dc.contributor.author |
Mendis, D.S.K. |
|
dc.contributor.author |
Karunananda, A.S. |
|
dc.contributor.author |
Samaratunga, U. |
|
dc.contributor.author |
Rathnayake, U. |
|
dc.date.accessioned |
2016-05-12T05:54:21Z |
|
dc.date.available |
2016-05-12T05:54:21Z |
|
dc.date.issued |
2013 |
|
dc.identifier.citation |
Mendis, D.S.K., Karunananda, A.S., Samaratunga, U. and Rathnayake, U. 2013. A fuzzy expert system for business intelligence. Proceedings of the 10th International Conference on Business Management. p. 24-31. |
en_US |
dc.identifier.uri |
|
|
dc.identifier.uri |
http://repository.kln.ac.lk/handle/123456789/13072 |
|
dc.description.abstract |
Business Intelligence (BI) is recognized as an increasingly important support for business decision making in emerging business environment, where a huge amount of data is growing fast and scattered around. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as commonsense knowledge can be represented in informal way and further modeling needed for BI. Acquiring useful Business Intelligence (BI) for decision-making is a challenging task in dynamic business environment. In this paper we present an approach for modeling commonsense knowledge in Business Intelligence. A fuzzy expert system based on principal component analysis (PCA) and statistical fuzzy inference system for modeling Business Intelligence in commonsense knowledge is introduced in, which enables holistic approach for disaster management. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
Business Intelligence |
en_US |
dc.subject |
Statistical inference system |
en_US |
dc.subject |
Common sense knowledge |
en_US |
dc.subject |
Principal component analysis |
en_US |
dc.subject |
Ayurvedic medicine |
en_US |
dc.title |
A fuzzy expert system for business intelligence |
en_US |
dc.type |
Article |
en_US |