Browsing by Author "Hayashi, Kentaro"

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    Applications of Recurrent Neural Networks in Modeling the COVID-19 Pandemic
    (University of the Witwatersrand, Johannesburg, 2024-03) Hayashi, Kentaro; Mellado, Bruce
    This study attempted to introduce moving averages and a feature selection method to the forecasting model, with the aim of improving the fluctuating values and unstable accuracy of the risk index developed by the University of Witwatersrand and iThemba LABS and used by the Gauteng Department of Health. It was confirmed that the introduction of moving averages improved the fluctuation of the values, with the seven-day moving average being the most effective. For feature selection, Correlation-based Feature Selection(CFS), the simplest of the filter methods with low computational complexity, was introduced as it is not possible to spend as much time as possible on daily operations due to providing information timely. The introduction of CFS was found to enable efficient feature selection.