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Utilization of artificial immune system in prediction of paddy production

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dc.citedby1
dc.contributor.authorKhidzir A.B.M.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorIsmail A.R.en_US
dc.contributor.authorJuneng L.en_US
dc.contributor.authorChun T.S.en_US
dc.contributor.authorid56532488700en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid36995749000en_US
dc.contributor.authorid23976053900en_US
dc.contributor.authorid56338030500en_US
dc.date.accessioned2023-05-29T06:02:06Z
dc.date.available2023-05-29T06:02:06Z
dc.date.issued2015
dc.description.abstractThis 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. � 2006-2015 Asian Research Publishing Network (ARPN).en_US
dc.description.natureFinalen_US
dc.identifier.epage1467
dc.identifier.issue3
dc.identifier.scopus2-s2.0-84923838698
dc.identifier.spage1462
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84923838698&partnerID=40&md5=6966568be8ed176bb52c1c1d32491ae0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22568
dc.identifier.volume10
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleARPN Journal of Engineering and Applied Sciences
dc.titleUtilization of artificial immune system in prediction of paddy productionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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