{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:46:21Z","timestamp":1743137181731,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030755287"},{"type":"electronic","value":"9783030755294"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-75529-4_5","type":"book-chapter","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T19:02:43Z","timestamp":1621969363000},"page":"59-67","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modeling and Prediction of COVID-19 in India Using Machine Learning"],"prefix":"10.1007","author":[{"given":"Arindam","family":"Ghosh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6156-6292","authenticated-orcid":false,"given":"Arnab","family":"Sadhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Yang, Z., et al.: Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J. Thoracic Dis. 12(3) (2020). https:\/\/doi.org\/10.21037\/jtd.2020.02.64. http:\/\/jtd.amegroups.com\/article\/view\/36385","DOI":"10.21037\/jtd.2020.02.64"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of Covid-19 pandemic using machine learning and cloud computing. Internet Things 1\u201316 (2020). https:\/\/doi.org\/10.1016\/j.iot.2020.100222","DOI":"10.1016\/j.iot.2020.100222"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Gupta, R., Pandey, G., Chaudhary, P., Pal, S.: SEIR and regression model based covid-19 outbreak predictions in India. medRxiv (2020). https:\/\/doi.org\/10.1101\/2020.04.01.20049825. https:\/\/www.medrxiv.org\/content\/early\/2020\/04\/03\/2020.04.01.20049825","DOI":"10.1101\/2020.04.01.20049825"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Tomar, A., Gupta, N.: Prediction for the spread of Covid-19 in India and effectiveness of preventive measures. Sci. Total Environ. 728 (2020). https:\/\/doi.org\/10.1016\/j.scitotenv.2020.138762. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0048969720322798","DOI":"10.1016\/j.scitotenv.2020.138762"},{"issue":"7","key":"5_CR5","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s00477-020-01827-8","volume":"34","author":"R Sujath","year":"2020","unstructured":"Sujath, R., Chatterjee, J.M., Hassanien, A.E.: A machine learning forecasting model for COVID-19 pandemic in India. Stochast. Environ. Res. Risk Assess. 34(7), 959\u2013972 (2020). https:\/\/doi.org\/10.1007\/s00477-020-01827-8","journal-title":"Stochast. Environ. Res. Risk Assess."},{"issue":"6","key":"5_CR6","doi-asserted-by":"publisher","first-page":"890","DOI":"10.3390\/math8060890","volume":"8","author":"G Pinter","year":"2020","unstructured":"Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., Gloaguen, R.: Covid-19 pandemic prediction for Hungary; a hybrid machine learning approach. Mathematics 8(6), 890 (2020). https:\/\/doi.org\/10.3390\/math8060890","journal-title":"Mathematics"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Azarafza, M., Azarafza, M., Tanha, J.: Covid-19 infection forecasting based on deep learning in Iran, pp. 1\u20137. medRxiv. https:\/\/doi.org\/10.1101\/2020.05.16.20104182","DOI":"10.1101\/2020.05.16.20104182"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chaos.2020.109864","volume":"135","author":"VKR Chimmula","year":"2020","unstructured":"Chimmula, V.K.R., Zhang, L.: Time series forecasting of Covid-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals 135, 1\u20136 (2020). https:\/\/doi.org\/10.1016\/j.chaos.2020.109864","journal-title":"Chaos Solitons Fractals"}],"container-title":["Communications in Computer and Information Science","Computational Intelligence in Communications and Business Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75529-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T19:04:27Z","timestamp":1621969467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75529-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030755287","9783030755294"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75529-4_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICBA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Communications and Business Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiniketan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicba2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cicba.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"84","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.44","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.54","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}