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    Neural Network-Based Approach for Predicting Trust Values Based on Non-uniform Input in Mobile Applications

    Access Status
    Fulltext not available
    Authors
    Raza, Muhammad
    Hussain, Farookh Khadeer
    Hussain, Omar
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Raza, Muhammad and Hussain, Farookh Khadeer and Hussain, Omar Khadeer. 2012. Neural Network-Based Approach for Predicting Trust Values Based on Non-uniform Input in Mobile Applications. The Computer Journal. 55 (3): pp. 347-378.
    Source Title
    The Computer Journal
    DOI
    10.1093/comjnl/bxr104
    ISSN
    00104620
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/37540
    Collection
    • Curtin Research Publications
    Abstract

    Recently, there has been much research focus on trust and reputation modelling as one of the key strategies for the formation of successful business intelligence strategies, particularly for service in mobile applications. One of the key trust modelling activities is trust prediction. During this process, the accuracy and reliability of the predicted trust values play an important role in the making of informed business decisions. Key factors to be considered at this stage are the variability and the high levels of distortion in the input series that have to be captured when predicting the trust values at a point in time in the future. In this paper, we propose a Multi-layer Feed Forward Artificial Neural Network to predict the future trust values of entities (services, agents, products etc.) for a future point in time based on data series input. We use four different non-uniform’ data input series and measure the accuracy of the predicted values under different experimental scenarios for benchmarking and comparison with existing approaches. Results indicate that the model is reliable in predicting trust values even in scenarios where there are only limited data available on training the neural network and a high level of distortion is present in the input series.

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