Selected article for: "absolute error and machine learning deep technique"
Author: Ayris, Devante; Horbury, Kye; Williams, Blake; Blackney, Mitchell; See, Celine Shi Hui; Shah, Syed Afaq Ali
Title: Deep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)
  • Cord-id: sj1lkd7x
  • Document date: 2020_7_29
    • ID: sj1lkd7x
    Snippet: SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propo
    Document: SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance of the proposed models.

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