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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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2012
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| Online Access: | http://umpir.ump.edu.my/id/eprint/6889/1/Classification_of_Agarwood_using_ANN.pdf |
| _version_ | 1825821849330647040 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T11:47:44Z |
| format | Article |
| id | UMPir6889 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:47:44Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | UMPir68892018-04-19T01:18:18Z 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. (Published) |
| 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 |
| title | 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_short | Classification of Agarwood using ANN |
| title_sort | classification of agarwood using ann |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/6889/1/Classification_of_Agarwood_using_ANN.pdf |
| work_keys_str_mv | AT msnajib classificationofagarwoodusingann AT namohdali classificationofagarwoodusingann AT mnmatarip classificationofagarwoodusingann AT mabdjalil classificationofagarwoodusingann AT mntaib classificationofagarwoodusingann |