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The cornovirus disease (COVID-19) pandemic has had a severe impact on our daily lives. As a result, there has been an increasing demand for technological solutions to overcome such challenges. The Internet of Things (IoT) has recently emerged to improve many aspects of human’s day-to-day activities and routines. IoT makes it easier to follow the safety guidelines and precautions provided by the World Health Organization (WHO). Prior reports have shown that the world nowadays may need more IoT facilities than ever before. However, little is known about the reaction of the IoT community towards defeating the COVID-19 pandemic, technologies being used, solutions being provided, and how our societies perceive the IoT means available to them. In this paper, we conduct an empirical study to investigate the IoT response to the COVID-19 pandemic. In particular, we study the characteristics of the IoT solutions hosted on a large online IoT community (i.e., Hackster.io) throughout the year of 2020. The study: (a) explores the proportion, types, and nations of IoT solutions/engineers that contributed to defeating COVID-19, (b) characterizes the complexity of COVID-19 IoT solutions, and (c) identifies how IoT solutions are perceived by the surrounding community. Our results indicate that IoT engineers have been actively working towards providing solutions to help their societies, especially in the most affected nations. Our findings (i) provide insights into the aspects IoT practitioners need to pay more attention to when developing IoT solutions for COVID-19 and to (ii) outlines the common IoT solutions and technologies available to humans to deal with the current challenges.

Text classification is still an area growing as text sizes grow rapidly with the information and internet revolution. The majority of conventional text classification approaches are hierarchical training, where the classification system explicitly differentiates between groups. However, this is different from the nature of language processing which suffers from ambiguity and lack of clarity. That is, the instance could be of more than one class. This paper handles the problem of text classification by applying a novel classification method based on sequence alignment with simple fuzzy concepts. The experiments showed expected performance compared to other conventional classifications of natural languages.