Roman Tkachenko
Impact in
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- Information Systems and Technology Applications
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- Artificial Intelligence in Healthcare
Papers in
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- Information Systems and Technology Applications 26
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- Statistical and Computational Modeling 15
- Neural Networks and Applications 5
- Co-authors
- Ivan Izonin (68 shared papers)Khrystyna Zub (11 shared papers)Natalia Kryvinska (10 shared papers)Nataliia Lotoshynska (6 shared papers)Павло Іванович Ткаченко (7 shared papers)Ivanna Dronyuk (6 shared papers)Nataliya Shakhovska (5 shared papers)Zoia Duriagina (10 shared papers)
In The Last Decade
Roman Tkachenko
80 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 146
- Management Information Systems 256
- Health Information Management 76
- Artificial Intelligence 481
- Industrial and Manufacturing Engineering 91
- Information Systems 197
Countries citing papers authored by Roman Tkachenko
This map shows the geographic impact of Roman Tkachenko'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 Tkachenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman Tkachenko more than expected).
Fields of papers citing papers by Roman Tkachenko
This network shows the impact of papers produced by Roman Tkachenko. 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 Tkachenko. The network helps show where Roman Tkachenko may publish in the future.
Co-authors
The 25 scholars most cited alongside Roman Tkachenko, 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 86 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 106 | |
| 2 | 2020 | 75 | |
| 3 | 2022 | 63 | |
| 4 | 2018 | 61 | |
| 5 | 2018 | 61 | |
| 6 | 2019 | 60 | |
| 7 | 2017 | 57 | |
| 8 | 2020 | 54 | |
| 9 | 2023 | 47 | |
| 10 | 2021 | 45 | |
| 11 | 2021 | 41 | |
| 12 | 2021 | 35 | |
| 13 | 2015 | 32 | |
| 14 | 2020 | 28 | |
| 15 | 2018 | 28 | |
| 16 | 2019 | 26 | |
| 17 | 2019 | 24 | |
| 18 | 2018 | 24 | |
| 19 | 2019 | 20 | |
| 20 | 2020 | 19 |
About Roman Tkachenko
Roman Tkachenko is a scholar working on Management Information Systems, Artificial Intelligence, Information Systems, Control and Systems Engineering and Industrial and Manufacturing Engineering, having authored 86 papers that have together received 1.4k indexed citations. Recurring topics across this work include Information Systems and Technology Applications (26 papers), Advanced Computational Techniques in Science and Engineering (22 papers), Advanced Data Processing Techniques (21 papers), Statistical and Computational Modeling (15 papers), Engineering Technology and Methodologies (13 papers), Advanced Scientific Research Methods (8 papers), Aerospace, Electronics, Mathematical Modeling (6 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Management Information Systems (256 citations), Health Information Management (76 citations), Artificial Intelligence (481 citations), Industrial and Manufacturing Engineering (91 citations) and Information Systems (197 citations). Roman Tkachenko has collaborated with scholars based in Ukraine, Slovakia and Austria. Frequent co-authors include Ivan Izonin, Khrystyna Zub, Natalia Kryvinska, Nataliia Lotoshynska, Павло Іванович Ткаченко, Ivanna Dronyuk, Nataliya Shakhovska, Zoia Duriagina, Michal Greguš and Michal Greguš. Their work appears in journals such as Scientific Reports, Applied Sciences, Sensors, Mathematical Biosciences & Engineering and Engineering With Computers.
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.