pagoh, Super Pagoh, Ulu Tembeling and Indonesia. The data analysis using Principal Component Analysis (PCA) was done to find significant input selection from 32 sensors of the E-nose and to recognize pattern variations from different number of Agarwood samples as inputs to ANN training. The network developed based on three layers feed forward network and the back propagation learning algorithm was used in executing the network training. Five input neurons, two hidden layer sizes and one output neurons were found to be the optimized combination for the network. The experimental results reveal that the proposed method is effective and significant to the classification of Agarwood region.
format	Article
author	M. S., Najib N. A., Mohd Ali M. N., Mat Arip M., Abd Jalil M. N., Taib
author_facet	M. S., Najib N. A., Mohd Ali M. N., Mat Arip M., Abd Jalil M. N., Taib
author_sort	M. S., Najib
title	Classification of Agarwood using ANN
title_short	Classification of Agarwood using ANN
title_full	Classification of Agarwood using ANN
title_fullStr	Classification of Agarwood using ANN
title_full_unstemmed	Classification of Agarwood using ANN
title_sort	classification of agarwood using ann
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/6889/ http://umpir.ump.edu.my/id/eprint/6889/1/Classification_of_Agarwood_using_ANN.pdf
first_indexed	2023-09-18T22:03:03Z
last_indexed	2023-09-18T22:03:03Z
_version_	1777414526893817856

Classification of Agarwood using ANN

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