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 article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.

Ensuring that students with special needs learn on an equal basis as other students and participate fully in the educational process has become a global priority within inclusive education. The main goal of this movement is to introduce an inclusive pedagogical approach to the educational process that takes into account the special needs of students. For this reason, there is an increasing need for effective teaching methods used in sustainable and inclusive education. This need determined the purpose of the study. The purpose of this study is to investigate the impact of an adapted learning environment based on artificial intelligence on the academic performance and motivation of students with special needs. A mixed-methods approach combining quantitative and qualitative data was employed. Quantitative data were analyzed using ANOVA, RM-ANOVA, and ANCOVA methods. In addition, the Pearson correlation analysis was conducted to examine the relationship between learning achievement and motivation, and the Cronbach alpha was used to assess the reliability of measuring instruments. Qualitative data was collected through interviews and analyzed using thematic analysis. The findings suggested that the AI-based adaptive learning environment was associated with improvements in students’ academic performance and learning motivation. The interaction between time and group factors indicated that the experimental group demonstrated greater improvements compared to the control group. However, the main influence of the group has not reached statistical significance; therefore, in future studies it is recommended to increase the sample size and duration of the experiment. Qualitative analysis revealed that adapted materials supported the understanding of complex topics, but some participants faced technical difficulties and difficulties in adapting to the new format. In addition, since the thematic analysis was carried out by one researcher, it is recommended to involve several encoders in the future. In conclusion, the study suggests that AI-based adaptive learning environments may contribute to improving the academic performance and motivation of students with special educational needs. This contributes to the development of sustainable and inclusive education.

This paper examines the application of machine learning methods for automatic text processing of abstracts from scientific articles. With the increasing volume of scientific information, researchers are faced with the problem of information overload, which makes it difficult to find and analyze relevant materials. To solve this problem, we are implementing machine learning algorithms such as the Support vector Machine (SVM) method and word representation using Word2Vec, which allows us to effectively classify annotations and extract key information. In the process, we collect data from open databases. Annotations go through preprocessing stages, including tokenization, lemmatization, and deletion of stop words. Then we use Word2Vec to convert annotation texts into vector representations, which serve as input data for the SVM model. The effectiveness of the models is evaluated using accuracy, completeness and F1-measure metrics. The results show that the integration of SVM and Word2Vec significantly improves the quality of annotation classification, which makes it possible to speed up the process of searching for scientific information. The work highlights the potential of using machine learning methods to automate the processing of scientific texts and suggests areas for further research, including the use of more complex models such as transformers. This methodology can become the basis for the development of effective tools that facilitate faster knowledge sharing in the scientific community.

This article presents a hybrid machine learning model designed for soil type classification based on the analysis of geophysical characteristics. The proposed model combines two algorithms – RandomForestClassifier and MLPClassifier – integrating the high accuracy of ensemble methods with the ability of neural networks to capture complex nonlinear dependencies between parameters. The input dataset included indicators such as electrical conductivity, density, P-wave propagation velocity, and burial depth. Prior to training, data preprocessing was performed, including outlier removal, standardization, and categorical feature encoding. The hybrid architecture allowed the integration of results from both models with different weights, optimizing classification accuracy. The effectiveness of the proposed approach was compared with alternative algorithms such as XGBoost and Keras using metrics including Accuracy, F1-score, Precision, and Recall. The hybrid model achieved an accuracy of 96.07%, outperforming individual algorithms. Visualization of confusion matrices provided insights into class distribution and model robustness. The results confirm that combining ensemble and neural methods ensures more stable and reliable predictions when working with geophysical data. The developed model can be effectively applied in geotechnical studies, construction, agriculture, and environmental monitoring, enhancing analytical efficiency and reducing the need for costly laboratory testing.

The article discusses the basic requirements for a distributed information system and the management of limited user access. Distributed information systems (RIS) play an important role in modern organizations, ensuring efficient processing, storage and transfer of data between various elements of the system. The focus is on security, data protection and access control issues, which are key aspects to prevent unauthorized access to confidential information. The methods of authentication, authorization and distribution of access rights depending on user roles, as well as approaches to monitoring and controlling activity in the system are described.This article solves the problem of developing technology to support large information repositories and the organization of delimited user access to this information, providing a service for both managing these objects and organizing access to these objects. Solving the problem will allow you to create a conceptual model with the allocation of basic entities among information objects and the establishment of links between them. It will also allow the development of technical documentation reflecting the results of the first stage of the information system creation: solving problems of syntactic and technical interoperability, developing a single interface, interacting with users, etc.