Francisco Herrera: h-index, Total Citations, and Citation Map
Francisco Herrera's h-index is 197 (861 i10-index, 190,986+ total citations across 5+ publications) according to Google Scholar as of May 2026. Francisco Herrera is affiliated with Professor Computer Science and AI, DaSCI Research Institute, Granada University, Spain.
Francisco Herrera is a researcher affiliated with Professor Computer Science and AI, DaSCI Research Institute, Granada University, Spain, specializing in Artificial Intelligence, Computational Intelligence, Data Science. Their work has been cited 190,986 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Francisco Herrera'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
- 197
- i10-Index
- 861
- Total Citations
- 190,986
- Citing Countries
- 33
As of May 2026.
Francisco Herrera has an h-index of 197 and 190,986 total citations across 5 publications, with research cited by institutions in 33 countries.
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We've mapped 5,000 of 190,986 citations for Francisco Herrera
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Global Impact Map
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Top Cited Works
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
202014,340
Top Citing Countries
Top Citing Institutions
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 a foundational framework for Explainable AI by providing a comprehensive taxonomy and analysis of concepts, opportunities, and challenges for responsible AI.
The researcher developed a 2-tuple fuzzy linguistic representation model for computing with words, establishing a foundational framework for handling linguistic information in fuzzy systems.
The researcher developed a seminal methodology for detecting, quantifying, and visualizing the evolution of research fields, establishing a foundational framework for bibliometric analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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