This research was funded by the: Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan, grant number AP14869972 «Development and adaptation of computer vision and machine learning methods for solving precision agriculture problems using unmanned aerial systems», grant number AP08856412 « Development of Intelligent Data Processing and Flight Planning Models for Precision Farming Tasks Using UAVs», grant number BR18574144 "Development of a data mining system for monitoring dams and other engineering structures under the conditions of man-made and natural impacts", grant number BR10965172 «Space monitoring and GIS for quantitative assessment of soil salinity and degradation of agricultural lands in South Kazakhstan»; the Slovak Research and Development Agency ''New methods development for reliability analysis of complex system'' under Grant APVV-18-0027; the Ministry of Education, Science, Research, and Sport of the Slovak Republic ''New approaches of reliability analysis of non-coherent systems'' under Grant VEGA 1/0165/21.

Abstract-Requirements and criteria for selection of cloud platform and platform visualization are stated by which optimal cloud products will be chosen for the Republic of Kazakhstan eGovernment considering quality-price ratio, and also the framework of information and communication architecture will be introduced.

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.

Salinization of cultivated soil is an important negative factor that reduces crop yields. Obtaining accurate and timely data on the salinity of soil horizons allows for planning the agrotechnical measures to reduce this negative impact. The method of soil salinity mapping of the 0–30 cm layer on irrigated arable land with the help of multispectral data received from the UAV is described in this article. The research was carried out in the south of the Almaty region of Kazakhstan. In May 2022, 80 soil samples were taken from the ground survey, and overflight of two adjacent fields was performed. The flight was carried out using a UAV equipped with a multispectral camera. The data preprocessing method is proposed herein, and several machine learning algorithms are compared (XGBoost, LightGBM, random forest, support vector machines, ridge regression, elastic net, etc.). Machine learning methods provided regression reconstruction to predict the electrical conductivity of the 0–30 cm soil layer based on an optimized list of spectral indices. The XGB regressor model showed the best quality results: the coefficient of determination was 0.701, the mean-squared error was 0.508, and the mean absolute error was 0.514. A comparison with the results obtained based on Landsat 8 data using a similar model was performed. Soil salinity mapping using UAVs provides much better spatial detailing than satellite data and has the possibility of an arbitrary selection of the survey time, less dependence on the conditions of cloud cover, and a comparable degree of accuracy of estimates.

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