Daniel Molina Cabrera: h-index, Total Citations, and Citation Map
Daniel Molina Cabrera's h-index is 32 (55 i10-index, 28,248+ total citations across 4+ publications) according to Google Scholar as of May 2026. Daniel Molina Cabrera is affiliated with Computer Science, Granada University.
Daniel Molina Cabrera is a researcher affiliated with Computer Science, Granada University, specializing in Soft Computing, Evolutionary Algorithms, Computational Intelligence. Their work has been cited 28,248 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Daniel Molina Cabrera's Citation Metrics
Bibliometric impact based on 4 indexed publications.
- H-Index
- 32
- i10-Index
- 55
- Total Citations
- 28,248
- Citing Countries
- 24
As of May 2026.
Daniel Molina Cabrera has an h-index of 32 and 28,248 total citations across 4 publications, with research cited by institutions in 24 countries.
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We've mapped 5,000 of 28,248 citations for Daniel Molina Cabrera
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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
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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 established a rigorous non-parametric statistical framework for evaluating evolutionary algorithms, transforming how the community validates algorithmic performance through widely adopted methodological standards.
The researcher established a foundational framework for Explainable AI by defining its concepts, taxonomies, and challenges, creating a seminal reference point for responsible AI development.
The researcher provided a seminal assessment of bio-inspired computation, defining the field's current status and future directions in a highly cited 2019 publication.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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