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Abstract
Due to the huge impact of COVID-19, the world is currently facing a medical emergency and shortage of vaccine. Many countries do not have enough medical equipment and infrastructure to tackle this challenge. Due to the lack of a central administration to guide the countries to take the necessary precautions, they do not proactively identify the cases in advance. This has caused Covid-19 cases to be on the increase, with the number of cases increasing at a geometric progression. Rapid testing, RT-PCR testing, and a CT-Scan/X-Ray of the chest are the primary procedures in identifying the covid-19 disease. Proper immunization is delivered on a priority basis based on the instances discovered in order to preserve human lives. In this research paper, we suggest a technique for identifying covid-19 positive cases and determine the most affected locations of covid-19 cases for vaccine distribution in order to limit the disease's impact. To handle the aforementioned issues, we propose a cloud based image analysis approach for using a COVID-19 vaccination distribution (CIA-CVD) model. The model uses a deep learning, machine learning, digital image processing and cloud solution to deal with the increasing cases of COVID-19 and its priority wise distribution of the vaccination.
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Details
1 Nirma University, Computer Science and Engineering Department, Institute of Technology, Ahmedabad, India (GRID:grid.412204.1) (ISNI:0000 0004 1792 2351)
2 Rajendra University, Department of Computer Science, Balangir, India (GRID:grid.412204.1)
3 GLA University, Department of CEA, Institute of Engineering and Technology, Mathura, India (GRID:grid.448881.9) (ISNI:0000 0004 1774 2318)
4 Afe Babalola University, Department of Electrical/Electronics and Computer Engineering, Ado-Ekiti, Nigeria (GRID:grid.448570.a) (ISNI:0000 0004 5940 136X); Saveetha Institute of Medical and Technical Sciences, Saveetha School of Engineering, Chennai, India (GRID:grid.412431.1) (ISNI:0000 0004 0444 045X)