Sandi Baressi Šegota: h-index, Total Citations, and Citation Map
Sandi Baressi Šegota's h-index is 19 (38 i10-index, 1,651+ total citations across 153+ publications) according to Google Scholar as of May 2026. Sandi Baressi Šegota is affiliated with Faculty of Informatics, Juraj Dobrila University of Pula.
Sandi Baressi Šegota is a researcher affiliated with Faculty of Informatics, Juraj Dobrila University of Pula, specializing in Robotics, Artificial Intelligence, Machine Learning. Their work has been cited 1,651 times. This profile visualizes their global influence, highlighting strong citation networks in Croatia.
Sandi Baressi Šegota's Citation Metrics
Bibliometric impact based on 153 indexed publications.
- H-Index
- 19
- i10-Index
- 38
- Total Citations
- 1,651
- Citing Countries
- 42
As of May 2026.
Sandi Baressi Šegota has an h-index of 19 and 1,651 total citations across 153 publications, with research cited by institutions in 42 countries.
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Global Impact Map
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Top Cited Works
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Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron
2020283
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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 a multilayer perceptron model to simulate COVID-19 infection spread, establishing a computational framework that has garnered significant independent academic attention.
The researcher developed evolutionary algorithm-based path planning optimization methods for six-degree-of-freedom robotic manipulators, establishing a foundational approach widely adopted by independent scholars.
The researcher developed a hybrid forecasting model integrating stationary wavelet transform with bidirectional LSTM to address stock price volatility during the COVID-19 pandemic.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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