Oscar Cordón: h-index, Total Citations, and Citation Map
Oscar Cordón's h-index is 67 (227 i10-index, 18,581+ total citations across 5+ publications) according to Google Scholar as of May 2026. Oscar Cordón is affiliated with Professor, IEEE-IFSA Fellow, Natl. Computer Science Award. DaSCI Research Institute & DECSAI, UGR.
Oscar Cordón is a researcher affiliated with Professor, IEEE-IFSA Fellow, Natl. Computer Science Award. DaSCI Research Institute & DECSAI, UGR, specializing in Artificial Intelligence, Computational Intelligence, Social Network Analysis. Their work has been cited 18,581 times. This profile visualizes their global influence, highlighting strong citation networks in Spain.
Oscar Cordón's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 67
- i10-Index
- 227
- Total Citations
- 18,581
- Citing Countries
- 19
As of May 2026.
Oscar Cordón has an h-index of 67 and 18,581 total citations across 5 publications, with research cited by institutions in 19 countries.
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We've mapped 5,000 of 18,581 citations for Oscar Cordón
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Top Cited Works
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Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases
20011,597
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher established the foundational framework for genetic fuzzy systems and subsequently advanced the field by reviewing evolutionary learning methods for interpretable Mamdani-type systems.
The researcher established a foundational framework for evolutionary tuning of fuzzy knowledge bases, a seminal contribution that has garnered nearly 1,600 citations and broad independent adoption.
The researcher proposed foundational reasoning methods for fuzzy rule-based classification systems, establishing a seminal framework that has been widely adopted by independent scholars.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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