4 citations · 6 across the 4 of their papers we have counts for
4 papers
A multi-artifact EEG denoising by frequency-based deep learning
Matteo Gabardi, Aurora Saibene, Francesca Gasparini +2
Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. Thes…
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Alessandro Bregoli, Karin Rathsman, Marco Scutari +2
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is importa…
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models…
CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
Daniele Codecasa, Fabio Stella
Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not cha…