Current trends in computer data analysis

Authors

  • Yuriy S. Kharin Research Institute for Applied Problems of Mathematics and Informatics, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus
  • Sergey V. Ablameyko Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

Keywords:

statistical data analysis, robustness, stochastics, count data, neural networks, computer vision

Abstract

The current trends in computer data analysis presented at the conference «Computer data analysis and modelling: stochastics and data science» and the congress «Information systems and technologies» are described.

Author Biographies

  • Yuriy S. Kharin, Research Institute for Applied Problems of Mathematics and Informatics, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

    doctor of science (physics and mathematics), academician of the National Academy of Sciences of Belarus, full professor; director

  • Sergey V. Ablameyko, Belarusian State University, 4 Niezaliezhnasci Avenue, Minsk 220030, Belarus

    doctor of science (engineering), academician of the National Academy of Sciences of Belarus, full professor; professor at the department of web technologies and computer simulation, faculty of mechanics and mathematics

References

  1. Kharin Yu. Robustness in statistical pattern recognition. Dordrecht: Kluwer Academic Publishers; 1996. 302 p.
  2. Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA. Robust statistics: the approach based on influence functions. New York: Wiley; 1986. 350 p.
  3. Kharin Yu. Robustness in statistical forecasting. New York: Springer; 2013. 356 p.
  4. Maronna RA, Martin RD, Yohai VJ. Robust statistics: theory and methods. Chichester: Wiley; 2006. 420 p.
  5. Rousseeuw PJ, Leroy AM. Robust regression and other detection. London: Wiley; 1987. 485 p.
  6. Becker C, Fried R, Kuhnt S, editors. Robustness and complex data structures. New York: Springer; 2013. 283 p.
  7. Fokianos K, Fried R, Kharin Yu, Voloshko V. Statistical analysis of multivariate discrete-valued time series. Journal of Multivariate Analysis. 2022;188:104805.
  8. Kharin YuS, Voloshko VA. Robust estimation of AR coefficients under simultaneously influencing outliers and missing values. Journal of Statistical Planning and Inference. 2011;141:3276–3288. DOI: 10.1016/j.jspi.2011.04.015.
  9. Kharin Yu, Voloshko V. Robust estimation for binomial conditionally non-linear autoregressive time series based on multivariate conditional frequencies. Journal of Multivariate Analysis. 2021;185:104777.
  10. Kharin YuS, Petlitskii AI. A Markov chain of order s with r partial connections and statistical inference on its parameters. Discrete Mathematics and Applications. 2007;17(3):295–317.
  11. Kharin Yu, Voloshko V. Statistical analysis of parsimonious high-order multivariate finite Markov chains based on sufficient statistics. Journal of Multivariate Analysis. 2025;208:105422. DOI: 10.1016/j.jmva.2025.105422.
  12. Kharin YuS, Valoshka VA, Dernakova OV, Malugin VI, Kharin AYu. Statistical forecasting of the dynamics of epidemiological indicators for COVID-19 incidence in the Republic of Belarus. Journal of the Belarusian State University. Mathematics and Informatics. 2020;3:36–50. Russian. DOI: 10.33581/2520-6508-2020-3-36-50.
  13. Kim S, Park S, Na B, Yoon S. Spiking-YOLO: spiking neural network for energy-efficient object detection. arXiv:1903.06530 [Preprint]. 2019 [cited 2025 December 20]: [9 p.]. Available from: https://arxiv.org/abs/1903.06530.
  14. Luo X, Yao M, Chou Y, Xu B, Li G. Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection. arXiv:2407.20708 [Preprint]. 2024 [cited 2025 December 20]: [19 p.]. Available from: https://arxiv.org/pdf/2407.20708.
  15. Niu W-H, Zhai R-B. A video human behavior recognition method based on improved 3D ResNet[J]. Computer Engineering & Science. 2023;45(10):1814–1821.
  16. Lai HY, Hu C-C, Wen C-H, Wu J-X, Pai N-S, Yeh C-Y. Mel-scale frequency extraction and classification of dialect-speech signals with 1D CNN based classifier for gender and region recognition. IEEE Access. 2024;12:102962–102976. DOI: 10.1109/ACCESS.2024.3430296.
  17. Haruna Y, Qin S, Chukkol AHA, Yusuf AA, Bello I, Lawan A. Exploring the synergies of hybrid convolutional neural network and vision transformer architectures for computer vision: a survey. Engineering Applications of Artificial Intelligence. 2025;144:110057. DOI: 10.1016/j.engappai.2025.110057.
  18. Kang Hong H, Cho H. Cross-modal dynamic transfer learning for multimodal emotion recognition. IEEE Access. 2024;12:14324–14333. DOI: 10.1109/ACCESS.2024.3356185.

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Published

2026-05-06

How to Cite

[1]
Kharin, Y.S. and Ablameyko, S.V. 2026. Current trends in computer data analysis. Journal of the Belarusian State University. Mathematics and Informatics. 1 (May 2026), 6–15.