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Документ Machine learning methods application for solving the problem of biological data analysis(Національний технічний університет "Харківський політехнічний інститут", 2018) Akhiiezer, O. B.; Dunaievska, О. I.; Serdiuk, I. V.; Spivak, S. V.According to statistics, every fifth married couple is faced with the inability to conceive a child. Male germ cells are very vulnerable, and the growing number of cases of male infertility confirms that in today's world there are many factors that affect the activity of spermatozoa and their number. But the important thing is not so much their quantity, but quality. The spermogram is an objective method of laboratory diagnosis, which allows to accurately assess the man’s ability to fertilize by analyzing ejaculate for a number of key parameters. Only a spermogram can answer the question of a possible male infertility and the presence of urological diseases. When constructing spermograms, it is important to determine not only the number of good spermatozoa, but also their morphology and mobility. Therefore, research and improvement of some stages of spermogramm is the purpose of the study. This article addresses the problem of classification of spermatozoa in good and bad ones, taking into account their mobility and morphology, using methods of machine learning. In order to implement the first stage of machine learning (with a teacher) in the graphic editor, educational specimens (training sample) were created. The training was implemented by three methods: the method of support vector machine, the logistic regression and the method of K - the nearest neighbors. As a result of testing, the method K - the nearest neighbors is chosen. At the testing stage, a sample of 15 different spermatozoa was used in different variations of rotation around their axis. The test sample did not contain specimens from the training sample and was formed taking into account the morphological characteristics of the spermatozoa, but did not copy them from the training sample. At the final stage of study, the program's functioningwas tested on real data.Документ Structure and corrosion resistance of vacuum-arc multi-period CrN/Cu, ZrN/Cu and NbN/Cu coatings(2020) Postelnyk, H. O.; Sobol, O. V.; Stolbovoy, V. A.; Serdiuk, I. V.; Chocholaty, O.The structure and properties of vacuum-arc multi-period composite coatings of the MeN/Cu system (where Me is Cr, Zr, and Nb) are studied. It was found that at the smallest nanolayer thickness (about 8…10 nm) of composites in the layers of all systems, only a phase with an fcc lattice is formed, without a pronounced texture in the nitride layers. For ZrN and CrN, the phases with an fcc lattice are equilibrium, and for NbN, they are nonequilibrium. An increase in the thickness of nitride layers leads to the appearance of a texture in ZrN/Cu and CrN/Cu systems and the formation of an equilibrium ε-NbN phase in the layers of the NbN/Cu system. Tests for corrosion resistance in the environment of the formation of chloride ions showed that the coatings are anodic reaction. The best corrosion properties were obtained for coatings with the smallest layer thickness (about 8…10 nm).