Fedor Tsarev

922 total citations
16 papers, 127 citations indexed

About

Fedor Tsarev is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Fedor Tsarev has authored 16 papers receiving a total of 127 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 2 papers in Molecular Biology. Recurrent topics in Fedor Tsarev's work include Evolutionary Algorithms and Applications (7 papers), Formal Methods in Verification (6 papers) and Algorithms and Data Compression (4 papers). Fedor Tsarev is often cited by papers focused on Evolutionary Algorithms and Applications (7 papers), Formal Methods in Verification (6 papers) and Algorithms and Data Compression (4 papers). Fedor Tsarev collaborates with scholars based in Russia, Poland and Spain. Fedor Tsarev's co-authors include Vladimir Ulyantsev, Anatoly Shalyto, С. В. Казаков, Francesc Xavier Muñoz, Natalia Abramova, Dmitry Kirsanov, Alexey Sergushichev, Andrey Bratov, Juan M. Ribo and M.C. Riva and has published in prestigious journals such as Sensors and Actuators B Chemical, BMC Bioinformatics and Journal of Computer and Systems Sciences International.

In The Last Decade

Fedor Tsarev

14 papers receiving 115 citations

Peers

Fedor Tsarev
Andreas Bauer Germany
Nima Roohi United States
Guillaume Gardey France
Richard Lassaigne France
Karl‐Heinz Pennemann Germany
Konstantin Korovin United Kingdom
Benedikt Bollig France
Shankara Narayanan Krishna India
Gitanjali Swamy United States
Richard Trefler Canada
Andreas Bauer Germany
Fedor Tsarev
Citations per year, relative to Fedor Tsarev Fedor Tsarev (= 1×) peers Andreas Bauer

Countries citing papers authored by Fedor Tsarev

Since Specialization
Citations

This map shows the geographic impact of Fedor Tsarev'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 Fedor Tsarev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fedor Tsarev more than expected).

Fields of papers citing papers by Fedor Tsarev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fedor Tsarev. 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 Fedor Tsarev. The network helps show where Fedor Tsarev may publish in the future.

Co-authorship network of co-authors of Fedor Tsarev

This figure shows the co-authorship network connecting the top 25 collaborators of Fedor Tsarev. A scholar is included among the top collaborators of Fedor Tsarev based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fedor Tsarev. Fedor Tsarev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Казаков, С. В., et al.. (2015). Modification of the method of generation of control finite-state machines with continuous actions based on training examples. Journal of Computer and Systems Sciences International. 54(6). 853–865. 4 indexed citations
2.
Shalyto, Anatoly, et al.. (2014). Application of genetic programming and model checking for finite-state machine induction. SPIIRAS Proceedings. 4(15). 123–123.
3.
Kirsanov, Dmitry, Fedor Tsarev, Natalia Abramova, et al.. (2014). Water pollution monitoring by an artificial sensory system performing in terms of Vibrio fischeri bacteria. Sensors and Actuators B Chemical. 207. 1069–1075. 28 indexed citations
4.
Ulyantsev, Vladimir, et al.. (2014). Improving the quality of supervised finite-state machine construction using real-valued variables. 1037–1040. 2 indexed citations
5.
Sergushichev, Alexey, Anton Alexandrov, С. В. Казаков, Fedor Tsarev, & Anatoly Shalyto. (2013). Combining De Bruijn Graphs, Overlap Graphs and Microassembly for De Novo Genome Assembly. Izvestiya of Saratov University Mathematics Mechanics Informatics. 13(2(2)). 51–57. 3 indexed citations
6.
Tsarev, Fedor, et al.. (2013). De Bruijn Superwalk with Multiplicities Problem is NP-hard. BMC Bioinformatics. 14(S5). S7–S7. 9 indexed citations
7.
Ulyantsev, Vladimir, et al.. (2013). Search-based construction of finite-state machines with real-valued actions. 199–200. 4 indexed citations
8.
Buzdalov, Maxim & Fedor Tsarev. (2013). An Evolutionary Approach to Hard Test Case Generation for Shortest Common Superstring Problem. 81–85. 1 indexed citations
9.
Казаков, С. В., et al.. (2013). The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior. Journal of Computer and Systems Sciences International. 52(3). 410–425. 6 indexed citations
10.
11.
Ulyantsev, Vladimir & Fedor Tsarev. (2012). Extended Finite-State Machine Induction using SAT-Solver. IFAC Proceedings Volumes. 45(6). 236–241. 10 indexed citations
12.
Ulyantsev, Vladimir & Fedor Tsarev. (2011). Extended Finite-State Machine Induction Using SAT-Solver. 346–349. 24 indexed citations
13.
Alexandrov, Anton, Alexey Sergushichev, С. В. Казаков, & Fedor Tsarev. (2011). Genetic algorithm for induction of finite automata with continuous and discrete output actions. 775–778. 4 indexed citations
14.
Tsarev, Fedor, et al.. (2011). Finite state machine induction using genetic algorithm based on testing and model checking. 759–762. 25 indexed citations
15.
16.
Shalyto, Anatoly, et al.. (2006). Modeling Technology for One Class of Multi-Agent Systems with Automata Based Programming. 1. 15–20. 3 indexed citations

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.

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