Fernando E. Casado
Impact in
- Computer Science Applications top 10%
- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Data Stream Mining Techniques
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Internet Traffic Analysis and Secure E-voting
Papers in
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- Context-Aware Activity Recognition Systems 2
-
- Privacy-Preserving Technologies in Data 4
- Domain Adaptation and Few-Shot Learning 2
- Data Stream Mining Techniques 2
- Co-authors
- Roberto Iglesias (6 shared papers)Carlos V. Regueiro (6 shared papers)Senén Barro (4 shared papers)Germán Rodríguez (2 shared papers)A. Santana‐Alonso (1 shared paper)Yiannis Demiris (3 shared papers)Xosé M. Pardo (1 shared paper)
- Journals
- Sensors (2 papers)Multimedia Tools and Applications (1 paper)Machine Learning (1 paper)Information Fusion (1 paper)IET Biometrics (1 paper)
- Partner nations
- SpainUnited Kingdom
In The Last Decade
Fernando E. Casado
10 papers receiving 172 citations
Peers
Comparison fields: 5 of 47
- Computer Science Applications 23
- Artificial Intelligence 116
- Computer Vision and Pattern Recognition 35
- Computer Networks and Communications 33
- Signal Processing 12
Countries citing papers authored by Fernando E. Casado
This map shows the geographic impact of Fernando E. Casado's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fernando E. Casado with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando E. Casado more than expected).
Fields of papers citing papers by Fernando E. Casado
This network shows the impact of papers produced by Fernando E. Casado. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fernando E. Casado. The network helps show where Fernando E. Casado may publish in the future.
Co-authors
The 7 scholars most cited alongside Fernando E. Casado, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 74 | |
| 2 | 2021 | 43 | |
| 3 | 2017 | 16 | |
| 4 | 2020 | 14 | |
| 5 | 2018 | 11 | |
| 6 | 2023 | 9 | |
| 7 | 2019 | 6 | |
| 8 | 2022 | 4 | |
| 9 | 2024 | 1 | |
| 10 | 2025 | 1 |
About Fernando E. Casado
Fernando E. Casado is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Science Applications, Social Psychology and Electrical and Electronic Engineering, having authored 10 papers that have together received 179 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Mobile Crowdsensing and Crowdsourcing (4 papers), Domain Adaptation and Few-Shot Learning (2 papers), Context-Aware Activity Recognition Systems (2 papers), Indoor and Outdoor Localization Technologies (2 papers), Human-Automation Interaction and Safety (2 papers), Data Stream Mining Techniques (2 papers) and Gait Recognition and Analysis (2 papers). The work is most often cited by research in Computer Science Applications (23 citations), Artificial Intelligence (116 citations), Computer Vision and Pattern Recognition (35 citations), Computer Networks and Communications (33 citations) and Signal Processing (12 citations). Fernando E. Casado has collaborated with scholars based in Spain and United Kingdom. Frequent co-authors include Roberto Iglesias, Carlos V. Regueiro, Senén Barro, Germán Rodríguez, A. Santana‐Alonso, Yiannis Demiris and Xosé M. Pardo. Their work appears in journals such as Sensors, Multimedia Tools and Applications, Machine Learning, Information Fusion and IET Biometrics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.