Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.

The article is devoted to the study of vegetation indices for assessing the productivity of agricultural crops of the North Kazakhstan Agricultural Experimental Station (NKAES) LLP. The research was carried out using a modern software package for processing satellite images, EOS Land Viewer. The work used images from the Landsat 8 (USA) and Sentitel 2 (European Space Agency) spacecraft. Digitized Earth remote sensing data for the last 3 years are presented, showing changes in the amount of moisture reserves on the territory of NKAES LLP. Time series of distribution of the studied coefficients were constructed according to different phases of active vegetation biomass in the study area. The resulting time series made it possible to identify annually repeating patterns, a linear trend of increasing and decreasing NDWI and NDVI on the territory of the NKAES LLP. Review of studies over the past 5 years, published in highly rated foreign journals, on various vegetation indices, including indices designed to assess moisture content in vegetation and soil. It is noted that the first normalized water index, NDWI, using the SWIR infrared channel, unlike the widely used NDVI vegetation index, actually penetrates 80% of the atmosphere. Analysis of the obtained NDWI allowed us to identify dry, moderately dry and fairly humid periods on the territory of the NKAES LPP from 2020 to 2023. Based on the research carried out, the feasibility of using normalized difference water indicators and normalized vegetation indices for further use in forecasting yields in the conditions of the North Kazakhstan region is substantiated. Next, using vegetation indices and additional agrometeorological factors, a linear model for predicting crop yields was developed. The coefficient of determination of the resulting model is 0.90 which indicates that the selected trend line reliably approximates the process under study.

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.

This study investigates the application of Hierarchical clustering for image segmentation, with a focus on its efficacy in analyzing medical images, particularly MRI scans of brain tumors. Image segmentation plays a pivotal role in computer vision, facilitating various applications across industries. Leveraging a systematic approach, we conduct a comprehensive review of recent literature on machine learning algorithms for image segmentation. Subsequently, utilizing a dataset comprising MRI images with and without tumors, we preprocess and analyze the data using the Histogram of Oriented Gradients (HOG) technique to extract pertinent features. These features serve as input for the Hierarchical clustering algorithm to partition the images into distinct regions of interest. For each row of vectors, the Jensen-Shenton distance was calculated. The resulting symmetric matrices are distances among the corresponding vectors, quantifying dissimilarity in cluster analysis. Our findings underscore the effectiveness of Hierarchical clustering in clustering medical images, with potential implications for advancing computational analysis in healthcare and related domains.

Purpose: The problem of ethnic conflicts and unrest is at the forefront in a diverse world today. This paper aims to identify ways of resolving social conflicts and establishing a balanced way of taking into account the diverse interests of a multinational society. Design/methodology/approach: Modeling of dynamical ethnic processes in the Republic of Kazakhstan was based on the Parson's sociological scheme and the Nash equilibrium theory. The model consists of differential equations describing the development of four ethnic subsystems: political system, economic system, societal community and Institute of the Assembly of the People of Kazakhstan. This model allows investigating how the interests of various ethnic groups change over time and identifying the states of equilibrium in which the interests of all groups are satisfied. Findings: The results of computer simulation showed that one of the solutions to the problem of social stability is establishing social equilibrium. For this, the Institute of the Assembly of the People of Kazakhstan (APK Institute) must take changes that occur within ethnic groups into account. The proposed model can reveal states of equilibrium with respect to positive and negative dynamic processes that exist between different ethnic groups. Originality/value: The proposed model can be used to predict changes in social behavior and find balance between ethnic subsystems in the research on ethnic processes in multinational countries to early detect conflicts of interest and crisis situations. Future studies will benefit from expanding the range of subsystems that can affect alterations in the ethnic community.