activity
20212023
most citedForecasting new diseases in low-data settings using transfer learning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2023

Machine learning-based prediction of Q-voter model in complex networks

Aruane M. Pineda, Paul Kent, Colm Connaughton +1

In this article, we consider machine learning algorithms to accurately predict two variables associated with the -voter model in complex networks, i.e., (i) the consensus time a…

physics.soc-ph2023

Cultural heterogeneity constrains diffusion of innovations

Aruane M. Pineda, Sandro M. Reia, Colm Connaughton +2

Rogers' diffusion of innovations theory asserts that cultural similarity among individuals plays a crucial role in the acceptance of an innovation in a community. However, most stu…

physics.soc-ph2022

Machine learning for the prediction of voter model opinions through complex network structures

Aruane M. Pineda, Caroline L. Alves, Colm Connaughton +1

The inference of outcomes in dynamic processes from structural features of systems is a crucial endeavor in network science. Recent research has suggested a machine learning-based…

cs.LG2022★ 1 cited

Forecasting new diseases in low-data settings using transfer learning

Kirstin Roster, Colm Connaughton, Francisco A. Rodrigues

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting n…

eess.SP2021

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

Caroline L. Alves, Aruane M. Pineda, Kirstin Roster +2

Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis, but the absence of establishe…