An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

• DOI: 10.32604/iasc.2023.036684
• Publication Date: 2023-05-04T01:20:45Z
• Authors: Meilin Wu; Lianggui Tang; Qingda Zhang; Ke Yan
• Keywords: Feature (linguistics); Multilayer perceptron

As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series jobs, frequently hysteresis in the anticipated values relative real values. The multilayer deep-time convolutional network feature fusion are combined this paper’s proposal of enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) prediction address problem. particular, it possible record deep features temporal dependencies uncertain series, may then be using perceptron. Last but not least, experimental verification conducted on task daily confirmed cases world United States with uncertainty, realizing short-term long-term cases, verifying effectiveness accuracy suggested method, as well reducing results.