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Документ On the Characteristics of the Input Material Flow of the Transport Conveyor(Vasyl Stefanyk Precarpathian National University, 2022) Pihnastyi, O. M.; Sobol, Maksym; Yelchaninov, D. B.In this paper, the statistical characteristics of the flow of material entering the input of a conveyor-type transport system are studied. For a set of data obtained as a result of experimental measurements of the input flow of material, the law of distribution of a random variable and the correlation function is investigated. Theoretical assumptions about the law of change of the correlation function for the input flow of material are confirmed.Документ Analysis of the text preprocessing methods influence on the destructive messages classifier(Національний технічний університет "Харківський політехнічний інститут", 2020) Orlovskyi, Oleksandr; Ostapov, SergeySocial networks are increasingly becoming an environment for threats, insults, profanity and other destructive manifestations of human communication. Today, a huge number of people are involvedin online platforms, and the amount of content created and reactions to it is constantly breaking records. Therefore, there is a need to automate the detection and counteraction of antisocial influences. One of the important areas of such activities is the detection of toxic comments that contain threats, insults, profanity, contempt for others and more. To perform this task, researchers usually build a classifier based on neural networks. And for their training they use a collected or publicly available set of data. The article investigates how different methods of pre-processing of input data affect the final accuracy of the classifier. Previous studies in this direction have confirmed the presence of an impact on the result, but did not allow to draw definitive conclusions about the effectiveness. Goal. Research of preliminary processing of text data methods influence on the destructive messages classifier. Results.It has been shown that the effect of a particular method can be quite dependent on the content in the data set. In addition, it is noted that sometimes the impact may be insignificant, and in some cases may even lead to a worsening of the result. It is also justified the need to pre-check the data set for the percentage of elements that fall under the impact of a particular method. Originality. The methods of data processing are evaluated on the basis of English and Russian data sets. Practical significance. The obtained results allow to make better decisions about the usage of certain pre-processing methods to improve the accuracy of the destructive messages classifier.Документ Implementing of Microsoft Azure machine learning technology for electric machines optimization(Національний технічний університет "Харківський політехнічний інститут", 2019) Pliuhin, V.; Sukhonos, M.; Pan, M.; Petrenko, O.; Petrenko, M.Purpose. To consider problems of electric machines optimizationwithin a wide range of many variables variation as well as the presence of many calculation constraints in a single-criteria optimization search tasks. Results. A structural model for optimizing electric machines of arbitrary type using Microsoft Azure machine learning technology has been developed. The obtained results, using several optimization methods from the Microsoft Azure database are demonstrated. The advantages of cloud computing and optimization based on remote servers are shown. The results of statistical analysis of the results are given. Originality. Microsoft Azure machine learning technology was used for electrical machines optimization for the first time. Recommendations for modifying standard algorithms, offered by Microsoft Azure are given. Practical value. Significant time reduction and resources spent on the optimization of electrical machines in a wide range of variable variables. Reducing the time to develop optimization algorithms. The possibility of automatic statistical analysis ofthe results after performing optimization calculations.