Sergey Subbotin
Impact in
- Fuel Technology top 5%
- Metals and Alloys top 10%
Papers in
-
- Neural Networks and Applications 27
- Evolutionary Algorithms and Applications 23
- Fuzzy Logic and Control Systems 14
- Machine Learning and Data Classification 12
-
- Coal and Coke Industries Research 17
- Co-authors
- Аndrii Oliinyk (55 shared papers)David J. Hooper (1 shared paper)Johannes Hallmann (1 shared paper)Maurice Moens (4 shared papers)Sergei E. Spiridonov (2 shared papers)Elena Zaitseva (3 shared papers)Vitaly Levashenko (3 shared papers)Jan Rabčan (3 shared papers)
- Journals
- Journal of Nematology (6 papers)Optical Memory and Neural Networks (6 papers)Nematology (4 papers)Atomic Energy (22 papers)Engineering Structures (1 paper)
- Partner nations
- UkraineRussiaUnited States
In The Last Decade
Sergey Subbotin
192 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 111
- Fuel Technology 22
- Metals and Alloys 35
- Insect Science 131
- Plant Science 475
- Artificial Intelligence 366
Countries citing papers authored by Sergey Subbotin
This map shows the geographic impact of Sergey Subbotin'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 Sergey Subbotin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergey Subbotin more than expected).
Fields of papers citing papers by Sergey Subbotin
This network shows the impact of papers produced by Sergey Subbotin. 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 Sergey Subbotin. The network helps show where Sergey Subbotin may publish in the future.
Co-authors
The 25 scholars most cited alongside Sergey Subbotin, 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 234 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 274 | |
| 2 | 2003 | 56 | |
| 3 | 2015 | 51 | |
| 4 | 2013 | 46 | |
| 5 | 2019 | 40 | |
| 6 | 2001 | 37 | |
| 7 | 2019 | 34 | |
| 8 | 2018 | 31 | |
| 9 | 2016 | 28 | |
| 10 | 2014 | 25 | |
| 11 | 2020 | 25 | |
| 12 | 2013 | 23 | |
| 13 | 2005 | 22 | |
| 14 | 2013 | 22 | |
| 15 | 2015 | 22 | |
| 16 | 2017 | 22 | |
| 17 | 2019 | 21 | |
| 18 | 2016 | 19 | |
| 19 | 2015 | 18 | |
| 20 | 2016 | 18 |
About Sergey Subbotin
Sergey Subbotin is a scholar working on Artificial Intelligence, Fuel Technology, Aerospace Engineering, Metals and Alloys and Health Information Management, having authored 234 papers that have together received 1.7k indexed citations. Recurring topics across this work include Nuclear reactor physics and engineering (40 papers), Neural Networks and Applications (27 papers), Evolutionary Algorithms and Applications (23 papers), Nematode management and characterization studies (22 papers), Coal and Coke Industries Research (17 papers), Fuzzy Logic and Control Systems (14 papers), Artificial Intelligence in Healthcare (13 papers) and Machine Learning and Data Classification (12 papers). The work is most often cited by research in Fuel Technology (22 citations), Metals and Alloys (35 citations), Insect Science (131 citations), Plant Science (475 citations) and Artificial Intelligence (366 citations). Sergey Subbotin has collaborated with scholars based in Ukraine, Russia and United States. Frequent co-authors include Аndrii Oliinyk, David J. Hooper, Johannes Hallmann, Maurice Moens, Sergei E. Spiridonov, Elena Zaitseva, Vitaly Levashenko, Jan Rabčan, Miroslav Kvaššay and Pavel Nikolaevich Alekseev. Their work appears in journals such as Journal of Nematology, Optical Memory and Neural Networks, Nematology, Atomic Energy and Engineering Structures.
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.