The article is devoted to the analysis of fire risks of the operation of electrical installations in the agro-industrial complex of the region. According to statistics, the number of fires occurring for electrical reasons is steadily increasing, which makes the issue of fire risk management relevant. In order to identify and prevent these risks, a technogenic safety system has been developed, presented as a set of methods and tools, which are based on the generation of new data on the causes of threats to the operation of electrical installations based on the analysis of data on the current state of the system. Highlighted characteristics such as Human Parameters, Electrical Installation Parameters and Environmental Parameters. Mathematical models of the given components are considered, a fire risk assessment tree is compiled, in which the input parameters and intermediate vertices with the solution methods in them were determined. The developed model can be used to carry out experiments in order to study the behavior of an electrical installation under various conditions. The application of this method is especially important in cases where the removal of indicators and testing of the operation of an electrical installation is impossible due to the risk of injury or material damage.

The paper presents a neural network study of the data of wheat seed quality. It is established that the analysis of bioelectrical signals of wheat seeds based on a neural network can be used in practice for the solution of two problems - diagnostics of seed material quality and the evaluation of cleaning line quality (separation into fractions). The paper presents the results of initial data preparation, formation of a neural network, analysis of training data for two practical problems of classification. It was established using a neural network that there is a nonlinear dependence of the membrane potential maximum value and the signal rise time on the seed yield. The model makes it possible to predict yield in terms of the seed material quality. A nonlinear dependence of the maximum membrane potential, the signal rise time of wheat seeds and the seeds variety to one or another faction (speed of separation into the fractions in this example) was also established in this paper. Studies have shown that the seeds variety is an important informative feature for solving the problem of classifying seeds by fractions. Therefore, it is necessary to conduct additional studies with other wheat seeds varieties to apply this method in practice.

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The purpose of this work is neural network technologies in solving the problem of stratification of patients at the oncologic dispensary in order to optimize the treatment course and reduce the cost of its implementation. The material for the study is a sample containing data on the medical indicators of 77 patients with colorectal cancer. As a result of the use of artificial neural networks, it is planned to increase the efficiency of analysis of oncological research results and clinical data of patients.