Кіріченко, Л. О.Радівілова, Т. А.Зінченко, П. П.2021-06-102021-06-102021Kirichenko L. Classification of time realizations using machine learning recognition of recurrence plots / L. Kirichenko, P. Zinchenko, T. Radivilova // Advances in Intelligent Systems and Computing. – 2021. – P. 687–696.https://openarchive.nure.ua/handle/document/16446In the article,the machine learning classification of time realizations using the recurrence plot visualization is considered. Every time realization is converted intoa matrix of recurrence states and it is presented as a black-and-white image. The resulting images of realizations are classified using deep neural networks. A deep residual neural network is used as an image classifier. The binary classifi-cation of EEG realizationsis carried out. The result of the binary classification is the detection of an epileptic seizure. The data for the experiment are records of brain activity containing 178 values. The results of the studyshow that the consi-dered method has a high classification accuracy. The proposed classification ap-proach can be readily used in practice.enmachine learning classificationtime series classificationrecurrence plotEEG realizationsdeep residual networksArticle