{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:36:40Z","timestamp":1743086200041,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811666230"},{"type":"electronic","value":"9789811666247"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-6624-7_46","type":"book-chapter","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T04:26:48Z","timestamp":1646022408000},"page":"459-470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting the Trends of COVID-19 Cases Using LSTM, GRU and RNN in India"],"prefix":"10.1007","author":[{"given":"Sweeti","family":"Sah","sequence":"first","affiliation":[]},{"given":"Akash","family":"Kamerkar","sequence":"additional","affiliation":[]},{"given":"B.","family":"Surendiran","sequence":"additional","affiliation":[]},{"given":"R.","family":"Dhanalakshmi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Alakus, T.B, Turkoglu, I.: Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons Fractals 110120 (2020)","DOI":"10.1016\/j.chaos.2020.110120"},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Arora, P., Kumar, H., Panigrahi, B.K.: Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos Solitons Fractals 139, 110017 (2020)","DOI":"10.1016\/j.chaos.2020.110017"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Ayyoubzadeh, S.M., Ayyoubzadeh, S.M., Zahedi, H., Ahmadi, M., Kalhori, S.R.N.: Predicting COVID-19 incidence through analysis of Google trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveillance 6(2), e18828 (2020)","DOI":"10.2196\/18828"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Basu, S., Campbell, R.H.: Going by the numbers: learning and modeling COVID-19 disease dynamics. medRxiv (2020)","DOI":"10.1101\/2020.05.18.20106112"},{"key":"46_CR5","doi-asserted-by":"crossref","unstructured":"Chimmula, V.K.R., Zhang, L.: Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 109864 (2020)","DOI":"10.1016\/j.chaos.2020.109864"},{"key":"46_CR6","unstructured":"Britz, D.: Recurrent Neural Network. http:\/\/www.wildml.com\/2015\/10\/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano\/. Posted on 27 October 2015"},{"key":"46_CR7","unstructured":"dProgrammer lopez: RNN, LSTM and GRU. http:\/\/dprogrammer.org\/rnn-lstm-gru, 6 April 2019"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Dutta, S., Bandyopadhyay, S.K., Kim, T.-H.: CNN-LSTM model for verifying predictions of COVID-19 cases. Asian J. Res. Comput. Sci. 25\u201332 (2020)","DOI":"10.9734\/ajrcos\/2020\/v5i430141"},{"key":"46_CR9","unstructured":"GeeksforGeeks: https:\/\/www.geeksforgeeks.org\/introduction-to-recurrent-neural-network\/, 3 October 2018"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Hartono, P.: Similarity maps and pairwise predictions for transmission dynamics of COVID-19 with neural networks. Inform. Med. Unlocked 20, 100386 (2020)","DOI":"10.1016\/j.imu.2020.100386"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395(10223), 497\u2013506 (2020)","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"46_CR12","unstructured":"Kaggle: COVID-19 in India. https:\/\/www.kaggle.com\/sudalairajkumar\/covid19-in-india?select=covid_19_india.csv. Accessed on 13 September 2020"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"K\u0131rba\u015f, \u0130., S\u00f6zen, A., Tuncer, A.D., Kazanc\u0131o\u011flu, F.\u015e.: Comperative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos Solitons Fractals 110015 (2020)","DOI":"10.1016\/j.chaos.2020.110015"},{"issue":"4","key":"46_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3411760","volume":"1","author":"M Kumar","year":"2020","unstructured":"Kumar, M., Gupta, S., Kumar, K., Sachdeva, M.: Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model. Dig. Govern. Res. Prac. 1(4), 1\u20139 (2020)","journal-title":"Dig. Govern. Res. Prac."},{"key":"46_CR15","unstructured":"Phi, M.: Illustrated guide to LSTM\u2019s and GRU\u2019s. https:\/\/towardsdatascience.com\/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21, 24 September 2018"},{"key":"46_CR16","unstructured":"Oinkina: Understanding LSTM network. https:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/, 27 August 2015"},{"key":"46_CR17","unstructured":"Organization, W.H., et al.: Q&A on coronaviruses (2020b)"},{"key":"46_CR18","doi-asserted-by":"publisher","unstructured":"Di Pietro, R., Hager, G.D.: Chapter 21\u2014Deep learning: RNNs and LSTM. In: Zhou, S.K., Rueckert, D., Fichtinger, G. (eds.) Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 503\u2013519. Academic Press (2020). ISBN 9780128161760, https:\/\/doi.org\/10.1016\/B978-0-12-816176-0.00026-0. http:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128161760000260","DOI":"10.1016\/B978-0-12-816176-0.00026-0"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Shahid, F., Zameer, A., Muneeb, M.: Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals 110212 (2020)","DOI":"10.1016\/j.chaos.2020.110212"},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Acter, T., Uddin, N., Das, J., Akhter, A., Choudhury, T.R., Kim, S.: Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: a global health emergency. Sci. Total Environ. 138996 (2020)","DOI":"10.1016\/j.scitotenv.2020.138996"},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"Tomar, A., Gupta, N.: Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci. Total Environ. 138762 (2020)","DOI":"10.1016\/j.scitotenv.2020.138762"},{"key":"46_CR22","doi-asserted-by":"crossref","unstructured":"Wang, P., Zheng, X., Ai, G., Liu, D., Zhu, B.: Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: case studies in Russia, Peru and Iran. Chaos Solitons Fractals 110214 (2020)","DOI":"10.1016\/j.chaos.2020.110214"},{"key":"46_CR23","unstructured":"World Health Organization: Report of the WHO-China joint mission on coronavirus disease (COVID-19) (2020). https:\/\/www.who.int\/docs\/default-source\/coronaviruse\/who-china-joint-mission-on-covid-19-final-report.pdf"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Zeroual, A., Harrou, F., Dairi, A., Sun, Y.: Deep learning methods for forecasting COVID-19 time-Series data: a comparative study. Chaos Solitons Fractals 110121 (2020)","DOI":"10.1016\/j.chaos.2020.110121"}],"container-title":["Smart Innovation, Systems and Technologies","Intelligent Data Engineering and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-6624-7_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T17:20:59Z","timestamp":1651771259000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-6624-7_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811666230","9789811666247"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-6624-7_46","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}