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http://dspace.uniten.edu.my/jspui/handle/123456789/5016
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nagi, J. | en_US |
dc.contributor.author | Yap, K.S. | en_US |
dc.contributor.author | Tiong, S.K. | en_US |
dc.contributor.author | Ahmed, S.K. | en_US |
dc.contributor.author | Nagi, F. | en_US |
dc.date.accessioned | 2017-11-14T03:21:20Z | - |
dc.date.available | 2017-11-14T03:21:20Z | - |
dc.date.issued | 2011 | - |
dc.description.abstract | This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy if-then rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective. © 2011 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Transactions on Power Delivery Volume 26, Issue 2, April 2011, Article number 5738432, Pages 1284-1285 | en_US |
dc.title | Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TPWRD.2010.2055670 | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | COE Scholarly Publication |
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