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

Jirawit Yanchinda; Thepparit Sinthamrongruk; Keshav Dahal

doi:10.1109/SKIMA.2017.8294113

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 proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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 language	English
Title of host publication	Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)
Publisher	IEEE
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781538646021
ISBN (Print)	9781538646038
DOIs	https://doi.org/10.1109/SKIMA.2017.8294113
Publication status	Published - 8 Dec 2017
Event	11th International Conference on Software, Knowledge, Information Management and Applications - Sri Lanka Institute of Information Technology, Colombo, Sri Lanka Duration: 6 Dec 2017 → 8 Dec 2017 http://skimanetwork.info/skima2017/ (Conference website)

Publication series

Name
Publisher	IEEE
ISSN (Electronic)	2573-3214

Conference

Conference	11th International Conference on Software, Knowledge, Information Management and Applications
Abbreviated title	SKIMA 2017
Country/Territory	Sri Lanka
City	Colombo
Period	6/12/17 → 8/12/17
Internet address	http://skimanetwork.info/skima2017/ (Conference website)

Keywords

knowledge acquisition
fuzzy learning rule
ontology
STEM
knowledge representation

Access to Document

10.1109/SKIMA.2017.8294113

Cite this

@inproceedings{601c66c582af4b28b78e6acb4d6d230b,

title = "STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition",

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.",

keywords = "knowledge acquisition, fuzzy learning rule, ontology, STEM, knowledge representation",

author = "Jirawit Yanchinda and Thepparit Sinthamrongruk and Keshav Dahal",

year = "2017",

month = dec,

day = "8",

doi = "10.1109/SKIMA.2017.8294113",

language = "English",

isbn = "9781538646038",

publisher = "IEEE",

pages = "1--8",

booktitle = "Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)",

address = "United States",

note = "11th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2017 ; Conference date: 06-12-2017 Through 08-12-2017",

url = "http://skimanetwork.info/skima2017/",

}

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). IEEE, pp. 1-8, 11th International Conference on Software, Knowledge, Information Management and Applications, Colombo, Sri Lanka, 6/12/17. https://doi.org/10.1109/SKIMA.2017.8294113

STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition. / Yanchinda, Jirawit; Sinthamrongruk, Thepparit; Dahal, Keshav.
Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2017. p. 1-8.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - STEM learning concept with fuzzy inference for organic rice farming knowledge acquisition

AU - Yanchinda, Jirawit

AU - Sinthamrongruk, Thepparit

AU - Dahal, Keshav

PY - 2017/12/8

Y1 - 2017/12/8

N2 - 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.

AB - 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.

KW - knowledge acquisition

KW - fuzzy learning rule

KW - ontology

KW - STEM

KW - knowledge representation

U2 - 10.1109/SKIMA.2017.8294113

DO - 10.1109/SKIMA.2017.8294113

M3 - Conference contribution

SN - 9781538646038

SP - 1

EP - 8

BT - Proceedings of the 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)

PB - IEEE

T2 - 11th International Conference on Software, Knowledge, Information Management and Applications

Y2 - 6 December 2017 through 8 December 2017

ER -

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

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this