Alberto Fernández Hilario: h-index, Total Citations, and Citation Map
Alberto Fernández Hilario's h-index is 52 (107 i10-index, 27,991+ total citations across 2+ publications) according to Google Scholar as of May 2026. Alberto Fernández Hilario is affiliated with Full Professor of Computer Science and Artificial Intelligence, University of Granada.
Alberto Fernández Hilario is a researcher affiliated with Full Professor of Computer Science and Artificial Intelligence, University of Granada, specializing in Trustworthy AI, XAI, Machine Learning. Their work has been cited 27,991 times. This profile visualizes their global influence, highlighting strong citation networks in Spain.
Alberto Fernández Hilario's Citation Metrics
Bibliometric impact based on 2 indexed publications. Of these, 1 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 52
- i10-Index
- 107
- Total Citations
- 27,991
- Citing Countries
- 16
As of May 2026.
Alberto Fernández Hilario has an h-index of 52 and 27,991 total citations across 2 publications, with research cited by institutions in 16 countries.
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Top Cited Works
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
20123,692
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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 the KEEL data-mining software tool, providing a unified framework for dataset repositories, algorithm integration, and experimental analysis in soft computing.
The researcher provided a seminal synthesis of ensemble methods for class imbalance, establishing a foundational framework for bagging, boosting, and hybrid approaches in machine learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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