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dc.contributor.authorDarcy, Peteren_US
dc.contributor.authorStantic, Belaen_US
dc.contributor.authorSattar, Abdulen_US
dc.date.accessioned2017-04-24T08:20:18Z
dc.date.available2017-04-24T08:20:18Z
dc.date.issued2011en_US
dc.date.modified2012-04-01T23:04:50Z
dc.identifier.issn1088467Xen_US
dc.identifier.doi10.3233/IDA-2011-0503en_US
dc.identifier.urihttp://hdl.handle.net/10072/44149
dc.description.abstractRadio Frequency Identification (RFID) technology allows wireless interaction between tagged objects and readers to automatically identify large groups of items. This technology is widely accepted in a number of application domains, however, it suffers from data anomalies such as false-positive observations. Existing methods, such as manual tools, user specified rules and filtering algorithms, lack the automation and intelligence to effectively remove ambiguous false-positive readings. In this paper, we propose a methodology which incorporates a highly intelligent feature set definition utilised in conjunction with various state-of-the-art classifying techniques to correctly determine if a reading flagged as a potential false-positive anomaly should be discarded. Through experimental study we have shown that our approach cleans highly ambiguous false-positive observational data effectively. We have also discovered that the Non-Monotonic Reasoning classifier obtained the highest cleaning rate when handling false-positive RFID readings.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent855058 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.publisherIOS Pressen_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom931en_US
dc.relation.ispartofpageto954en_US
dc.relation.ispartofissue6en_US
dc.relation.ispartofjournalIntelligent Data Analysis Journalen_US
dc.relation.ispartofvolume15en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchData Format not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.subject.fieldofresearchcode080499en_US
dc.titleAn Intelligent Approach to Handle False-Positive Radio Frequency Identification Anomaliesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2011 IOS Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.en_US
gro.date.issued2011
gro.hasfulltextFull Text


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