STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition

Jirawit Yanchinda, Thepparit Sinthamrongruk, Keshav Dahal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)9781538646021
ISBN (Print)9781538646038
DOIs
Publication statusPublished - 8 Dec 2017
Event11th International Conference on Software, Knowledge, Information Management and Applications - Sri Lanka Institute of Information Technology, Colombo, Sri Lanka
Duration: 6 Dec 20178 Dec 2017
http://skimanetwork.info/skima2017/ (Conference website)

Publication series

Name
PublisherIEEE
ISSN (Electronic)2573-3214

Conference

Conference11th International Conference on Software, Knowledge, Information Management and Applications
Abbreviated titleSKIMA 2017
CountrySri Lanka
CityColombo
Period6/12/178/12/17
Internet address

Keywords

  • knowledge acquisition
  • fuzzy learning rule
  • ontology
  • STEM
  • knowledge representation

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  • Cite this

    Yanchinda, J., Sinthamrongruk, T., & Dahal, K. (2017). STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition. In Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-8). IEEE. https://doi.org/10.1109/SKIMA.2017.8294113