Fernando E. Casado

596 total citations
10 papers, 173 citations indexed

About

Fernando E. Casado is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Fernando E. Casado has authored 10 papers receiving a total of 173 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 4 papers in Computer Science Applications. Recurrent topics in Fernando E. Casado's work include Privacy-Preserving Technologies in Data (4 papers), Mobile Crowdsensing and Crowdsourcing (4 papers) and Indoor and Outdoor Localization Technologies (2 papers). Fernando E. Casado is often cited by papers focused on Privacy-Preserving Technologies in Data (4 papers), Mobile Crowdsensing and Crowdsourcing (4 papers) and Indoor and Outdoor Localization Technologies (2 papers). Fernando E. Casado collaborates with scholars based in Spain and United Kingdom. Fernando E. Casado's co-authors include Roberto Iglesias, Carlos V. Regueiro, Senén Barro, Germán Rodríguez, A. Santana‐Alonso, Yiannis Demiris and Xosé M. Pardo and has published in prestigious journals such as Sensors, Machine Learning and Information Fusion.

In The Last Decade

Fernando E. Casado

9 papers receiving 163 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fernando E. Casado Spain	7	115	34	33	24	22	10	173
Zhe Qu China	8	211 1.8×	21 0.6×	90 2.7×	25 1.0×	42 1.9×	23	293
Huan Feng China	7	46 0.4×	24 0.7×	25 0.8×	20 0.8×	20 0.9×	25	134
Yuanfeng Song Hong Kong	8	121 1.1×	31 0.9×	13 0.4×	10 0.4×	27 1.2×	47	185
Ayush K Tarun India	4	108 0.9×	38 1.1×	16 0.5×	13 0.5×	12 0.5×	6	178
Diego Pizzocaro United Kingdom	9	85 0.7×	14 0.4×	67 2.0×	11 0.5×	12 0.5×	21	186
Xiaoming Huang China	5	288 2.5×	15 0.4×	42 1.3×	25 1.0×	28 1.3×	13	323
Marcin Gabryel Poland	5	59 0.5×	41 1.2×	18 0.5×	4 0.2×	12 0.5×	13	148
Kevin J Liang United States	7	139 1.2×	93 2.7×	9 0.3×	8 0.3×	8 0.4×	15	209
Cise Midoglu Norway	10	62 0.5×	128 3.8×	82 2.5×	14 0.6×	78 3.5×	46	272
Tuong Do United Kingdom	6	125 1.1×	105 3.1×	18 0.5×	4 0.2×	28 1.3×	16	202

Countries citing papers authored by Fernando E. Casado

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Fernando E. Casado

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando E. Casado. A scholar is included among the top collaborators of Fernando E. Casado based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fernando E. Casado. Fernando E. Casado is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Casado, Fernando E., et al.. (2025). An Integrated 3D Eye-Gaze Tracking Framework for Assessing Trust in Human–Robot Interaction. ACM Transactions on Human-Robot Interaction. 14(3). 1–28.

2.

Casado, Fernando E., et al.. (2024). On the Effect of Augmented-Reality Multi-User Interfaces and Shared Mental Models on Human-Robot Trust. 1316–1322. 1 indexed citations

3.

Casado, Fernando E., et al.. (2023). Ensemble and continual federated learning for classification tasks. Machine Learning. 112(9). 3413–3453. 8 indexed citations

4.

Casado, Fernando E., et al.. (2022). Non-IID data and Continual Learning processes in Federated Learning: A long road ahead. Information Fusion. 88. 263–280. 71 indexed citations

5.

Casado, Fernando E. & Yiannis Demiris. (2022). Federated Learning from Demonstration for Active Assistance to Smart Wheelchair Users. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 9326–9331. 4 indexed citations

6.

Casado, Fernando E., et al.. (2021). Concept drift detection and adaptation for federated and continual learning. Multimedia Tools and Applications. 81(3). 3397–3419. 42 indexed citations

7.

Casado, Fernando E., et al.. (2020). Walking Recognition in Mobile Devices. Sensors. 20(4). 1189–1189. 14 indexed citations

8.

Pardo, Xosé M., et al.. (2019). Dataset bias exposed in face verification. IET Biometrics. 8(4). 249–258. 6 indexed citations

9.

Rodríguez, Germán, et al.. (2018). Robust Step Counting for Inertial Navigation with Mobile Phones. Sensors. 18(9). 3157–3157. 11 indexed citations

10.

Casado, Fernando E., et al.. (2017). Pose Estimation and Object Tracking Using 2D Images. Procedia Manufacturing. 11. 63–71. 16 indexed citations

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.

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