Bibliometric analysis of scientific production on student adaptation to college

Adaptation to university life represents a major challenge for young people. Given the new difficulties added to this challenge because of COVID-19, the aim of this study is to analyse the scientific production on this issue over the last three decades (1991-2021). For this purpose, the bibliometric study has been combined with social network analysis techniques using Rstudio and Vosviewer software. A total of 291 articles from the Scopus database were analysed. The results provide a holistic view of this field and its evolution over time. © GKA Ediciones, authors. Creative Commons Reconocimiento-NoComercial-SinObraDerivada.

A Novel Layered Learning Approach for Forecasting Respiratory Disease Excess Mortality during the COVID-19 pandemic

Forecasting model selection and model combination are the two contending approaches in the time series forecasting literature. Ensemble learning is useful for addressing a given predictive task by different predictive models when direct mapping from inputs to outputs is inaccurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, we build each model with a specific holdout and make the ensemble model of time series with a dynamic selection approach. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series of reported respiratory disease deaths to show the amount of improvement in predictive performance of excess mortality. Then we compare the forecasting outcome of our model with the corresponding total deaths of COVID-19 for selected countries. © 2021 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.