Abstract
In December 2019, the COVID-19 broke out. From that point, the situation has become much dire. The number of cases kept spiking and a cure is still unknown for COVID-19. For this reason, we must be more cautious and take all possible precautions. We know a few things about this disease. Fever happens to be one of the early symptoms of COVID-19. Hence, we do thermal scanning in public places. Our paper proposes a way to make this process more efficient. We can scan body temperature using various sensors and store it in the cloud. Doing so, it gives us more flexibility to monitor the data and predict if someone might suffer from fever in the future. In our analysis, we have found that among the different machine learning algorithms, moving averages smoothing was able to predict the data better. Now, in order to run this machine learning model automatically, we used AWS. Also, due to GUI, it is much easier to use the system. Overall, the main purpose of our work is to collect daily thermal scan reports and use that data for our benefit.
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Athul Das, K., Mounika, S., Krishna Mohan, V. (2022). Automating the Body Temperature Scanning System and Predictive Analysis Using Machine Learning Models. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_14
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