Prospero C. Naval
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 2%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Water Science and Technology top 10%
- Co-authors
- Eduardo MendozaChristoph KüperOrland GonzalezKirsten JungDavid Earl HostalleroPilarita T. RiveraLaura DavidElena A. Villacorte
- Topics
- Digital Imaging for Blood Diseases (4 papers)Reinforcement Learning in Robotics (4 papers)Protein Structure and Dynamics (3 papers)
- Partner nations
- PhilippinesGermanyFrance
In The Last Decade
Prospero C. Naval
43 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 357
- Computational Theory and Mathematics 254
- Computer Vision and Pattern Recognition 171
- Molecular Biology 164
- Water Science and Technology 96
Countries citing papers authored by Prospero C. Naval
This map shows the geographic impact of Prospero C. Naval'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 Prospero C. Naval with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prospero C. Naval more than expected).
Fields of papers citing papers by Prospero C. Naval
This network shows the impact of papers produced by Prospero C. Naval. 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 Prospero C. Naval. The network helps show where Prospero C. Naval may publish in the future.
Co-authorship network of co-authors of Prospero C. Naval
This figure shows the co-authorship network connecting the top 25 collaborators of Prospero C. Naval. A scholar is included among the top collaborators of Prospero C. Naval 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 Prospero C. Naval. Prospero C. Naval is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 7 | |
| 7 | 21 | |
| 8 | 3 | |
| 9 | 9 | |
| 10 | 1 | |
| 11 | 8 | |
| 12 | 9 | |
| 13 | 27 | |
| 14 | 18 | |
| 15 | 21 | |
| 16 | 45 | |
| 17 | 3 | |
| 18 | 0 | |
| 19 | 9 | |
| 20 | 15 |
About Prospero C. Naval
Prospero C. Naval is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 46 papers that have together received 1.1k indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (4 papers), Reinforcement Learning in Robotics (4 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Computational Theory and Mathematics (254 citations), Artificial Intelligence (357 citations) and Neurology (76 citations). Prospero C. Naval has collaborated with scholars based in Philippines, Germany and France. Frequent co-authors include Eduardo Mendoza, Christoph Küper, Orland Gonzalez, Kirsten Jung, David Earl Hostallero, Pilarita T. Rivera, Laura David, Elena A. Villacorte, Helen Yap and Maricor Soriano. Their work appears in journals such as Bioinformatics, Conservation Biology and Agriculture Ecosystems & Environment.
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.