The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India
Open accessAbstract
In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level.
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Cited by
Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study
Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110227 · Cited by 268
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Metadata
- Authors
- Arora, P., Kumar, H., & Panigrahi, B. K.
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 139
- Pages
- 110017
- ISSN
- 0960-0779 (ISSN-L)
- OpenAlex
- W3036309913
- Cited by
- 355
- References
- 19
- FWCI
- 11.35
- Type
- journal article
- Language
- en
- Open access
- green
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