The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study
Open accessAbstract
Highlights • Deep Learning based time series forecasting and comparative case study of Covid-19 confirmed and death cases in India and USA.• Recurrent neural network (RNN) based variants of long short term memory (LSTM) are being used to design proposed models.• Convolutional LSTM based model outperform other models with high accuracy and very less error.• One of the unique studies providing state-of-the-art results to help both countries to recede Covid-19 impact.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conference · doi:10.25080/majora-92bf1922-00a · Cited by 11,032
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
A novel coronavirus outbreak of global health concern
Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30185-9 · Cited by 7,700
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,839
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Sherstinsky, A. (2020) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2019.132306 · Cited by 5,680
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,591
It is time to address airborne transmission of coronavirus disease 2019 (COVID-19)
Morawska, L., & Milton, D. K. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa939 · Cited by 1,189
Metadata
- Authors
- Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V.
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 140
- Pages
- 110227
- ISSN
- 0960-0779 (ISSN-L)
- OpenAlex
- W3061808290
- Cited by
- 268
- References
- 24
- FWCI
- 8.18
- Type
- journal article
- Language
- en
- Open access
- green
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