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              <text>Predictive Analysis of the Recovery Rate from Coronavirus (COVID-19)</text>
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              <text>Classification; COVID-19; Predictive analysis; Situational analysis; Summarization</text>
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              <text>Estimation of recovery rate of COVID-19 positive persons is significant to measure the severity of the disease for mankind. In this work, prediction of the recovery rate is estimated based on machine learning technology. Standard data set of Kaggle has been used for experimental purpose, and the data sets of COVID cases in Italy, China and India for these countries are considered. Based on that data set and the present scenario, the proposed technique predicts the recovery rate.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Bhattacharya A.; Ghosh G.; Mandal R.; Ghatak S.; Samanta D.; Shukla V.K.; Mukherjee S.; Dutta S.; Mandal A.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-291, pp. 309-320.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-4284-5_27" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-4284-5_27&lt;/a&gt;
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              <text>ISSN: 23673370; ISBN: 978-981164283-8</text>
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              <text>Bhattacharya A., Institute of Engineering and Management, Kolkata, India; Ghosh G., Institute of Engineering and Management, Kolkata, India; Mandal R., Institute of Engineering and Management, Kolkata, India; Ghatak S., Institute of Engineering and Management, Kolkata, India; Samanta D., CHRIST (Deemed To Be University), Bengaluru, India; Shukla V.K., Amity University Dubai, Dubai, United Arab Emirates; Mukherjee S., Asansol Engineering College, Asansol, India; Dutta S., Institute of Engineering and Management, Kolkata, India; Mandal A., Institute of Engineering and Management, Kolkata, India</text>
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