Davide Barbieri
Biographic Data
| ID | 6326008 |
|---|---|
| NAME | Davide Barbieri |
| GIVEN NAMES | Davide |
| FAMILY NAME | Barbieri |
| SIGNATURE | BARBIERI D |
| AFFILIATIONS | University of Ferrara |
| ORCID | 0000-0002-7324-8811 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance
Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine s…
Le cerveau comme virtuel incarné
Dans ce travail nous souhaitons analyser l’évolution des modèles différentiels du cerveau qui sont sous-jacents à toute activité de perception et de cognition. Les contraintes différentielles – la distribution des forces, des différences et des tensions – constituent le virtuel de la morphodynamique cognitive, s'actualisant dans la perception/cognition. Notre champ d'interêt concerne notamment l'évolution des modèles des profils récepteurs des co…
No prominent works on this page.
Le cerveau comme virtuel incarné
Dans ce travail nous souhaitons analyser l’évolution des modèles différentiels du cerveau qui sont sous-jacents à toute activité de perception et de cognition. Les contraintes différentielles – la distribution des forces, des différences et des tensions – constituent le virtuel de la morphodynamique cognitive, s'actualisant dans la perception/cognition. Notre champ d'interêt concerne notamment l'évolution des modèles des profils récepteurs des co…
Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance
Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine s…
Action Observation and Synchronization (1 works) · Anthropometry (1 works) · Artificial Intelligence (1 works) · Athletes (1 works) · Cardiovascular Effects of Exercise (1 works) · Computer Science (1 works) · Data mining (1 works) · Decision tree (1 works) · Decision tree learning (1 works) · Environmental health (1 works)