Adriano C. M. Pereira: h-index, Total Citations, and Citation Map
Adriano C. M. Pereira's h-index is 32 (121 i10-index, 6,847+ total citations across 3+ publications) according to Google Scholar as of May 2026. Adriano C. M. Pereira is affiliated with Federal University of Minas Gerais (UFMG).
Adriano C. M. Pereira is a researcher affiliated with Federal University of Minas Gerais (UFMG), specializing in Quantitative Finance, Financial Computing, e-Commerce. Their work has been cited 6,847 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Adriano C. M. Pereira's Citation Metrics
Bibliometric impact based on 3 indexed publications. Of these, 2 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 32
- i10-Index
- 121
- Total Citations
- 6,847
- Citing Countries
- 20
As of May 2026.
Adriano C. M. Pereira has an h-index of 32 and 6,847 total citations across 3 publications, with research cited by institutions in 20 countries.
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Top Cited Works
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Stock market's price movement prediction with LSTM neural networks
20171,373
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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 pioneered the application of LSTM neural networks to predict stock market price movements, establishing a foundational reference point in computational finance.
The researcher advanced multilingual sentiment analysis by establishing a comparative framework for machine translation approaches, a contribution validated by widespread independent scholarly adoption.
The researcher established a foundational systematic review framework for applying artificial intelligence to injury risk assessment and performance prediction in team sports.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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