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dc.contributor.convenorB. Mans and M. Reynoldsen_AU
dc.contributor.authorDarcy, Peteren_US
dc.contributor.authorStantic, Belaen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorB. Mans and M. Reynoldsen_US
dc.date.accessioned2017-05-03T11:26:28Z
dc.date.available2017-05-03T11:26:28Z
dc.date.issued2010en_US
dc.date.modified2010-07-08T08:09:35Z
dc.identifier.refurihttp://www.comp.mq.edu.au/conferences/acsc10/en_AU
dc.identifier.urihttp://hdl.handle.net/10072/31373
dc.description.abstractSince the emergence of Radio Frequency Identification technology (RFID), the community has been promised a cost effective and efficient means of identifying and tracking large sums of items with relative ease. Unfortunately, due to the unreliable nature of the passive architecture, the RFID revolution has been reduced to a fraction of its intended audience due to anomalies such as missed readings. Previous work within this field of study have focused on restoring the data at the recording phase which we believe does not allow enough evidence for consecutive missed readings to be corrected. In this study, we propose a methodology of intelligently imputing missing observations through the use of an /emph{Artificial Neural Network} (ANN) in a static environment. Through experimentation, we discover the most effective algorithm to train the network and establish that the ANN restores a cleaner data set than other intelligent classifier methodologies.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent763585 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherAustralian Computer Societyen_US
dc.publisher.placeSydney Australiaen_US
dc.publisher.urihttp://www.comp.mq.edu.au/conferences/acsc10/en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameThe Thirty-Third Australasian Computer Science Conferenceen_US
dc.relation.ispartofconferencetitleProceedings of the Thirty-Third Australasian Computer Science Conference (ACSC 2010)en_US
dc.relation.ispartofdatefrom2010-01-18en_US
dc.relation.ispartofdateto2010-01-22en_US
dc.relation.ispartoflocationBrisbane, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchDatabase Managementen_US
dc.subject.fieldofresearchcode080604en_US
dc.titleApplying a Neural Network to Recover Missed RFID Readingsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright (c) 2010, Australian Computer Society, Inc. This paper appeared at the Thirty-First Australasian Computer Science Conference (ACSC2010), Brisbane, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 102. B. Mans and M. Reynolds, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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