This article presents a comparative analysis of international benchmarking systems and their application to assessing urban livability and citizen engagement. The research examines key global indices – Economist Intelligence Unit (EIU), Mercer, PwC, TUWIEN Smart City Model, MIT Treepedia, and the National League of Cities (NLC) – to identify dominant trends and classification models of city benchmarking. Four major types of benchmarking practices were defined: multi-factor indices, single-indicator rankings, thematic analytical reviews, and diagnostic metrics. An empirical case study of Almaty, Kazakhstan, demonstrates the adaptation of international practices to the local context. Between 2020 and 2023, over 1,500 participatory projects were implemented under the Participatory Budget program, primarily in urban greening, infrastructure, and public safety. The findings show that digital governance platforms (Open Almaty, iKomek, Almaty Urban Center) enhance civic participation but remain limited by unequal digital access. The study concludes that benchmarking serves as an effective governance and evaluation tool for improving urban livability and inclusiveness. The Almaty case illustrates the potential of combining global best practices, data-driven governance, and participatory approaches to foster sustainable urban transformation. © 2025, Kazakh-British Technical University. All rights reserved.

Gene expression analysis has become a key component in understanding cellular behavior, disease mechanisms, and drug response. The advent of high-throughput sequencing, particularly single-cell RNA sequencing (scRNA- seq), has expanded our ability to study cellular heterogeneity to an unprecedented level. Clustering algorithms needed to group genes or cells with similar expression profiles have become invaluable for analyzing the massive data sets generated by these technologies. This article reviews various clustering methods applied to gene expression data, particularly single-cell RNA sequencing. The analysis covers traditional methods such as hierarchical clustering and k-means, as well as more advanced approaches such as model-based clustering, machine learning-based methods, and deep learning methods. The primary challenges encompass handling high-dimensional data, mitigating noise, and achieving scalability for large datasets. Moreover, new advancements such as multi-omics data integration, deep learning-based clustering, and federated learning offer potential enhancements in accuracy and biological relevance for clustering applications in gene expression research. The review concludes with a discussion of clustering algorithms in handling increasingly complex gene expression data for more accurate biological insights.

With the rapid development of big data and internet technology, big data financial platform companies collect and organize massive data through their own platforms, improve credit scoring parameters, and use machine learning methods to conduct comprehensive and scientific credit scoring assessments. Thus, banks in the construction of credit scoring face big problems. Based on the limitations of the existing system and methods of personal credit score, it is necessary to study personal credit score based on machine learning methods, improve the parameters and scoring system of personal credit score, clarify data collection channels, and use dynamic desensitization technology to desensitize data, LOF test method to test outliers' data and a random forest method to complete missing data values. You then use the gradient boosting decision tree method to view the important indicators, process the tested indicators with a scorecard model based on logistic regression, and derive a personal credit score. Finally, the model is tested with a BP neural network and the model is used to predict the level of personal credit. The study shows that machine learning can further improve the accuracy of individuals' credit ratings and provide the scientific basis and reference for credit ratings of commercial banks.

The object of the study is the text classification and semantic search tailored to the unique linguistic features of the Kazakh language. The research addresses the challenge of improving the accuracy, relevance, and efficiency of semantic search. This study focuses on improving semantic search for the Kazakh language by analyzing computational models tailored to its unique linguistic features, such as agglutinative morphology and rich inflectional systems. The research compares traditional rule-based approaches and advanced transformer architectures, including fine-tuned models like RoBERTa, for their ability to handle semantic nuances, contextual relationships, and user intent. The results reveal that fine-tuned transformer models achieved significant advancements, with the RoBERTa model attaining a Precision@10 of 89.4 %, a Mean Reciprocal Rank (MRR) of 85.6 %, and an F1-Score of 88.0 %. Additionally, the semantic search system developed in this study demonstrated a precision of 88.4 %, recall of 87.6 %, and an F1-score of 88.0 % on a domain-specific Kazakh dataset. Key to these improvements were innovations in preprocessing pipelines, including custom tokenization and lemmatization tailored to Kazakh's agglutinative morphology, and the integration of contextual embeddings to resolve issues such as synonymy and homonymy. Computational efficiency was enhanced through resource optimization techniques, enabling the deployment of these advanced models in constrained environments. These findings underscore the potential of tailored transformer models to bridge the gap in semantic search capabilities for underrepresented languages like Kazakh, advancing the inclusivity of natural language processing technologies

A composite intelligence scoring framework for identifying high-potential individuals using multi-metric predictive models

Background: The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods: An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion: The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

Разработана интеллектуальная система непрерывного контроля реологических параметров буровых растворов на основе автоматического измерения плотности и методов искусственного интеллекта. Проведены лабораторные испытания и сравнительный анализ с эталонным вискозиметром Fann 35 при температуре 80 °C. Разработанная система обеспечивает измерения с погрешностью не более 2,5%, соответствующей требованиям API RP 13B-1, и выявляет аномальные изменения параметров менее чем за 30 секунд. Полученные результаты подтверждают эффективность предложенного подхода для повышения оперативности контроля, снижения влияния человеческого фактора и интеграции в цифровые системы управления бурением.

В статье рассматривается решение прямой задачи гравиразведки для интерпретации данных гравиметрического мониторинга состояния недр на нефтегазовых месторождениях. Для оценки геодинамических рисков, возникающих при длительной эксплуатации залежей углеводородов, предложено использование метода имитации отжига в рамках стохастической оптимизации. Подход реализован на моделях горизонтальной призмы, однородной сферы и вертикального уступа. Выполнено тестирование алгоритма на реальных данных гравиметрических наблюдений, полученных на месторождении в Атырауской области Казахстана. Результаты показали приемлемую точность и устойчивость метода, а также возможность повышения достоверности геологической модели и сокращения времени обработки гравиметрических данных.

В статье рассматриваются вопросы применения комплекса геофизических методов для обнаружения и картирования перспективных зон прожилково-вкрапленной минерализации на участке Жасылтюбе. На основе анализа геоэлектрических, магнитометрических и других геофизических параметров авторами обоснована оптимальная методика поисковых работ. Выявлены основные закономерности распределения аномальных зон, ассоциирующих с оруденением, и дана оценка их структурно-морфологических особенностей. Полученные результаты позволяют локализовать поисковые мишени и оптимизировать заложение горно-буровых выработок на исследуемой площади.

В статье представлены результаты высокоточной аэромагнитной съемки вдоль Шу-Сарысуйского геотраверса. Выполнена интерпретация магнитного поля для изучения глубинного строения бассейна, выделения разломных зон, картирования структур фундамента и оценки перспектив нефтегазоносности территории. Показана эффективность аэромагнитных исследований при региональном геологическом изучении газоносных бассейнов