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Solution to Uncertainty Using Random Forests for Predicting Mobile User Behavior

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Mobile phones have gained tremendous growth due to its wide range of applications. The introduction of smart phones and its location based services provides comfortable usages to the mobile users. Prediction of user behavior and locations has become so important in order to provide uninterrupted service. In our previous work, the Modified Particle Swarm Optimization based Optimal Time Interval Identification for Predicting Mobile User Behavior (MPSO-OTI2-PMB) was introduced in location based services. Though the method provided higher performance than its predecessors, the predicting approach can further be enhanced to avoid the uncertainty in the system. The Prediction engine, used prediction strategy model which causes the uncertainty problem to the MPSO method, should be replaced with a more efficient strategy. Hence in order provide maximum support to our system, the prediction engine is provided with a model called Random forest approach. The random forest classifier uses the pattern classification approach which can be altered to predict the locations and user behavior in a mobile environment. The performance of the MPSO based method can be improved efficiently by using the random forest classifier. Unlike the prediction strategy, the random forest approach enables prediction by recognition of exact classes of the patterns in the nodes available. Experimental results show that the proposed MPSO with Random forest strategy solves the uncertainty problem along with providing higher performance than the MPSO method with prediction strategy.
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Keywords: Particle Swarm Optimization; Prediction Engine; Random Forest Approach

Document Type: Research Article

Affiliations: 1: Research Scholar, Bharathiar University, Coimbatore 641046, Tamilnadu, India 2: J.K.K.Nattraja College of Engineering and Technology, Kumarapalayam 638183, Tamilnadu, India

Publication date: January 1, 2016

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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