Volgenau School of Engineering Graduate Research
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Browsing Volgenau School of Engineering Graduate Research by Author "Bhandari, Shova"
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Item COVID-19 Vaccine Data Review and Reactions on Social Media(2021-04-28) Bhandari, Shova; Mohamed, Mazen; Maupin, JakeThe USA is one of the hardest hit areas by COVID-19. As of March 13th, 2021, 29.5M people were infected and 534K have lost their lives [1]. The USA government has authorized Pfizer and Moderna mRNA COVID-19 vaccines for the prevention of Coronavirus. However, these vaccines are allocated to certain groups and are not available to the public yet. Vaccine’s demand is skyrocketing as the United States of America is unable to contain the virus, and a new more contagious and deadlier Covid-19 variant is emerging. Given the impact of the COVID-19 pandemic, it is imperative to efficiently distribute vaccines to contain and eventually eradicate the virus. It is important to identify the hardest hit region/state that is still struggling to fight and protect their residence from viruses. The main question we are attempting to answer is how we can use COVID-19 data to identify focal points for COVID-19 breakout and formulate an efficient and speedy response. In this research we analyze COVID-19 vaccine trend.Item Information Retrieval Model for Social Media Applications(2021-01) Bhandari, ShovaSocial networks are rich source of data to analyze user habits in all aspects of life. User’s behaviour is decisiv e component of a health system in various countries. Promoting good behaviour can improve the public health signif icantly. In this work, we develop a new model for social network analysis by using text analysis approach. We defi ne each user reaction to global pandemic with analysing his online behaviour. Clustering a group of online users w ith similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social media have significant effect on public health and reducing the effect of global pandemic. In this work, we introduce a new approach to clustering habits based on user activities on social media in the time of pandemic and recommend a machine learning model to promote health in the online platforms.