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  • The Evolution of Intelligent Classifiers into an Integrated Approach to Correct RFID Anomalies

    Author(s)
    Darcy, Peter
    Stantic, Bela
    Sattar, Abdul
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Year published
    2013
    Metadata
    Show full item record
    Abstract
    Radio 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 ...
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    Radio 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.
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    Book Title
    Advanced RFID Systems, Security, and Applications
    Publisher URI
    http://dx.doi.org/10.4018/978-1-4666-2080-3
    DOI
    https://doi.org/10.4018/978-1-4666-2080-3.ch003
    Subject
    Data engineering and data science
    Publication URI
    http://hdl.handle.net/10072/54524
    Collection
    • Book chapters

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