This article discusses the problems of determining the organs of air respiration on computed tomography images using convolutional neural networks of the U-NET architecture. The prospects of using neural networks in the analysis of medical images, as well as the use of the U-NET architecture for semantic segmentation of images are described. The structure of an artificial neural network based on the U-NET architecture is being formed. The structure of the layers of this network is visualized and the components of this structure are described. Special attention is paid to the description and implementation of the convolution process. The formula for determining the weight coefficients of the separation boundary is presented. Algorithms for the formation of an artificial neural network model and an algorithm for constructing layers are proposed. A method of increasing data for a training sample of images of medical images is considered. The image of the result of the determination of the chest organs and the corresponding mask are presented.

Keywords: convolutional neural networks, U-NET architecture, deep learning, image recognition, machine learning

The problem of atmospheric air pollution in the regions is considered. Attention is paid to the development and implementation of automated air pollution control systems. The developed information and analytical system has a two-level system and consists of two subsystems: a city information system and a regional information system. The proposed software allows you to perform calculations of surface concentrations of pollutants with the formation of a data bank, as well as the construction of maps of pollution in the region and histograms of the distribution of the level of adverse effects of atmospheric pollution on humans.

Keywords: atmospheric air, pollution, monitoring, information and analytical system, maps of the pollution of the region

The results of clinical trials are the main source of information in the implementation of medical activities in accordance with the principles of evidence-based medicine. At the moment, there are no information systems that would allow a doctor to select clinical studies within the framework of nosology that best match the profile of a particular patient, in order to further analyze their results and select therapy. The aim of the study was to improve the existing process of searching for clinical trials by using the prioritization method according to the inclusion criteria set by the doctor during the selection. To achieve this goal, the following tasks were implemented, namely, the process of selecting and searching for clinical trials by doctors was studied and the method of searching for clinical trials by doctors and the allocation of the necessary criteria was worked out. The team of authors proposed an algorithm for searching for clinical trials according to inclusion criteria, which in turn will significantly increase the effectiveness and reduce the time for searching and choosing therapy.

Keywords: clinical studies, criteria search algorithms, criteria search methods, including factors, search for the nearest class, services