Roman Rybka
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Authorship Attribution and Profiling
- Hate Speech and Cyberbullying Detection
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- Neuroscience and Neural Engineering
Papers in
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- Topic Modeling 18
- Advanced Text Analysis Techniques 9
- Authorship Attribution and Profiling 8
- Natural Language Processing Techniques 7
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- Advanced Memory and Neural Computing 20
- Co-authors
- Alexander Sboev (49 shared papers)Tatiana Litvinova (5 shared papers)В. А. Демин (5 shared papers)Nikolay A. Kudryashov (2 shared papers)А. В. Ситников (1 shared paper)V. V. Rylkov (1 shared paper)П. К. Кашкаров (1 shared paper)M. V. Kovalchuk (1 shared paper)
In The Last Decade
Roman Rybka
50 papers receiving 334 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 166
- Cellular and Molecular Neuroscience 89
- Cognitive Neuroscience 85
- Electrical and Electronic Engineering 167
- Statistical and Nonlinear Physics 29
Countries citing papers authored by Roman Rybka
This map shows the geographic impact of Roman Rybka's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Roman Rybka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Rybka more than expected).
Fields of papers citing papers by Roman Rybka
This network shows the impact of papers produced by Roman Rybka. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Roman Rybka. The network helps show where Roman Rybka may publish in the future.
Co-authors
The 16 scholars most cited alongside Roman Rybka, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 68 | |
| 2 | 2016 | 38 | |
| 3 | 2017 | 25 | |
| 4 | 2020 | 19 | |
| 5 | 2023 | 17 | |
| 6 | 2018 | 15 | |
| 7 | 2018 | 14 | |
| 8 | 2021 | 12 | |
| 9 | 2022 | 10 | |
| 10 | 2016 | 10 | |
| 11 | 2018 | 9 | |
| 12 | 2015 | 8 | |
| 13 | 2018 | 7 | |
| 14 | 2022 | 6 | |
| 15 | Gender Prediction for Authors of Russian Texts Using Regression And Classification Techniques. | 2016 | 6 |
| 16 | 2016 | 5 | |
| 17 | 2020 | 5 | |
| 18 | 2021 | 5 | |
| 19 | 2016 | 5 | |
| 20 | 2018 | 5 |
About Roman Rybka
Roman Rybka is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Cognitive Neuroscience, Cellular and Molecular Neuroscience and Molecular Biology, having authored 55 papers that have together received 345 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (20 papers), Neural dynamics and brain function (18 papers), Topic Modeling (18 papers), Neuroscience and Neural Engineering (10 papers), Advanced Text Analysis Techniques (9 papers), Authorship Attribution and Profiling (8 papers), Biomedical Text Mining and Ontologies (8 papers) and Natural Language Processing Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (166 citations), Cellular and Molecular Neuroscience (89 citations), Cognitive Neuroscience (85 citations), Electrical and Electronic Engineering (167 citations) and Statistical and Nonlinear Physics (29 citations). Roman Rybka has collaborated with scholars based in Russia, China and Taiwan. Frequent co-authors include Alexander Sboev, Tatiana Litvinova, В. А. Демин, Nikolay A. Kudryashov, А. В. Ситников, V. V. Rylkov, П. К. Кашкаров, M. V. Kovalchuk, A. V. Emelyanov and K. E. Nikiruy. Their work appears in journals such as Big Data and Cognitive Computing, Neural Networks, Mathematical Methods in the Applied Sciences, Applied Mathematics Letters and Applied Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.