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View all- Subair AG G(2024)Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learningProcedia Computer Science10.1016/j.procs.2024.03.195233(56-65)Online publication date: 2024
Covid-19 spread Forecast with respect to vaccination based on LSTM and GRU
Authors: 
Sidharth Sunil, Vishnu Dinesh Kumar, Akhil Babu, Goutham Thilak, Divya UdayanAuthors Info & Claims
Pages 32 - 36
Abstract
All the studies proposed so far have either tackled the issue of Covid 19 spread around the world or the vaccination coverage of a specific region. The relation between Covid 19 and vaccination is not exploited in any of these studies to achieve better performance in the AI models they propose. Our solution is to learn and forecast the trend of Covid 19 spread across the world with respect to vaccination. Preprocessing the data to exempt any covid cases before vaccination would remove the unnecessary training of the machine learning model with patterns that can be considered as noise. Our study introduces each country to a model tailored for them. Our model would allow the researchers to approach Covid 19 spread prediction from a new perspective with respect to vaccination statistics and also to choose which model to presume according to their needs.
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Published In
August 2022
710 pages
ISBN:9781450396752
DOI:10.1145/3549206
- General Chairs:
- Sartaj Sahni,
- Vikas Saxena,
- Program Chair:
- Sundaraja Sitharama Iyengar
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Published: 24 October 2022
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IC3-2022
IC3-2022: 2022 Fourteenth International Conference on Contemporary Computing
August 4 - 6, 2022
Noida, India
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View all- Subair AG G(2024)Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learningProcedia Computer Science10.1016/j.procs.2024.03.195233(56-65)Online publication date: 2024
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