ABSTRACT
Amidst the deadly COVID-19 pandemic situation, the increasing number of cases is a major concern especially at places where tests are not available easily, are inconvenient and results take a long time to be declared. We present a solution by which tests can be performed easily by individuals with the aid of a mask. It involves no hazard and saves time for immediate treatment of positive patients. To enhance the efficiency of the product, we have also incorporated a predictive model using machine learning which produces outcome based on real life scenarios. © 2021 IEEE.
ABSTRACT
Sports data has become widely available in the recent past. With the improvement of machine learning techniques, there have been attempts to use sports data to analyze not only the outcome of individual games but also to improve insights and strategies. The outbreak of COVID-19 has interrupted sports leagues globally, giving rise to increasing questions and speculations about the outcome of this season’s leagues. What if the season was not interrupted and concluded normally? Which teams would end up winning trophies? Which players would perform the best? Which team would end their season on a high and which teams would fail to keep up with the pressure? We aim to tackle this problem and develop a solution. In this paper, we propose UCLData, which is a dataset containing detailed information of UEFA Champions League games played over the past six years. We also propose a novel autoencoder based machine learning pipeline that can come up with a story on how the rest of the season will pan out. © Springer Nature Switzerland AG 2020.