Open Access
| Issue |
ITM Web Conf.
Volume 46, 2022
International Conference on Engineering and Applied Sciences (ICEAS’22)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 6 | |
| Section | Computer Sciences | |
| DOI | https://doi.org/10.1051/itmconf/20224602007 | |
| Published online | 06 June 2022 | |
- “Worldometers.info,” 2020, [Online]. Available: https://www.worldometers.info/. [Google Scholar]
- “World Health Organization (WHO),” 2020. https://covid19.who.int/region/searo/country/in [Google Scholar]
- “REUTERS Covid-19 Tracker.” https://graphics.reuters.com/world-coronavirus-tracker-and-maps/countries-and-territories/india/. [Google Scholar]
- S. Hanumanthu, “Role of Intelligent Computing in COVID-19 Prognosis: A State- of-the-Art Review,” Chaos, Solitons Fractals Interdiscip. J. Nonlinear Sci. Nonequilibrium Complex Phenom., p. 109947, 2020, doi: 10.1016/j.chaos.2020.109947. [Google Scholar]
- F. Shahid, A. Zameer, and M. Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,” Chaos, Solitons Fractals Interdiscip. J. Nonlinear Sci. Nonequilibrium Complex Phenom., vol. 140, p. 110212, 2020, doi: 10.1016/j.chaos.2020.110212. [Google Scholar]
- K. S. Bhangu, J. K. Sandhu, and L. Sapra, “Time series analysis of COVID-19 cases,” vol. 1, no. November 2020, pp. 40–48, 2022, doi: 10.1108/WJE-09-2020-0431. [Google Scholar]
- D. Tiwari, B. S. Bhati, and B. Nagpal, “Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques,” vol. 2, no. April, pp. 1–20, 2021, doi: 10.1111/exsy.12714. [Google Scholar]
- F. Rustam et al., “COVID-19 Future Forecasting Using,” vol. 4, pp. 1–12, 2020, doi: 10.1109/ACCESS.2020.2997311. [Google Scholar]
- H. Verma, S. Mandal, and A. Gupta, “Temporal deep learning architecture for prediction of COVID-19 cases in India,” no. January, 2020. [Google Scholar]
- N. Ayoobi, D. Sharifrazi, R. Alizadehsani, and A. Shoeibi, “Results in Physics Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods,” vol. 27, no. March, 2021, doi: 10.1016/j.rinp.2021.104495. [Google Scholar]
- N. Science et al., “Time series forecasting of Covid-19 using deep learning models: India- USA comparative case study,” Chaos, Solitons Fractals Interdiscip. J. Nonlinear Sci. Nonequilibrium Complex Phenom., vol. 140, p. 110227, 2020, doi:10.1016/j.chaos.2020.110227. [Google Scholar]
- P. T. Yamak, “A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting,” 2017. [Google Scholar]
- Y. Jeon and S. Seong, “Robust recurrent network model for intermittent time-series forecasting,” Int. J. Forecast., no. xxxx, 2021, doi: 10.1016/j.ijforecast.2021.07.004. [Google Scholar]
- D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, “DeepAR: Probabilistic forecasting with autoregressive recurrent networks,” Int. J. Forecast., no. xxxx, 2019, doi: 10.1016/j.ijforecast.2019.07.001. [Google Scholar]
- V. Flunkert, D. Salinas, and J. Gasthaus, “DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks,” 2017. [Google Scholar]
- A. Tadjer, A. Hong, and R. B. Bratvold, “Machine learning based decline curve analysis for short-term oil production forecast,” 2021, doi: 10.1177/01445987211011784. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.