Type
Funder
Country
—
Documents
12
through any edge
Papers
0
authorship edges
Awards made
21
18 grant numbers
Named as funder
12
no grant number
Identifiers
Funding
Read from the funding tables, never from the document count — that count includes authorship, so it ranks institutions, not funders.
What Consejería de Sanidad, Comunidad de Madrid has funded
21 awards under 18 distinct grant numbers, across 10 documents. A further 12 documents name it as a funder without a grant number — a weaker claim, counted separately.
Hybrid Training Strategies: Improving Performance of Temporal Difference Learning in Board Gamesgrant S2018/NMT-4331 · 2022-03-10Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selectiongrant B2017/BMD3773 · 2022-08-18Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selectiongrant PID2021-125549OB-I00 · 2022-08-18Predicting the Risk of Overweight and Obesity in Madrid—A Binary Classification Approach with Evolutionary Feature Selectiongrant RTI2018-095180-B-I00 · 2022-08-18Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant 101017385 · 2023-03-23Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant 2020-661 · 2023-03-23Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant H2020-EU.3.1.4.2. · 2023-03-23Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant HELSE-EU-project 269882 · 2023-03-23Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant MCIN/AEI/10.13039/501100011033 · 2023-03-23Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factorsgrant PID2019-106623RB-C41/AEI/ 10.13039/501100011033 · 2023-03-23
10 of 33 funding edges