Mikhail Babenko

1.3k total citations
98 papers, 741 citations indexed

About

Mikhail Babenko is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Mikhail Babenko has authored 98 papers receiving a total of 741 indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Information Systems, 55 papers in Artificial Intelligence and 38 papers in Computer Networks and Communications. Recurrent topics in Mikhail Babenko's work include Cryptography and Residue Arithmetic (49 papers), Cryptography and Data Security (40 papers) and Advanced Data Storage Technologies (24 papers). Mikhail Babenko is often cited by papers focused on Cryptography and Residue Arithmetic (49 papers), Cryptography and Data Security (40 papers) and Advanced Data Storage Technologies (24 papers). Mikhail Babenko collaborates with scholars based in Russia, Mexico and France. Mikhail Babenko's co-authors include Andrei Tchernykh, N.I. Chervyakov, Vanessa Miranda-López, Arutyun Avetisyan, Jorge M. Cortés-Mendoza, Maxim Deryabin, El‐Ghazali Talbi, Pavel Lyakhov, Gleb Radchenko and Raúl Rivera-Rodríguez and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Access.

In The Last Decade

Mikhail Babenko

88 papers receiving 697 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mikhail Babenko Russia 13 469 408 299 146 81 98 741
Alzubair Hassan China 15 447 1.0× 390 1.0× 325 1.1× 75 0.5× 34 0.4× 34 804
Yong Yu China 15 605 1.3× 602 1.5× 265 0.9× 103 0.7× 22 0.3× 44 927
A. Kumaravel India 11 176 0.4× 245 0.6× 300 1.0× 93 0.6× 37 0.5× 51 824
Pascal Lafourcade France 11 172 0.4× 215 0.5× 207 0.7× 64 0.4× 69 0.9× 61 513
Devesh C. Jinwala India 14 265 0.6× 363 0.9× 528 1.8× 108 0.7× 37 0.5× 92 969
Apostolos P. Fournaris Greece 12 330 0.7× 429 1.1× 115 0.4× 88 0.6× 40 0.5× 86 663
Maxime Cordy Luxembourg 15 317 0.7× 524 1.3× 137 0.5× 46 0.3× 50 0.6× 80 847
Tomáš Bureš Czechia 16 374 0.8× 543 1.3× 445 1.5× 44 0.3× 64 0.8× 106 877
Patrizia Scandurra Italy 15 348 0.7× 471 1.2× 354 1.2× 54 0.4× 89 1.1× 91 933
Pericle Perazzo Italy 14 198 0.4× 219 0.5× 264 0.9× 55 0.4× 39 0.5× 37 599

Countries citing papers authored by Mikhail Babenko

Since Specialization
Citations

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

Fields of papers citing papers by Mikhail Babenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mikhail Babenko

This figure shows the co-authorship network connecting the top 25 collaborators of Mikhail Babenko. A scholar is included among the top collaborators of Mikhail Babenko 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 Mikhail Babenko. Mikhail Babenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Emon, Md Mehedi Hasan, et al.. (2025). An Integrated Architecture for Colorectal Polyp Segmentation: The µ-Net Framework with Explainable AI. Diagnostics. 15(22). 2890–2890. 1 indexed citations
2.
Babenko, Mikhail, et al.. (2024). Transverse-layer partitioning of artificial neural networks for image classification. Computer Optics. 48(2). 312–320. 1 indexed citations
3.
Ermakova, Tatiana, et al.. (2024). Reliablity and Security for Fog Computing Systems. Information. 15(6). 317–317. 1 indexed citations
4.
Babenko, Mikhail, et al.. (2023). Investigation of Adversarial Attacks on Pattern Recognition Neural Networks. Proceedings of the Institute for System Programming of RAS. 35(2). 35–48. 1 indexed citations
5.
Babenko, Mikhail, et al.. (2023). Optimization of a Number Division Algorithm in the Residue Number System Based on the Akushsky Core Function. Proceedings of the Institute for System Programming of RAS. 35(5). 157–168.
6.
Babenko, Mikhail, et al.. (2023). Algorithm for Determining the Optimal Weights for the Akushsky Core Function with an Approximate Rank. Applied Sciences. 13(18). 10495–10495. 1 indexed citations
7.
Valuev, Georgii, Maria Valueva, Mikhail Babenko, & Andrei Tchernykh. (2023). Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System. IEEE Access. 11. 26807–26819.
8.
Babenko, Mikhail, Andrei Tchernykh, Arutyun Avetisyan, et al.. (2022). Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control. Mathematics. 10(12). 2006–2006. 4 indexed citations
9.
Babenko, Mikhail, et al.. (2022). Modified Error Detection and Localization in the Residue Number System. Proceedings of the Institute for System Programming of RAS. 34(3). 75–88.
10.
Tchernykh, Andrei, et al.. (2022). Dynamic performance–Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds. PLoS ONE. 17(1). e0261856–e0261856. 5 indexed citations
11.
Babenko, Mikhail, S.J. Piestrak, N.I. Chervyakov, & Maxim Deryabin. (2021). The Study of Monotonic Core Functions and Their Use to Build RNS Number Comparators. Electronics. 10(9). 1041–1041. 1 indexed citations
12.
Babenko, Mikhail, et al.. (2021). Advanced supervised learning in multi-layer perceptrons to the recognition tasks based on correlation indicator. Proceedings of the Institute for System Programming of RAS. 33(1). 33–46. 1 indexed citations
13.
Babenko, Mikhail, et al.. (2020). Neural network method for base extension in residue number system. 2 indexed citations
14.
Babenko, Mikhail, et al.. (2020). Computationally secure threshold secret sharing scheme with minimal redundancy. 3 indexed citations
15.
Babenko, Mikhail, et al.. (2019). Efficient Number Comparison in the Residue Number System based on Positional Characteristics. Proceedings of the Institute for System Programming of RAS. 31(2). 187–202. 2 indexed citations
16.
Babenko, Mikhail, et al.. (2019). Efficient Number Comparison in the Residue Number System based on Positional Characteristics. SHILAP Revista de lepidopterología. 31(2). 187–202. 5 indexed citations
17.
Chervyakov, N.I., et al.. (2018). Reliability Improvement of Information Systems by Residue Number System Code. 9(1). 81–84. 3 indexed citations
18.
Deryabin, Maxim, et al.. (2018). High Performance Parallel Computing in Residue Number System. 9(1). 62–67. 5 indexed citations
19.
20.
Chervyakov, N.I., Amir Sabbagh Molahosseini, Pavel Lyakhov, Mikhail Babenko, & Maxim Deryabin. (2016). Residue-to-binary conversion for general moduli sets based on approximate Chinese remainder theorem. International Journal of Computer Mathematics. 94(9). 1833–1849. 37 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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