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  • Integrating RFID Technology with Intelligent Classifiers for Meaningful Prediction Knowledge

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    Author
    Darcy, Peter
    Tucker, Steven
    Stantic, Bela
    Year published
    2013
    Metadata
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    Abstract
    Radio Frequency Identification (RFID) is wireless technology that has been designed to automatically identify tagged objects using a reader. Several applications of this technology have been introduced in past literature such as pet identi-fication and luggage tracking which have increased the efficiency and effectiveness of each environment into which it was integrated. However, due to the ambiguous nature of the captured information with the existence of missing, wrong and duplicate readings, the wide-scale adoption of the architecture is limited to commercial sectors where the integrity of the observations can tolerate ...
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    Radio Frequency Identification (RFID) is wireless technology that has been designed to automatically identify tagged objects using a reader. Several applications of this technology have been introduced in past literature such as pet identi-fication and luggage tracking which have increased the efficiency and effectiveness of each environment into which it was integrated. However, due to the ambiguous nature of the captured information with the existence of missing, wrong and duplicate readings, the wide-scale adoption of the architecture is limited to commercial sectors where the integrity of the observations can tolerate ambiguity. In this work, we propose an application of RFID to take the reporting of class attendance and to integrate a predictive classifier to extract high level meaningful information that can be used in diverse areas such as scheduling and low student retention. We conclude by providing an analysis of the core strengths and opportunities that exist for this concept and how we might extend it in future research.
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    Journal Title
    Advances in Internet of Things
    Volume
    3
    Issue
    2
    DOI
    https://doi.org/10.4236/ait.2013.32004
    Copyright Statement
    © 2013 The authors and SciRes. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Pattern Recognition and Data Mining
    Data Structures
    Publication URI
    http://hdl.handle.net/10072/58357
    Collection
    • Journal articles

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