An in-depth analysis of data reduction methods for sustainable deep learning [version 2; peer review: 2 approved]

Autor: Matteo Rucco, Javier Perera-Lago, Eduardo Paluzo-Hidalgo, Victor Toscano-Duran, Miguel A. Gutiérrez-Naranjo, Rocio Gonzalez-Diaz

Publikováno v: Open Research Europe, Vol 4 (2024)

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the im

Externí odkaz: https://doaj.org/article/f4f9346ede664b448d5ec20b6aabbb83

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Autor: Lorenzo Falsetti, Matteo Rucco, Marco Proietti, Giovanna Viticchi, Vincenzo Zaccone, Mattia Scarponi, Laura Giovenali, Gianluca Moroncini, Cinzia Nitti, Aldo Salvi

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)

Abstract Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper w

Externí odkaz: https://doaj.org/article/5eced6bb0775495d8144bc16703f310b

Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning

Autor: Matteo Rucco, Giovanna Viticchi, Lorenzo Falsetti

Publikováno v: Mathematics, Vol 8, Iss 5, p 770 (2020)

Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumor, which tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumors. Usually, GBMs are detected by magnetic resonance

Externí odkaz: https://doaj.org/article/4e58ea8c0f624f21a90a0b33b31be131

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Autor: Giovanna Viticchi, Marco Proietti, Mattia Scarponi, Laura Giovenali, Gianluca Moroncini, Vincenzo Zaccone, Lorenzo Falsetti, Matteo Rucco, Aldo Salvi, Cinzia Nitti

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Scientific Reports

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ced50d766401737fcb31f482787c2549
https://doi.org/10.1038/s41598-021-97218-2

Persistent entropy for separating topological features from noise in vietoris-rips complexes

Autor: Matteo Rucco, Nieves Atienza, Rocio Gonzalez-Diaz

Publikováno v: idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
idUS. Depósito de Investigación de la Universidad de Sevilla
instname

Persistent homology studies the evolution of k-dimensional holes along a nested sequence of simplicial complexes (called a filtration). The set of bars (i.e. intervals) representing birth and death times of k-dimensional holes along such sequence is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c66c61e2706808204cc6503f00456b1
https://doi.org/10.1007/s10844-017-0473-4