Show simple item record

dc.contributor.authorDarcy, Peter
dc.contributor.authorStantic, Bela
dc.contributor.authorSattar, Abdul
dc.contributor.editorNemai Chandra Karmaka
dc.date.accessioned2017-05-03T11:26:44Z
dc.date.available2017-05-03T11:26:44Z
dc.date.issued2013
dc.date.modified2014-02-07T05:53:58Z
dc.identifier.isbn978-1-4666-2080-3
dc.identifier.doi10.4018/978-1-4666-2080-3.ch003
dc.identifier.urihttp://hdl.handle.net/10072/54524
dc.description.abstractRadio Frequency Identification (RFID) refers to wireless technology that is used to seamlessly and automatically track various amounts of items around an environment. This technology has the potential to improve the efficiency and effectiveness of tasks such as shopping and inventory saving commercial organisations both time and money. Unfortunately, the wide scale adoption of RFID systems have been hindered due to issues such as false-negative and false-positive anomalies that lower the integrity of captured data. In this chapter, we propose the utilisation three highly intelligent classifiers, specifically a Bayesian Network, Neural Network and Non-Monotonic Reasoning, to handle missing, wrong and duplicate observations. After discovering the potential from using Bayesian Networks, Neural Networks and Non-Monotonic Reasoning to correct captured data, we decided to improve upon the original approach by combining the three methodologies into an integrated classifier. From our experimental evaluation, we have shown the high results obtained from cleaning both false-negative and false-positive anomalies using each of our concepts, and the potential it holds to enhance physical RFID systems.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIGI Global
dc.publisher.placeUnited States
dc.publisher.urihttp://dx.doi.org/10.4018/978-1-4666-2080-3
dc.relation.ispartofbooktitleAdvanced RFID Systems, Security, and Applications
dc.relation.ispartofchapter3
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom41
dc.relation.ispartofpageto73
dc.rights.retentionY
dc.subject.fieldofresearchData engineering and data science
dc.subject.fieldofresearchcode460501
dc.titleThe Evolution of Intelligent Classifiers into an Integrated Approach to Correct RFID Anomalies
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.date.issued2013
gro.hasfulltextNo Full Text
gro.griffith.authorStantic, Bela
gro.griffith.authorSattar, Abdul


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Book chapters
    Contains book chapters authored by Griffith authors.

Show simple item record