Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/62665
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dc.contributor.authorJirawit Yanchindaen_US
dc.contributor.authorThepparit Sinthamrongruken_US
dc.contributor.authorKeshav Dahalen_US
dc.date.accessioned2018-11-29T07:38:44Z-
dc.date.available2018-11-29T07:38:44Z-
dc.date.issued2018-02-16en_US
dc.identifier.issn25733214en_US
dc.identifier.issn2373082Xen_US
dc.identifier.other2-s2.0-85054203588en_US
dc.identifier.other10.1109/SKIMA.2017.8294113en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85054203588&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/62665-
dc.description.abstract© 2017 IEEE. This study aims at providing a hybrid knowledge acquisition approach with fuzzy machine learning technique which assists to obtain knowledge when constructing expert systems. The study defines the knowledge acquisition framework that has been developed in order to make it easier and to provide appropriate interpretations for organic rice farmers and build inferential knowledge based on STEM learning concept in a fuzzy rule representation framework. The main concentration of this study is to demonstrate the knowledge acquisition technique as a method for extending, modernizing and improving a defective knowledge based-system in which fuzzy machine learning is beneficial.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleSTEM learning concept with fuzzy inference for organic rice farming knowledge acquisitionen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleInternational Conference on Software, Knowledge Information, Industrial Management and Applications, SKIMAen_US
article.volume2017-Decemberen_US
article.stream.affiliationsUniversity of the West of Scotlanden_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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