At Springer Publishers, in a series of « Lecture Notes on Data Engineering and Communications Technologies» (LNDECT,volume 149) the book “Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making” is printed. The book contains extended versions of the conference papers presented at the scientific conference International Scientific Conference “Intellectual Systems of Decision Making and Problem of Computational Intelligence” ISDMCI 2022.

The results of the research by Prof. Radivilova T.A. of the V.V. Popovskyy ICE Department were presented in the following section:
Kirichenko, L., Pichugina, O., Radivilova, T., Pavlenko, K. (2023). Application of Wavelet Transform for Machine Learning Classification of Time Series. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_31

This work focuses on the classification of time series by applying a continuous wavelet transform and visualizing the resulting wavelet spectrum. The images of the wavelet spectrum are input into the classifying neural network. The wavelet transform analyzes the change over time of the frequency components of the time sequence. The article considers the classification of time realizations subject to normal additive noise with different values of dispersion. Visualization of the wavelet spectrum for different wavelet functions is presented. A residual neural network was used to classify the spectrum images. The results of computational experience show that classification based on wavelet spectra image recognition allows us to distinguish signals with additive noise component that have different signal-to-noise levels.

Congratulations to the authors of the section of this book for another publication in Springer!