Salvador García: h-index, Total Citations, and Citation Map
Salvador García's h-index is 68 (171 i10-index, 60,629+ total citations across 3+ publications) according to Google Scholar as of May 2026. Salvador García is affiliated with Full Professor of Computer Science and Artificial Intelligence. University of Granada..
Salvador García is a researcher affiliated with Full Professor of Computer Science and Artificial Intelligence. University of Granada., specializing in Artificial Intelligence, Data Science, Machine Learning. Their work has been cited 60,629 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Salvador García's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 68
- i10-Index
- 171
- Total Citations
- 60,629
- Citing Countries
- 26
As of May 2026.
Salvador García has an h-index of 68 and 60,629 total citations across 3 publications, with research cited by institutions in 26 countries.
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Top Cited Works
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
202014,302
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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 developed KEEL, a comprehensive data-mining software tool featuring a dataset repository, algorithm integration, and experimental analysis framework, establishing a standardized benchmark for the field.
The researcher established a foundational framework for Explainable AI by defining its concepts, taxonomies, and challenges, creating a widely adopted reference for responsible AI development.
The researcher established a standardized methodological framework for applying nonparametric statistical tests to rigorously compare evolutionary and swarm intelligence algorithms.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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