Jair Cervantes
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Information Systems top 5%
- Biomedical Engineering
- Co-authors
- Farid García‐LamontAsdrúbal López‐ChauLisbeth Rodríguez-MazahuaXiaoou LiWen YuKang LiGiner Alor‐HernándezJorge Luis García-Alcaráz
- Topics
- Face and Expression Recognition (12 papers)Imbalanced Data Classification Techniques (6 papers)Text and Document Classification Technologies (6 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceHealth Information Management
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEExpert Systems with Applications
- Partner nations
- MexicoUnited KingdomFrance
In The Last Decade
Jair Cervantes
45 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 183
- Artificial Intelligence 688
- Computer Vision and Pattern Recognition 458
- Electrical and Electronic Engineering 210
- Information Systems 200
- Biomedical Engineering 174
Countries citing papers authored by Jair Cervantes
This map shows the geographic impact of Jair Cervantes'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 Jair Cervantes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jair Cervantes more than expected).
Fields of papers citing papers by Jair Cervantes
This network shows the impact of papers produced by Jair Cervantes. 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 Jair Cervantes. The network helps show where Jair Cervantes may publish in the future.
Co-authorship network of co-authors of Jair Cervantes
This figure shows the co-authorship network connecting the top 25 collaborators of Jair Cervantes. A scholar is included among the top collaborators of Jair Cervantes 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 Jair Cervantes. Jair Cervantes 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 | 1 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 12 | |
| 9 | 19 | |
| 10 | 4 | |
| 11 | A comprehensive survey on support vector machine classification: Applications, challenges and trendsbreakdown → | 1405 |
| 12 | 2 | |
| 13 | 147 | |
| 14 | 8 | |
| 15 | 65 | |
| 16 | 12 | |
| 17 | 10 | |
| 18 | 1 | |
| 19 | 8 | |
| 20 | 10 |
About Jair Cervantes
Jair Cervantes is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 48 papers that have together received 2.3k indexed citations. Recurring topics across this work include Face and Expression Recognition (12 papers), Imbalanced Data Classification Techniques (6 papers) and Text and Document Classification Technologies (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (458 citations), Artificial Intelligence (688 citations) and Health Information Management (86 citations). Jair Cervantes has collaborated with scholars based in Mexico, United Kingdom and France. Frequent co-authors include Farid García‐Lamont, Asdrúbal López‐Chau, Lisbeth Rodríguez-Mazahua, Xiaoou Li, Wen Yu, Kang Li, Giner Alor‐Hernández, Jorge Luis García-Alcaráz, José Luis Sánchez-Cervantes and Isaac Machorro-Cano. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.
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.