Ricardo Jardim‐Gonçalves
Biographic Data
| ID | 6783583 |
|---|---|
| NAME | Ricardo Jardim‐Gonçalves |
| GIVEN NAMES | Ricardo |
| FAMILY NAME | Jardim‐Gonçalves |
| SIGNATURE | GONÇALVES R J |
| AFFILIATIONS | Universidade Nova de Lisboa |
| ORCID | 0000-0002-3703-6854 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Vessel Traffic Density Prediction
Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density…
Recent evolution of the degree of integration on vertically integrated organizations
The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 2.913 (2021 JCR, received in June 2022)The IJIC 20th Anniversary Issue was published in 2021
No prominent works on this page.
Recent evolution of the degree of integration on vertically integrated organizations
The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 2.913 (2021 JCR, received in June 2022)The IJIC 20th Anniversary Issue was published in 2021
Vessel Traffic Density Prediction
Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density…
Artificial neural network (1 work) · Computer Science (1 work) · Convolutional neural network (1 work) · Data modeling (1 work) · Deep learning (1 work) · Federated learning (1 work) · Field (mathematics (1 work) · Library science (1 work) · Maritime Navigation and Safety (1 work) · Maritime Ports and Logistics (1 work)