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    A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

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    Date
    2019
    Author
    Ben Said, Ahmed
    Erradi, Abdelkarim
    Neiat, Azadeh Ghari
    Bouguettaya, Athman
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    Abstract
    This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments. - 2018, Springer Science+Business Media, LLC, part of Springer Nature.
    DOI/handle
    http://dx.doi.org/10.1007/s11036-018-1105-0
    http://hdl.handle.net/10576/14848
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    • Computer Science & Engineering [‎2429‎ items ]

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