Forecasting COVID-19 Patients & Power Demand in Bangladesh
| dc.contributor.author | Azad, Saad Ebna | |
| dc.contributor.author | Anwar, Atif Mohd. | |
| dc.contributor.author | Saharear, Fahim Md. | |
| dc.date.accessioned | 2022-04-20T06:28:43Z | |
| dc.date.available | 2022-04-20T06:28:43Z | |
| dc.date.issued | 2021-03-30 | |
| dc.description | Supervised by Prof. Dr. Khondokar Habibul Kabir, Department of Electrical and Electronics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh | |
| dc.description.abstract | This research gives a better understanding & analysis of the global pandemic situation of covid-19 in Bangladesh. Using machine learning this model accurately forecasts new cases, deaths & recoveries. Then it forecasts the necessary hospital seats & power demands for those seats. This hopefully will provide a better support for the struggling power sector of developing countries like Bangladesh. This will also help to prepare other countries who are struggling for such epidemic situations in the future. | |
| dc.identifier.other | https://repository.iutoic-dhaka.edu/server/api/core/items/fab2d02d-8286-469a-ac4c-510e2a44c000 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/1365 | |
| dc.language.iso | en | |
| dc.publisher | Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh | |
| dc.source | IUT Institutional Repository | |
| dc.subject | Machine learning, COVID-19, ARIMA, CSSE, SVM, RMSE | |
| dc.title | Forecasting COVID-19 Patients & Power Demand in Bangladesh | |
| dc.type | Thesis |
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