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    A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

    Venkateswari Pichaimani1, K. R. Manjula2,*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 383-397, 2022, DOI:10.32604/iasc.2022.023105

    Abstract The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset… More >

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