This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses.

This study delves into the emerging opportunities and challenges arising from the integration of education and artificial intelligence in the unique backdrop of the COVID-19 pandemic. Its primary objective is to develop an optimized ensemble model that sheds light on the surge in learning engagement among secondary school students during Emergency Distance Learning (EDL) amid the pandemic. To achieve this, we explored three distinct methodologies: the k-Nearest Neighbor method (KNN), Random Forest (RF), and Gradient Boosting (XGB). Our approach involved constructing an ensemble model that synthesized the strengths and weaknesses of these individual models based on their training outcomes. In contrast to prevailing beliefs that Emergency Distance Learning (EDL) negatively impacts education, our study's findings underscore a positive upswing in students' learning activity during EDL. Furthermore, our ensemble model effectively identifies the underlying reasons behind this increased engagement, achieving an impressive overall accuracy rate of 87% in processing the survey responses. Our research encompassed a comprehensive sample, targeting 35,950 secondary school students from 16 regions and cities of significant importance within Kazakhstan. This diverse sample included students from urban, rural, and small schools, providing a well-rounded perspective on territorial affiliation. Data collection was conducted through an online survey using a methodologically verified structured questionnaire.

This paper presents the design and validation of a modular interface architecture for automated testing of IoT devices. The proposed system integrates asyn

In the COVID-19 pandemic, the education system has faced unprecedented challenges requiring adaptation and implementation of new teaching methods and data analysis. This article discusses the use of ensemble machine learning models to predict student learning activity in distance education. The data on student engagement collected through the questionnaire is analyzed, and forecasting models are built based on these data. The study revealed that the combination of random forest (RF), k-nearest neighbor (KNN) algorithms and the gradient bootstrap method (XGBoost) demonstrates high accuracy and reliability of forecasts. The results of the study show that ensemble models can be effectively used to analyze educational data and make informed decisions in crisis situations. The research uses data from a survey conducted by the National Academy of Education named after I. Altynsarin in 2020 with the participation of 39,450 students from schools in the Republic of Kazakhstan.

In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed.