The work is devoted to the analysis of machine learning methods for solving problems of automatic document processing. The study considers such methods as classification, information extraction, pattern recognition and natural language processing and their application in the analysis of text data. An analysis of existing algorithms and models, including linear models, decision trees, support vector methods, and a comparison of their effectiveness depending on various conditions and parameters is carried out. Particular attention is paid to the problems that specialists face when using machine learning methods in working with documents, such as data quality, the need for pre-processing and tuning of model parameters. Prospects for further research in this area and examples of possible integration of modern machine learning methods to improve the efficiency and accuracy of automatic document processing in various industries are given.

Keywords: machine learning, automatic document processing, computational experiment, artificial intelligence, classification models, software package

Drying module is a part of any modern gas analysing device. Using the Peltier elements for cooling the gas probe in druying module is a known technical solution. Peltier elements are very energy-intensive things. So the task of calculation of amount of electrical energy which is needed for predetermined temperature decline of gas probe is very topical. The solution of this task is given in the article. With formulas for partial pressure of vapour we can calculate the humidity of gas at the output of drying module. Then we can find the temperature of gas. Finally using the heat balance equation and nomogramms we can calculate the voltage for Peltier element.

Keywords: Peltier element, gas probe, drying process, gas analyzer, heat balance, partial pressure of vapour