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Publications in Math-Net.Ru |
Citations |
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2025 |
| 1. |
E. S. Bezuglova, E. M. Shiriaev, N. N. Kucherov, M. G. Babenko, “Modification of the Smith-Waterman algorithm for local alignment of genetic sequences based on the window method”, Proceedings of ISP RAS, 37:5 (2025), 183–194  |
| 2. |
V. V. Lutsenko, M. G. Babenko, “Generating compact residue number systems bases”, Proceedings of ISP RAS, 37:5 (2025), 43–52 |
| 3. |
M. A. Lapina, N. V. Podruchny, M. A. Rusanov, M. G. Babenko, “Research of machine learning methods for detecting network attacks”, Proceedings of ISP RAS, 37:4(2) (2025), 147–174 |
| 4. |
V. V. Lutsenko, D. E. Gorlachev, N. M. Mirny, M. G. Babenko, “Searching of optimal weights for the Akushsky core function”, Vestn. YuUrGU. Ser. Vych. Matem. Inform., 14:2 (2025), 26–41 |
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2024 |
| 5. |
N. A. Vershkov, M. G. Babenko, N. N. Kuchukova, V. A. Kuchukov, N. N. Kucherov, “Transverse-layer partitioning of artificial neural networks
for image classification”, Computer Optics, 48:2 (2024), 312–320 |
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| 6. |
V. V. Lutsenko, M. G. Babenko, M. M. Khamidov, “High speed method of conversion numbers from residue number system to positional notation”, Proceedings of ISP RAS, 36:4 (2024), 117–132 |
| 7. |
M. A. Lapina, V. V. Movzalevskaya, M. E. Tokmakova, M. G. Babenko, V. P. Kochyn, “Intelligent algorithms for detecting attacks in the web environment”, Proceedings of ISP RAS, 36:4 (2024), 99–116 |
| 8. |
N. A. Vershkov, M. G. Babenko, V. V. Lutsenko, N. N. Kuchukova, “Creating distributed artificial neural networks based on orthogonal transformations”, Proceedings of ISP RAS, 36:4 (2024), 57–68 |
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2023 |
| 9. |
V. V. Lutsenko, M. G. Babenko, A. N. Tchernykh, M. A. Lapina, “Optimization of a number division algorithm in the residue number system based on the Akushsky core function”, Proceedings of ISP RAS, 35:5 (2023), 157–168 |
| 10. |
D. V. Kotlyarov, G. D. Dyudyun, N. V. Rzhevskaya, M. A. Lapina, M. G. Babenko, “Investigation of adversarial attacks on pattern recognition neural networks”, Proceedings of ISP RAS, 35:2 (2023), 35–48 |
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2022 |
| 11. |
A. V. Gladkov, V. A. Kuchukov, M. G. Babenko, A. N. Tchernykh, V. V. Berezhnoi, A. Yu. Drozdov, “Modified Error Detection and Localization in the Residue Number System”, Proceedings of ISP RAS, 34:3 (2022), 75–88 |
| 12. |
M. V. Valueva, G. V. Valuev, M. G. Babenko, A. N. Tchernykh, J. M. Cortés-Mendoza, “Method for convolutional neural network hardware implementation based on a residue number system”, Proceedings of ISP RAS, 34:3 (2022), 61–74 |
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2021 |
| 13. |
N. A. Vershkov, M. G. Babenko, V. A. Kuchukov, N. N. Kuchukova, “Advanced supervised learning in multi-layer perceptrons to the recognition tasks based on correlation indicator”, Proceedings of ISP RAS, 33:1 (2021), 33–46 |
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2020 |
| 14. |
M. G. Babenko, E. I. Golimblevskaia, E. M. Shiriaev, “Comparative analysis of homomorphic encryption algorithms based on learning with errors”, Proceedings of ISP RAS, 32:2 (2020), 37–51 |
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2019 |
| 15. |
M. G. Babenko, A. N. Tchernykh, N. I. Chervyakov, V. A. Kuchukov, V. Miranda-López, R. Rivera-Rodriguez, Z. Du, “Efficient number comparison in the residue number system based on positional characteristics”, Proceedings of ISP RAS, 31:2 (2019), 187–202  |
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| 16. |
N. I. Chervyakov, M. A. Deryabin, A. S. Nazarov, M. G. Babenko, N. N. Kucherov, A. V. Gladkov, G. I. Radchenko, “Secure and reliable data transmission over MANET based on principles of computationally secure secret sharing”, Proceedings of ISP RAS, 31:2 (2019), 153–170 |
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2014 |
| 17. |
N. I. Chervyakov, M. G. Babenko, P. A. Lyakhov, I. N. Lavrinenko, A. M. Lyagin, “Multiplication and division in the residue number system using galois fields gf(p)”, St. Petersburg Polytechnical University Journal. Computer Science. Telecommunication and Control Sys, 2014, no. 3(198), 65–76 |
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