A model is proposed for selecting potentially interesting events for a person, in accordance with his requests and wishes, in order to achieve the goal of increasing the effectiveness of informing the population about cultural events of the city, and, as a result, improving the cultural level of society, Describes the implementation of the model in the form of a software product, which, with the help of intelligent data selection algorithms, allows you to simplify the process of searching for both potential cultural events that are interesting for a person, and new participants for their organizers. The criteria for selecting the event of interest and the process of selecting them in accordance with the degree of importance to the user are described. Formulas for calculating the final coefficients that serve as a numerical characteristic of the optimality of the event for a particular user are given. An example of the implementation of the proposed algorithm is given.

Keywords: intellectual search, selection criteria, cultural level, selection of events, forecasting

The article presents the results of experiments to study the possibility of using machine vision algorithms for identifying timber, in particular round timber, based on the recognition of the image of annual rings taken as a natural marker. Image recognition is carried out using fingerprint identification algorithms. The results of testing several algorithms for fingerprint identification are presented. The analysis of the effectiveness of the proposed method and its suitability for solving such problems is carried out.

Keywords: annual rings, fingerprints, recognition, identification, image processing, pattern recognition, operational accounting, efficiency

The article presents the results of experiments to study the possibilities of short-term forecasting of financial time series using various types of forecasting algorithms. There are results of testing of ARIMA method and Long short-term memory algorithm on a data set wich describes the value of the US dollar relative to the Russian ruble for one day, to predict the future value of the indicator under study. The estimation of the accuracy of forecasting of each of the methods and their suitability for solving such problems is made.

Keywords: forecasting, time series, neural networks, finance, regression algorithms, data analysis, reccurent neural networks, python, numpy, pandas, keras