Igor Atamanyuk

Department of Applied Mathematics, Warsaw University of Life Sciences, Warsaw, Poland;
Department of Higher and Applied Mathematics, Mykolayiv National Agrarian University, Ukraine;
Doctor of Technical Sciences,  Professor, Head of Department 
Modeling, forecasting, filtering, recognition of random sequences and functions

Scopus profile: link
Researcher ID: E-5125-2018
Google Scholar profile: link
ID ORCID: https://orcid.org/0000-0002-8127-6193

Selected Publications:

    1. Atamanyuk, I., Kondratenko, Y., Solesvik, M. (2024). Reliability Control of Technical Systems based on Canonical Decomposition of Random Sequences. Recent Developments in Automatic Control Systems, 355–378. https://doi.org/10.1201/9781003339229-17 
    2. Atamanyuk, I., Poltorak, A., Sidenko, I., Bartnicka, E., Kondratenko, Y. (2024). Concept of express monitoring of the state’s financial security. 22nd Industrial Simulation Conference ISC 2024, 24–29.
    3. Sidenko, I., Atamanyuk, I., Myroniuk, O., Kondratenko, G., Poltorak, A., Kondratenko, Y. (2024). Hybrid neural network and genetic algorithm combination for recognition and avoidance of vehicle obstacles. 22nd Industrial Simulation Conference ISC 2024, 88–92.
    4. Shevchenko, A. I., Kondratenko, Y. P., Slyusar, V. I., Atamanyuk, I. P., Kondratenko, G. V., Yeroshenko, T. V. (2024). Analysis of the Prospect Domains in AI Implementation: Nationals, NATO and Ukraine AI Strategies. Research Tendencies and Prospect Domains for AI Development and Implementation, 1–27. https://doi.org/10.1201/9788770046947-1 
    5. Atamanyuk, I., Kondratenko, Y., Havrysh, V., Volosyuk, Y. (2023). Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences. Scientific Reports, 13 (1). https://doi.org/10.1038/s41598-022-27318-0 
    6. Atamanyuk, I., Havrysh, V., Nitsenko, V., Diachenko, O., Tepliuk, M., Chebakova, T., Trofimova, H. (2022). Forecasting of Winter Wheat Yield: A Mathematical Model and Field Experiments. Agriculture, 13 (1), 41. https://doi.org/10.3390/agriculture13010041 
    7. Kondratenko, Y., Atamanyuk, I., Sidenko, I., Kondratenko, G., Sichevskyi, S. (2022). Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing. Sensors, 22 (3), 1062. https://doi.org/10.3390/s22031062