Utilization of artificial immune system in prediction of paddy production
This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as i...
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Asian Research Publishing Network (ARPN)
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iium-436042017-11-30T11:57:02Z http://irep.iium.edu.my/43604/ Utilization of artificial immune system in prediction of paddy production Mohd Khidzir, Ainul Bahiah Abdul Malek, Marlinda Ismail , Amelia Ritahani Juneng, Liew Ting , Sie Chun, QA75 Electronic computers. Computer science This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production. Asian Research Publishing Network (ARPN) 2015-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/43604/1/UTILIZATION_OF_ARTIFICIAL_IMMUNE_SYSTEM_IN_PREDICTION_OF.pdf Mohd Khidzir, Ainul Bahiah and Abdul Malek, Marlinda and Ismail , Amelia Ritahani and Juneng, Liew and Ting , Sie Chun, (2015) Utilization of artificial immune system in prediction of paddy production. ARPN Journal of Engineering and Applied Sciences, 10 (3). pp. 1462-1467. ISSN 1819-6608 http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0215_1616.pdf |
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QA75 Electronic computers. Computer science |
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QA75 Electronic computers. Computer science Mohd Khidzir, Ainul Bahiah Abdul Malek, Marlinda Ismail , Amelia Ritahani Juneng, Liew Ting , Sie Chun, Utilization of artificial immune system in prediction of paddy production |
description |
This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms
(CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on
climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy
ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production. |
format |
Article |
author |
Mohd Khidzir, Ainul Bahiah Abdul Malek, Marlinda Ismail , Amelia Ritahani Juneng, Liew Ting , Sie Chun, |
author_facet |
Mohd Khidzir, Ainul Bahiah Abdul Malek, Marlinda Ismail , Amelia Ritahani Juneng, Liew Ting , Sie Chun, |
author_sort |
Mohd Khidzir, Ainul Bahiah |
title |
Utilization of artificial immune system in prediction of paddy production |
title_short |
Utilization of artificial immune system in prediction of paddy production |
title_full |
Utilization of artificial immune system in prediction of paddy production |
title_fullStr |
Utilization of artificial immune system in prediction of paddy production |
title_full_unstemmed |
Utilization of artificial immune system in prediction of paddy production |
title_sort |
utilization of artificial immune system in prediction of paddy production |
publisher |
Asian Research Publishing Network (ARPN) |
publishDate |
2015 |
url |
http://irep.iium.edu.my/43604/ http://irep.iium.edu.my/43604/ http://irep.iium.edu.my/43604/1/UTILIZATION_OF_ARTIFICIAL_IMMUNE_SYSTEM_IN_PREDICTION_OF.pdf |
first_indexed |
2023-09-18T21:02:07Z |
last_indexed |
2023-09-18T21:02:07Z |
_version_ |
1777410693390139392 |