Clustering Analysis in the Student Academic Activities on COVID-19 Pandemic in Mexico

The pandemic caused by the COVID-19 disease has affected all aspects of the life of the people in every region of the world. The academic activities at universities in Mexico have been particularly disturbed by two years of confinement;all activities were migrated to an online modality where improvised actions and prolonged isolation have implied a significant threat to the educational institutions. Amid this pandemic, some opportunities to use Artificial Intelligence tools for understanding the associated phenomena have been raised. In this sense, we use the K-means algorithm, a well-known unsupervised machine learning technique, to analyze the data obtained from questionaries applied to students in a Mexican university to understand their perception of how the confinement and online academic activities have affected their lives and their learning. Results indicate that the K-means algorithm has better results when the number of groups is bigger, leading to a lower error in the model. Also, the analysis helps to make evident that the lack of adequate computing equipment, internet connectivity, and suitable study spaces impact the quality of the education that students receive, causing other problems, including communication troubles with teachers and classmates, unproductive classes, and even accentuate psychological issues such as anxiety and depression. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Artificial Neural Networks for COVID-19 Forecasting in Mexico: An Empirical Study

Artificial Neural Networks (ANN) have encountered interesting applications in forecasting several phenomena, and they have recently been applied in understanding the evolution of the novel coronavirus COVID-19 epidemic. Alone or together with other mathematical, dynamical, and statistical methods, ANN help to predict or model the transmission behavior at a global or regional level, thus providing valuable information for decision-makers. In this research, four typical ANN have been used to analyze the historical evolution of COVID-19 infections in Mexico: Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LTSM) neural networks, and the hybrid approach LTSM-CNN. From the open-source data of the Resource Center at the John Hopkins University of Medicine, a comparison of the overall qualitative fitting behavior and the analysis of quantitative metrics were performed. Our investigation shows that LSTM-CNN achieves the best qualitative performance;however, the CNN model reports the best quantitative metrics achieving better results in terms of the Mean Squared Error and Mean Absolute Error. The latter indicates that the long-term learning of the hybrid LSTM-CNN method is not necessarily a critical aspect to forecast COVID-19 cases as the relevant information obtained from the features of data by the classical MLP or CNN. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.