The necessity of studying the influence of the transformation of the frequency mismatch function of a coherent bundle of radio pulses on the quality of solving the radar frequency resolution problem is substantiated. This solution determines the effectiveness of radar observation of high-speed and maneuvering individual and group aerodynamic objects. The method is based on explicit expressions for calculating the normalized frequency mismatch function of a coherent bundle of radio pulses, taking into account its transformation due to the radial motion of high-speed and maneuvering individual and group aerodynamic objects. The estimation of the potential frequency resolution of bundles with different numbers of radio pulses with typical parameters for a coherent pulse radar is carried out. Possible values of frequency resolution under the additive effect of uncorrelated internal noise of the radar receiver and the multiplicative effect of correlated phase fluctuations of the radar signal are estimated. With an insignificant multiplicative effect of correlated phase fluctuations, a twofold increase in the number of radio pulses in a bundle provides an improvement in the frequency resolution (reduction of the width of the normalized frequency mismatch function) by 100 %. With the predominant multiplicative effect of these fluctuations, a twofold increase in the number of radio pulses results in an improvement in the frequency resolution by about 40 %. The developed method is of great theoretical and practical importance for the further development of the radar theory of high-speed and maneuvering individual and group aerodynamic objects.

This article investigated the problem of using machine learning algorithms to recognize and identify a user in a video sequence. The scientific novelty lies in the proposed improved Viola-Jones method, which will allow more efficient and faster recognition of a person's face. The practical value of the results obtained in the work is determined by the possibility of using the proposed method to create systems for human face recognition. A review of existing methods of face recognition, their main characteristics, architecture and features was carried out. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. Possible modifications of the Viola-Jones method are presented. The main contribution presented in this article is an experimental study of the impact of various types of noise and the improvement of company security through the development of a computer system for recognizing and identifying users in a video sequence. During the study, the following tasks were solved: – a model of face recognition is proposed, that is, the system automatically detects a person's face in the image (scanned photos or video materials); – an algorithm for analyzing a face is proposed, that is, a representation of a person's face in the form of 68 modal points; – an algorithm for creating a digital fingerprint of a face, which converts the results of facial analysis into a digital code; – development of a match search module, that is, the module compares the faceprint with the database until a match is found

Diabetesisadiseaseforwhichthereisnopermanentcure;therefore,methodsandinformationsystemsarerequired for its early detection. This paper proposes an information system for predicting diabetes based on the use of data mining methods and machine learning algorithms. The paper discusses a number of machine learning methods such as random forest, AdaBoost algorithm, multilayer perceptron, neuro–like structure of Consecutive Geometric Transformations Models (CGTM), linear regression based on the stochastic gradient descent, generalized regression neural network and regression based on the support vector machine. The Pima Indian Diabetes dataset collected from the UCI machine learning repository was used in the research. The dataset contains information about 768 patients and their corresponding nine unique attributes: the number of times of pregnancy; plasma glucose concentration for two hours in an oral glucose tolerance test; diastolic blood pressure; the thickness of the folds of the skin of the triceps; the concentration of serum insulin for two hours; body mass index; a function of diabetes heredity; the age of a person; the result of a variable class (0 – no diabetes, 1 – a sick person). The research has been carried out to improve the prediction index based on the Recursive Feature Elimination method. It was found that the logistic regression model performed well in predicting diabetes. It has been shown that in order to use the created model to predict the likelihood of diabetes mellitus with an accuracy of 78%, it is necessary and sufficient to use such indicators of the patient's health status as the number of times of pregnancy, the concentration of glucose in the blood plasma during the oral glucose tolerance test, the BMI index and the result of the calculation of the heredity functions "Diabetes Pedigree Function".

An approach to evaluating the software tests quality using aggregated quality criteria is proposed. The subject of the study is the formation of a software tests quality evaluation system, which can be used in the software development process. The article gives the full classification of the testing types. The testing process was represented in several stages: testing requirements, test analysis, and the testing process. Tests quality evaluation will improve the testing process, which purpose is to ensure the specified quality of the software being developed.

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