<p><span lang="EN-US">An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.</span></p>

Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures

Today's migration is a rather complex and controversial process. Having a number of advantages and positive results for the development of the receiving and sending countries, it also leads to negative consequences. The more a country's population is involved in migration processes, the more acutely its consequences manifest. The situation is also aggravated by the increasing economic and political crises, which negatively affect the ability of third countries to maintain internal stability. The issue does not exclude the Central Asian region in the context of the intensification of local armed conflicts in adjacent territories and current geopolitical aspects. The study purpose is to assess migration processes and their impact on the Republic of Kazakhstan as the one of the ‘host’ countries in Central Asia. A chronology of key economic events regarding strategic opportunities in the migration processes was carried out. Taking into account the current regional and global challenges, it has been established that the current trends in migration processes have a positive effect on the development of the labor market as an important component of the national economy, contributing to an increase in the welfare of the country. At the same time, the policy of Kazakhstan is also trying to pursue a pragmatic immigration policy to prevent the negative impact of immigrants on the local population.

The article examines how digital economy technologies change the living conditions and economic behavior of people. Attention is focused on business behavior and new opportunities in the business environment. In particular, they examine changes in business strategy, competition, new marketing and customer relations opportunities, the emergence of new sources of profit and competitiveness factors. Organizational forms and new methods of doing business in the context of digital transformation and digital economy are analyzed. At the same time, the key elements and features of the digital economy based on the development of information and communication technologies, the Internet and other technologies are being explored. It seems that in the context of evolutionary digitalization, there is a need for a structural transformation of business processes and the search for new models of doing business using digital technologies. The uneven spread of digital business in different countries is determined by the level of readiness of society and the economy for the transition to a new vector of development. In the domestic business environment, there are certain barriers that hinder the growth of digitalization. The article discusses the main problems and ways of business development in the digital economy. At the same time, data on the development of e-commerce as a factor in the development of digital entrepreneurship in the Republic of Kazakhstan are considered and analyzed.

In the 21st century, the European banking system is going through a period of fundamental structural changes caused by global challenges that will determine its future ability to serve the financial needs of the economy on digital platforms. The aim of this research is to study how electronic social media influence the effectiveness of e-banking in the context of banking sector instability. The empirical model for determining the impact of digital media was developed on the basis of theoretical concepts and tested on a sample of 307 respondents from banking institutions (UniCredit, Raiffeisen, PrivatGroup) and 2,800 consumers of banking services through quantitative methods, as well as economic and statistical calculation methods. The obtained results show that digital social media have a quite strong effect (r=0.788) on creating consumer loyalty and increasing indicators of digital trade in banking services. The theoretical significance of this study is the synthesis of scientific approaches to the creation of a conceptual method for determining the effectiveness of engaging digital media resources as a marketing tool for improving the effectiveness of digital trade in banking services. The key practical aspect is the combination of theoretical approaches and business experience in optimizing and improving the use of electronic social media in the trade in banking services in a difficult period of economic development.