Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification
This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backp...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28990/ http://umpir.ump.edu.my/id/eprint/28990/1/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network%20.pdf http://umpir.ump.edu.my/id/eprint/28990/2/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network_FULL.pdf |
| _version_ | 1848823184716988416 |
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| author | Nurul Shakila, Ahmad Zubir Mohamad Aqib Haqmi, Abas Ismail, N. A. Nor Azah, Mohd Ali Mohd Hezri, Fazalul Rahiman Ng, K. M. Saiful Nizam, Tajuddin |
| author_facet | Nurul Shakila, Ahmad Zubir Mohamad Aqib Haqmi, Abas Ismail, N. A. Nor Azah, Mohd Ali Mohd Hezri, Fazalul Rahiman Ng, K. M. Saiful Nizam, Tajuddin |
| author_sort | Nurul Shakila, Ahmad Zubir |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%). |
| first_indexed | 2025-11-15T02:53:06Z |
| format | Conference or Workshop Item |
| id | ump-28990 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:53:06Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-289902022-03-21T07:12:15Z http://umpir.ump.edu.my/id/eprint/28990/ Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification Nurul Shakila, Ahmad Zubir Mohamad Aqib Haqmi, Abas Ismail, N. A. Nor Azah, Mohd Ali Mohd Hezri, Fazalul Rahiman Ng, K. M. Saiful Nizam, Tajuddin QA Mathematics QD Chemistry TP Chemical technology This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%). IEEE 2017-10-17 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28990/1/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/28990/2/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network_FULL.pdf Nurul Shakila, Ahmad Zubir and Mohamad Aqib Haqmi, Abas and Ismail, N. A. and Nor Azah, Mohd Ali and Mohd Hezri, Fazalul Rahiman and Ng, K. M. and Saiful Nizam, Tajuddin (2017) Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification. In: 2017 IEEE 8th Control and System Graduate Research Colloquium, ICSGRC 2017 , 4 - 5 August 2017 , Grand Blue Wave Hotel, Shah Alam. pp. 122-126. (8070580). ISBN 9781538603802 (Published) https://doi.org/10.1109/ICSGRC.2017.8070580 |
| spellingShingle | QA Mathematics QD Chemistry TP Chemical technology Nurul Shakila, Ahmad Zubir Mohamad Aqib Haqmi, Abas Ismail, N. A. Nor Azah, Mohd Ali Mohd Hezri, Fazalul Rahiman Ng, K. M. Saiful Nizam, Tajuddin Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title | Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title_full | Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title_fullStr | Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title_full_unstemmed | Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title_short | Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| title_sort | analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification |
| topic | QA Mathematics QD Chemistry TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/28990/ http://umpir.ump.edu.my/id/eprint/28990/ http://umpir.ump.edu.my/id/eprint/28990/1/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network%20.pdf http://umpir.ump.edu.my/id/eprint/28990/2/Analysis%20of%20algorithms%20variation%20in%20multilayer%20perceptron%20neural%20network_FULL.pdf |