Alexander Sboev

email: sag111@mail.ru

Employment

Fields of interest

• neural networks
• machine learning
• natural language processing
• modeling socio-economic processes
• spiking neural networks
• textual information analysis
• time series analysis
• automated diagnostics systems
• robotics

Education

Skills

• Mathematical and statistical methods: linear algebra, probability, statistics, optimization, numerical methods, etc.
• Deep learning: understanding of the working principles and experience in using various types of neural networks, such as convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN).
• Data modeling and analysis: Development and application of models for data analysis, trend identification, time series analysis, forecasting, outlier identification, etc: clustering, classification, regression and other methods.
• Textual information processing: methods for analyzing texts in natural language (Natural Language Processing, NLP), including topic modeling, topic modelling, sentiment analysis, data labelling.
• Experience in working with interfaces of robotic platforms using machine learning methods.
• Development and application of biologically-inspired spiking neural networks.

Awards

• 2003 Kurchatov award

Projects

All publications

• Total: 147
• Scopus: 64
• Web of Science: 29
• Q1: 8

Publications in English:

1. O. Y. Atkov, S. G. Gorokhova, A. G. Sboev, E. V. Generozov, E. V. Muraseyeva, S. Y. Moroshkina, N. N. Cherniy. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters // Journal of Cardiology. — Vol. 59. — No. 2. — pp. 190–194. — 2012. https://www.sciencedirect.com/science/article/pii/S0914508711002255 DOI: 10.1016/j.jjcc.2011.11.005 (Q1, Q2, WoS)
2. S. G. Gorokhova, A. G. Sboev, K. A. Kukin, R. B. Rybka, E. V. Muraseeva, O. Y. Atkov. The application of artificial neural networks in the diagnosis of coronary heart disease // Network Topologies: Types, Performance Impact and Advantages/Disadvantages. — pp. 45–77. — 2013.
3. A. Sboev, D. Gudovskikh, R. Rybka, I. Moloshnikov. A Quantitative Method of Text Emotiveness Evaluation on Base of the Psycholinguistic Markers Founded on Morphological Features // 4th International Young Scientist Conference on Computational Science. — pp. 307–316. — 2015. https://www.sciencedirect.com/science/article/pii/S1877050915033852 DOI: 10.1016/j.procs.2015.11.036 (WoS)
4. A. G. Sboev, R. Rybka, I. Moloshnikov, D. Gudovskih. Syntactic Analysis of the Sentences of the Russian Language Based on Neural Networks // 4th International Young Scientist Conference on Computational Science. — pp. 277–286. — 2015. https://www.sciencedirect.com/science/article/pii/S1877050915033827 DOI: 10.1016/j.procs.2015.11.033 (WoS)
5. I. A. Moloshnikov, A. G. Sboev, R. B. Rybka, D. V. Gydovskikh. An Algorithm of Finding Thematically Similar Documents with Creating Context-semantic Graph Based on Probabilistic-entropy Approach // 4th International Young Scientist Conference on Computational Science. — pp. 297–306. — 2015. https://www.sciencedirect.com/science/article/pii/S1877050915033840 DOI: 10.1016/j.procs.2015.11.035 (WoS)
6. K. Kukin, A. Sboev. Comparison of learning methods for spiking neural networks // Optical Memory and Neural Networks (Information Optics). — Vol. 24. — No. 2. — pp. 123–129. — 2015. https://link.springer.com/article/10.3103\%2FS1060992X15020095 DOI: 10.3103/S1060992X15020095 (Q2)
7. A. Sboev, D. Vlasov, A. Serenko, R. Rybka, I. Moloshnikov. To the question of learnability of a spiking neuron with spike-timing-dependent plasticity in case of complex input signals // 1st International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures. — pp. 205–211. — 2016. DOI: 10.1007/978-3-319-32554-5_26 (WoS)
8. A. Sboev, T. Litvinova, D. Gudovskikh, R. Rybka, I. Moloshnikov. Machine Learning Models of Text Categorization by Author Gender Using Topic-independent Features // 5th International Young Scientist Conference on Computational Science. — pp. 135–142. — 2016. https://www.sciencedirect.com/science/article/pii/S1877050916326849 DOI: 10.1016/j.procs.2016.11.017 (WoS)
9. A. Sboev, I. Moloshnikov, D. Gudovskikh, R. Rybka. Visualization of subtopics of the thematic document collection using the context-semantic graph // Proceedings of 2015 International Conference on Computational Science and Computational Intelligence. — pp. 47–52. — 2016. DOI: 10.1109/CSCI.2015.124 (WoS)
10. A. Sboev, T. Litvinova, O. Litvinova, O. Zagorovskaya, P. Seredin, O. Romanchenko. Ruspersonality: A Russian corpus for authorship profiling and deception detection // Proceedings of the International FRUCT Conference on Intelligence, Social Media and Web. — pp. 7584767:1–7. — 2016. DOI: 10.1109/FRUCT.2016.7584767 (WoS)
11. A. Sboev, T. Litvinova, D. Vlasov, A. Serenko, I. Moloshnikov. On the Applicability of Spiking Neural Network Models to Solve the Task of Recognizing Gender Hidden in Texts // 5th International Young Scientist Conference on Computational Science (YSC). — pp. 187–196. — 2016. https://www.sciencedirect.com/science/article/pii/S1877050916326904 DOI: 10.1016/j.procs.2016.11.023 (WoS)
12. R. Rybka, A. Sboev, I. Moloshnikov, D. Gudovskikh. Morpho-syntactic parsing based on neural networks and corpus data // Proceedings of Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference. — pp. 89–95. — 2016. DOI: 10.1109/AINL-ISMW-FRUCT.2015.7382975 (WoS)
13. A. Sboev, R. Rybka, I. Moloshnikov, D. Gudovskikh. The complex of neural networks and probabilistic methods for mathematical modeling of the syntactic structure of a sentence of natural language // International Conference on Computer Simulation in Physics and Beyond. — pp. 012011:1–6. — 2016. DOI: 10.1088/1742-6596/681/1/012011 (Q3, WoS)
14. T. Litvinova, P. Seredin, O. Litvinova, O. Zagorovskaya, A. Sboev, D. Gudovskih, I. Moloshnikov, R. Rybka. Gender prediction for authors of Russian texts using regression and classification techniques // 3rd International Workshop on Concept Discovery in Unstructured Data. — pp. 44–53. — 2016. http://ceur-ws.org/Vol-1625/paper5.pdf
15. A. Sboev, D. Vlasov, A. Serenko, R. Rybka, I. Moloshnikov. A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms // International Conference on Computer Simulation in Physics and Beyond. — pp. 012013:1–6. — 2016. DOI: 10.1088/1742-6596/681/1/012013 (Q3, WoS)
16. A. Sboev, S. Gorokhova, V. Pfaf, I. Moloshnikov, D. Gudovskikh, R. Rybka, A. Selivanov, A. Serenko. Evaluation of the cardiovascular risk in middle-aged workers: An Artificial neural networks-based approach // International Conference on Computational Science. — pp. 2418–2422. — 2016. https://www.sciencedirect.com/science/article/pii/S1877050916310316 DOI: 10.1016/j.procs.2016.05.540
17. I. A. Moloshnikov, A. G. Sboev, R. B. Rybka, D. V. Gydovskikh. A probabilistic-entropy approach of finding thematically similar documents with creating context-semantic graph for investigating evolution of society opinion // International Conference on Computer Simulation in Physics and Beyond. — pp. 012012:1–6. — 2016. https://doi.org/10.1088/1742-6596/681/1/012012 DOI: 10.1088/1742-6596/681/1/012012 (Q3, WoS)
18. A. Sboev, D. Vlasov, A. Serenko, R. Rybka, I. Moloshnikov. On the applicability of STDP-based learning mechanisms to spiking neuron network models // AIP Advances. — Vol. 6. — No. 11. — pp. 111305:1–9. — 2016. http://aip.scitation.org/doi/abs/10.1063/1.4967353 DOI: 10.1063/1.4967353 (Q1, Q4, WoS)
19. Alexandr Sboev, Roman Rybka, Dmitry Gudovskikh, Ivan Moloshnikov, Anton Selivanov, Igor Ivanov. Syntactic Model for Russian: Deep Learning Models with Dependency Parsing Scheme // 2016 International Conference on Computational Science and Computational Intelligence. — pp. 541–544. — 2017. DOI: 10.1109/csci.2016.0108 (WoS)
20. Aleksandr Sboev, Tatiana Litvinova, Irina Voronina, Dmitry Gudovskikh, Roman Rybka. Deep Learning Network Models to Categorize Texts According to Author's Gender and to Identify Text Sentiment // 2016 International Conference on Computational Science and Computational Intelligence. — pp. 1101–1106. — 2017. DOI: 10.1109/csci.2016.0210 (WoS)
21. Alexander Sboev, Roman Rybka, Alexey Serenko. On the effect of stabilizing mean firing rate of a neuron due to STDP // 6th International Young Scientist Conference on Computational Science. — pp. 166–173. — 2017. https://www.sciencedirect.com/science/article/pii/S1877050917323839 DOI: 10.1016/j.procs.2017.11.173 (WoS)
22. T. Litvinova, D. Gudovskikh, A. Sboev, P. Seredin, O. Litvinova, D. Pisarevskaya, P. Rosso. Author gender prediction in Russian social media texts // Supplementary Proceedings of the Sixth International Conference on Analysis of Images, Social Networks and Texts. — pp. 105–110. — 2017. http://ceur-ws.org/Vol-1975/paper12.pdf
23. A. G. Sboev, D. V. Gudovskikh, I. Ivanov, I. A. Moloshnikov, R. B. Rybka, I. Voronina. Research of a deep learning neural network effectiveness for a morphological parser of Russian language // Computational Linguistics and Intellectual Technologies. — pp. 234–244. — 2017. http://www.dialog-21.ru/media/3944/sboevagetal.pdf
24. A. Sboev, I. Moloshnikov, D. Gudovskikh, R. Rybka. A comparison of data driven models of solving the task of gender identification of author in Russian language texts for cases without and with the gender deception // 6th International Conference Problems of Mathematical Physics and Mathematical Modelling. — pp. 012046:1–7. — 2017. DOI: 10.1088/1742-6596/937/1/012046 (Q3, WoS)
25. A. G. Sboev, A. V. Serenko, D. S. Vlasov. Effective calculations on neuromorphic hardware based on spiking neural network approaches // Lobachevskii Journal of Mathematics. — Vol. 38. — No. 5. — pp. 964–966. — 2017. DOI: 10.1134/s1995080217050304 (Q3, WoS)
26. D. V. Gydovskikh, I. A. Moloshnikov, A. V. Naumov, R. B. Rybka, A. G. Sboev, A. A. Selivanov. A probabilistically entropic mechanism of topical clusterisation along with thematic annotation for evolution analysis of meaningful social information of internet sources // Lobachevskii Journal of Mathematics. — Vol. 38. — No. 5. — pp. 910–913. — 2017. DOI: 10.1134/s1995080217050134 (Q3, WoS)
27. Nikolay A. Kudryashov, Roman B. Rybka, Aleksander G. Sboev. Analytical properties of the perturbed FitzHugh-Nagumo model // Applied Mathematics Letters. — Vol. 76. — pp. 142–147. — 2018. https://www.sciencedirect.com/science/article/pii/S0893965917302720 DOI: 10.1016/j.aml.2017.08.013 (Q1, WoS)
28. Alexander Sboev, Danila Vlasov, Roman Rybka, Alexey Serenko. Solving a classification task by spiking neurons with STDP and temporal coding // 8th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA). — pp. 494–500. — 2018. https://www.sciencedirect.com/science/article/pii/S1877050918300760 DOI: 10.1016/j.procs.2018.01.075
29. Aleksandr Sboev, Alexey Serenko, Roman Rybka, Danila Vlasov, Andrey Filchenkov. Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm // Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. — pp. 488–494. — 2018. http://www.sciencedirect.com/science/article/pii/S1877050918323998 DOI: 10.1016/j.procs.2018.11.111
30. Alexander Sboev, Roman Rybka, Alexey Serenko, Danila Vlasov, Nikolay Kudryashov, Vyacheslav Demin. To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity // 8th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA). — pp. 432–439. — 2018. https://www.sciencedirect.com/science/article/pii/S187705091830067X DOI: 10.1016/j.procs.2018.01.066
31. Tatiana Litvinova, Alexandr Sboev, Polina Panicheva. Profiling the age of Russian bloggers // Artificial Intelligence and Natural Language. — pp. 167–177. — 2018. http://link.springer.com/chapter/10.1007%2F978-3-030-01204-5_16 DOI: 10.1007/978-3-030-01204-5_16 (Q3, WoS)
32. Alexander Sboev, Ivan Moloshnikov, Dmitry Gudovskikh, Anton Selivanov, Roman Rybka, Tatiana Litvinova. Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception // 8th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA). — pp. 417–423. — 2018. https://www.sciencedirect.com/science/article/pii/S1877050918300656 DOI: 10.1016/j.procs.2018.01.064
33. Sergey Kartashov, Vadim Ushakov, Alexandra Maslennikova, Alexander Sboev, Anton Selivanov, Ivan Moloshnikov, Boris Velichkovsky. Human Brain Structural Organization in Healthy Volunteers and Patients with Schizophrenia // 1st International Early Research Career Enhancement School on Biologically Inspired Cognitive Architectures. — pp. 85–90. — 2018. DOI: 10.1007/978-3-319-63940-6_12 (WoS)
34. Aleksandr Sboev, Ivan Moloshnikov, Dmitry Gudovskikh, Roman Rybka. Data-driven Approaches to Author’s Profiling Identification for Russian Texts on Base of Complex Machine Learning Models in Combinations with Siamese Networks // 2018 International Conference on Computer, Electronic Information and Communications. — 2018.
35. Alexander Sboev, Danila Vlasov, Roman Rybka, Alexey Serenko. Spiking neural network reinforcement learning method based on temporal coding and STDP // Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. — pp. 458–463. — 2018. http://www.sciencedirect.com/science/article/pii/S1877050918323950 DOI: 10.1016/j.procs.2018.11.107
36. Alexander Sboev, Ivan Moloshnikov, Dmitry Gudovskikh, Anton Selivanov, Roman Rybka, Tatiana Litvinova. Deep Learning neural nets versus traditional machine learning in gender identification of authors of RusProfiling texts // 8th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA). — pp. 424–431. — 2018. https://www.sciencedirect.com/science/article/pii/S1877050918300668 DOI: 10.1016/j.procs.2018.01.065
37. Aleksandr Sboev, Alexey Serenko, Roman Rybka, Danila Vlasov. Influence of input encoding on solving a classification task by spiking neural network with STDP // Proceedings of the 16th International Conference of Numerical Analysis and Applied Mathematics. — pp. 270007:1–4. — 2019. http://aip.scitation.org/doi/abs/10.1063/1.5114281 DOI: 10.1063/1.5114281
38. Aleksandr Sboev, Dmitry Gudovskikh, Ivan Moloshnikov, Roman Rybka. A gender identification of text author in mixture of Russian multi-genre texts with distortions on base of data-driven approach using machine learning models // Proceedings of the 16th International Conference of Numerical Analysis and Applied Mathematics. — pp. 270006:1–4. — 2019. http://aip.scitation.org/doi/abs/10.1063/1.5114280 DOI: 10.1063/1.5114280
39. A. Sboev, R. Rybka, I. Moloshnikov, D. Gudovskikh, T. Litvinova. To the question of data-driven identification of author's age for Russian texts with age deceptions using machine learning // VII International Conference Problems of Mathematical Physics and Mathematical Modelling. — pp. 012049:1–6. — 2019. http://iopscience.iop.org/article/10.1088/1742-6596/1205/1/012049 DOI: 10.1088/1742-6596/1205/1/012049 (Q3, WoS)
40. A G Sboev, A V Emelyanov, K E Nikiruy, A V Serenko, A V Sitnikov, M Yu Presnyakov, R B Rybka, V V Rylkov, P K Kashkarov, M V Kovalchuk, V A Demin. Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights // Nanotechnology. — Vol. 31. — No. 4. — pp. 045201:1–10. — 2019. http://iopscience.iop.org/article/10.1088/1361-6528/ab4a6d DOI: 10.1088/1361-6528/ab4a6d (Q1, WoS)
41. Aleksandr Naumov, Roman Rybka, Alexander Sboev, Anton Selivanov, Artem Gryaznov. Neural-network method for determining text author's sentiment to an aspect specified by the named entity // Russian Advances in Artificial Intelligence. — pp. 134–143. — 2020. http://ceur-ws.org/Vol-2648/paper11.pdf
42. Anton A. Selivanov, Ivan A. Moloshnikov, Roman B. Rybka, Alexandr G. Sboev. Keyword Extraction Approach Based on Probabilistic-Entropy, Graph, and Neural Network Methods // Russian Conference on Artificial Intelligence. — pp. 284–295. — 2020. DOI: 10.1007/978-3-030-59535-7_21
43. A G Sboev, S G Sboeva, A V Gryaznov, A V Evteeva, R B Rybka, M S Silin. A neural network algorithm for extracting pharmacological information from Russian-language Internet reviews on drugs // The VI International Conference on Laser&Plasma researches and technologies. — pp. 012037:1–6. — 2020. https://iopscience.iop.org/article/10.1088/1742-6596/1686/1/012037 DOI: 10.1088/1742-6596/1686/1/012037
44. A. Sboev, R. Rybka, A. Gryaznov. Deep Neural Networks Ensemble with Word Vector Representation Models to Resolve Coreference Resolution in Russian // Advanced Technologies in Robotics and Intelligent Systems. — pp. 35–44. — 2020. http://link.springer.com/chapter/10.1007/978-3-030-33491-8_4 DOI: 10.1007/978-3-030-33491-8_4
45. Alexander Sboev, Alexey Serenko, Roman Rybka, Danila Vlasov. Solving a classification task by spiking neural network with STDP based on rate and temporal input encoding // Mathematical Methods in the Applied Sciences. — Vol. 43. — No. 13. — pp. 7802–7814. — 2020. https://onlinelibrary.wiley.com/doi/abs/10.1002/mma.6241 DOI: 10.1002/mma.6241 (Q1, WoS)
46. A. Sboev, A. Selivanov, R. Rybka, I. Moloshnikov, D. Bogachev. A Neural Network Model to Include Textual Dependency Tree Structure in Gender Classification of Russian Text Author // Advanced Technologies in Robotics and Intelligent Systems. — pp. 405–412. — 2020. http://link.springer.com/chapter/10.1007/978-3-030-33491-8_48 DOI: 10.1007/978-3-030-33491-8_48
47. A. Sboev, R. Rybka, A. Serenko, D. Vlasov. A Non-Fully-Connected Spiking Neural Network with STDP for Solving a Classification Task // Advanced Technologies in Robotics and Intelligent Systems. — pp. 223–229. — 2020. http://link.springer.com/chapter/10.1007/978-3-030-33491-8_27 DOI: 10.1007/978-3-030-33491-8_27
48. Alexander Sboev, Alexey Serenko, Roman Rybka, Danila Vlasov. Ensembling SNNs with STDP learning on base of rate stabilization for image classification // Brain-Inspired Cognitive Architectures for Artificial Intelligence. — pp. 446–452. — 2021. DOI: 10.1007/978-3-030-65596-9_53
49. Alexander Sboev, Sanna Sboeva, Ivan Moloshnikov, Artem Gryaznov, Roman Rybka, Alexander Naumov, Anton Selivanov, Gleb Rylkov, Viacheslav Ilyin. An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets // . — pp. 1–23. — 2021. http://arxiv.org/abs/2105.00059
50. A. G. Sboev, N. A. Kudryashov, I. A. Moloshnikov, A. V. Serenko, A. V. Naumov, R. B. Rybka. Baseline accuracy of forecasting COVID-19 cases in Moscow region on a year in retrospect using basic statistical and machine learning methods // The VII International Conference on Laser & Plasma Research and Technologies. — pp. 012029:1–8. — 2021. https://iopscience.iop.org/article/10.1088/1742-6596/2036/1/012029 DOI: 10.1088/1742-6596/2036/1/012029 (Q3, WoS)
51. Alexander Sboev, Danila Vlasov, Roman Rybka, Yury Davydov, Alexey Serenko, Vyacheslav Demin. Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks // Mathematics. — Vol. 9. — No. 24. — pp. 3237:1–10. — 2021. https://www.mdpi.com/2227-7390/9/24/3237 DOI: 10.3390/math9243237 (Q1, Q2, WoS)
52. Aleksandr Naumov, Alexander Sboev, Roman Rybka. Data-Driven Model for Emotion Detection in Russian Texts // Brain-Inspired Cognitive Architectures for Artificial Intelligence. — pp. 637–642. — 2021. https://www.sciencedirect.com/science/article/pii/S1877050921013247 DOI: 10.1016/j.procs.2021.06.075
53. A. V. Naumov, I. A. Moloshnikov, A. V. Serenko, A. G. Sboev, R. B. Rybka. Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods // 10th International Young Scientists Conference in Computational Science. — pp. 276–284. — 2021. https://www.sciencedirect.com/science/article/pii/S187705092102069X DOI: 10.1016/j.procs.2021.10.028
54. Alexander Sboev, Anton Selivanov, Roman Rybka, Ivan Moloshnikov, Gleb Rylkov. Evaluation of Machine Learning Methods for Relation Extraction Between Drug Adverse Effects and Medications in Russian Texts of Internet User Reviews // Proceedings of The 5th International Workshop on Deep Learning in Computational Physics. — pp. 006:1–12. — 2021. DOI: 10.22323/1.410.0006
55. Alexander Sboev, Ivan Moloshnikov, Alexander Naumov, Anastasia Levochkina, Roman Rybka. The Russian language corpus and a neural network to analyse Internet tweet reports about Covid-19 // Proceedings of The 5th International Workshop on Deep Learning in Computational Physics. — pp. 017:1–8. — 2021. DOI: 10.22323/1.410.0017
56. Alexander Sboev, Anton Selivanov, Gleb Rylkov, Roman Rybka. On the accuracy of different neural language model approaches to ADE extraction in natural language corpora // Brain-Inspired Cognitive Architectures for Artificial Intelligence. — pp. 706–711. — 2021. DOI: 10.1016/j.procs.2021.06.082
57. Aleksandr Sboev, Danila Vlasov, Alexey Serenko, Roman Rybka, Ivan Moloshnikov. STDP-based classificational spiking neural networks combining rate and temporal coding // Advances in Neural Computation, Machine Learning, and Cognitive Research IV. — pp. 403–411. — 2021. http://link.springer.com/chapter/10.1007%2F978-3-030-60577-3_48 DOI: 10.1007/978-3-030-60577-3_48
58. Roman Rybka, Alexander Sboev, Dmitriy Bogachev, Anton Selivanov, Ivan Moloshnikov. Graph convolution network with attention to include syntax trees into text author’s gender identification task // 18th International Conference of Numerical Analysis and Applied Mathematics. — p. 340005. — 2022. https://aip.scitation.org/doi/abs/10.1063/5.0081635 DOI: 10.1063/5.0081635
59. Alexander Sboev, Alexey Serenko, Roman Rybka. Correlation Encoding of Input Data for Solving a Classification Task by a Spiking Neural Network with Spike-Timing-Dependent Plasticity // Biologically Inspired Cognitive Architectures. — pp. 457–462. — 2022. DOI: 10.1007/978-3-030-96993-6_51
60. Alexander Sboev, Aleksandr Naumov, Ivan Moloshnikov, Roman Rybka. Sentiment Analysis of Russian Reviews to Estimate the Usefulness of Drugs Using the Domain-Specific XLM-RoBERTa Model // Biologically Inspired Cognitive Architectures. — pp. 447–456. — 2022. DOI: 10.1007/978-3-030-96993-6_49
61. Alexander Sboev, Sanna Sboeva, Ivan Moloshnikov, Artem Gryaznov, Roman Rybka, Alexander Naumov, Anton Selivanov, Gleb Rylkov, Vyacheslav Ilyin. Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models // Applied Sciences. — Vol. 12. — No. 1. — pp. 491:1–34. — 2022. https://www.mdpi.com/2076-3417/12/1/491 DOI: 10.3390/app12010491
62. Alexander Sboev, Anton Selivanov, Ivan Moloshnikov, Roman Rybka, Artem Gryaznov, Sanna Sboeva, Gleb Rylkov. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications // Big Data and Cognitive Computing. — Vol. 6. — No. 1. — p. 10. — 2022. (WoS)
63. Alexander Sboev, Gleb Rylkov, Roman Rybka, Artem Gryaznov, Sanna Sboeva. Data-driven model for identifying related pharmaceutically-significant entities in clinical texts // 18th International Conference of Numerical Analysis and Applied Mathematics. — p. 340003. — 2022. https://aip.scitation.org/doi/abs/10.1063/5.0081604 DOI: 10.1063/5.0081604
64. A. G. Sboev, R. B. Rybka, Yu. A. Davydov, I. A. Moloshnikov. Neural network data driven model of the process of analyzing control commands for a mobile robot in natural Russian language // 18th International Conference of Numerical Analysis and Applied Mathematics. — p. 340004. — 2022. https://aip.scitation.org/doi/abs/10.1063/5.0081608 DOI: 10.1063/5.0081608
65. Alexander Sboev, Ivan Moloshnikov, Anton Selivanov, Gleb Rylkov, Roman Rybka. The Two-Stage Algorithm for Extraction of the Significant Pharmaceutical Named Entities and Their Relations in the Russian-Language Reviews on Medications on Base of the XLM-RoBERTa Language Model // Biologically Inspired Cognitive Architectures. — pp. 463–471. — 2022. DOI: 10.1007/978-3-030-96993-6_51
66. Alexander Sboev, Anton Selivanov, Ivan Moloshnikov, Roman Rybka, Artem Gryaznov, Sanna Sboeva, Gleb Rylkov. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications // Big Data and Cognitive Computing. — Vol. 6. — No. 1. — pp. 10:1–16. — 2022. https://www.mdpi.com/2504-2289/6/1/10 DOI: 10.3390/bdcc6010010
67. Danila Vlasov, Yury Davydov, Alexey Serenko, Roman Rybka, Alexander Sboev. Spoken Digits Classification Based on Spiking Neural Networks with Memristor-Based STDP // 2022 International Conference on Computational Science and Computational Intelligence (CSCI). — pp. 330–336. — 2022.
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