Maria Silvia Pini

Andrea Loreggia, Anna Passarelli, Maria Silvia Pini

The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events. Artificial intelligence techniques may be useful in analyzing data related to areas affected by the virus. The aim of this work is to investigate any possible relationships between air quality and confirmed cases of COVID-19 in Italian districts. Specifically, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained on the environmental parameters to predict the number of future infected cases may be accurate. Predictive models may be useful for helping institutions in making decisions for protecting the population and contrasting the pandemic.

Cristina Cornelio, Michele Donini, Andrea Loreggia et al.

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.