Adriano Veloso: h-index, Total Citations, and Citation Map
Adriano Veloso's h-index is 38 (81 i10-index, 5,570+ total citations across 4+ publications) according to Google Scholar as of May 2026. Adriano Veloso is affiliated with Associate Professor of Computer Science, Universidade Federal de Minas Gerais.
Adriano Veloso is a researcher affiliated with Associate Professor of Computer Science, Universidade Federal de Minas Gerais, specializing in Machine Learning, Natural Language Processing. Their work has been cited 5,570 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Adriano Veloso's Citation Metrics
Bibliometric impact based on 4 indexed publications.
- H-Index
- 38
- i10-Index
- 81
- Total Citations
- 5,570
- Citing Countries
- 12
As of May 2026.
Adriano Veloso has an h-index of 38 and 5,570 total citations across 4 publications, with research cited by institutions in 12 countries.
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Global Impact Map
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Top Cited Works
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Supervised learning for fake news detection
2019693
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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 advanced associative classification by introducing a lazy learning approach, a seminal contribution that has garnered significant independent scholarly attention.
The researcher established a foundational supervised learning framework for fake news detection, a seminal contribution that has garnered significant independent scholarly attention.
The researcher developed a transfer-learning framework for real-time sentiment analysis, bridging the gap between bias detection and opinion extraction in high-velocity data streams.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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