Creating a barrier-free environment that takes into account the needs of the low-mobility part of the population is a constant task in various areas of personal and public life organization. The emergence of mandatory design and construction standards has ensured significant progress in this area. The demand for a barrier-free environment in apartments where families without disabilities live is constantly growing. A barrier-free environment becomes a criterion for the convenience of living in general. The inclusive design that ensures this quality of the home offers various solutions. One of the unresolved problems so far is the possibility of wheelchair users living on different floors of multi-storey buildings. This is due to the difficulty of their evacuation in case of emergency shutdown of elevators. An option to solve the problem is the installation of car ramps and apartment parking lots. These ramps, under normal conditions, provide highly comfortable automobile delivery of a representative of low-mobility groups of the population directly to the apartment doors. In the event of an accident, these ramps can be used freely, without creating a queue, to ensure the descent of an accompanied disabled person or a bedridden patient on special stretchers from any floor of an apartment building.

A review of the results of seismic surveys on the territory of Kazakhstan’s part of the Caspian Depression showed high efficiency of seismic exploration in the study of the subsalt sediments and assessing their oil and gas potential. Seismic exploration in conjunction with parametric drilling and clarified the geological structure of the pre-salt strata, outline border spread of major oil and gas complexes and exploration targets. The study of the subsalt sediments on the eastern side of the Caspian Depression has successfully completed the opening in the region of a number of large and small oil and gas fields. Actively introduced in recent years, the method of 3D seismic has shown itself a powerful tool for detailed study of the geological structure of the deposit, the evaluation of hydrocarbon reserves and the development of oil and gas fields. © National Academy of Sciences of the Republic of Kazakhstan, 2016.

Urban residents and especially children have limited contact with nature and are not sufficiently informed about the main problems of its protection and preservation. One of the aspects of increasing the environmental awareness of urban children is to familiarize them with the specifics of agricultural production. A significant problem in organizing agricultural-themed children’s gaming complexes is the creation of an appropriate infrastructure for exhibiting natural and agricultural processes and servicing visitors. One of the possible solutions to this problem is the use of used sea containers for facilities of educational and gaming infrastructure. An important characteristic of such containers is the ability to use them both individually and in various quantitative and combinatorial ways, forming the buildings necessary for exhibiting and servicing. The use of combined container groups makes it possible to organize appropriate temporary or permanent educational and gaming complexes in free territories within cities or suburbs, bringing them as close to consumers as possible. The children’s educational and game complex with agricultural themes is a set of organizationally and spatially interconnected buildings and structures that ensure the display of agricultural production processes based on the active participation of visitors.

This paper introduces an enhanced YOLOv11 model for accurate and efficient weed detection in precision agriculture. Our improvements to YOLOv11n include integrating SPD-Conv for faster speed and better multi-scale fusion, embedding the SCSA attention mechanism in Bottlenecks to mitigate occlusion-related misses, and utilizing the CIoU loss for an improved precision-recall balance. Leveraging the CottonWeedDet12 dataset augmented with random transformations, our model achieves a 1% higher mAP than YOLOv11n and outperforms counterparts like YOLOv5/7/8. It demonstrates robust performance across varying lighting, occlusion, and complex soil conditions, offering a viable solution for smart farming applications.

The paper provides an overview of the anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities. Main objective is to provide a comprehensive understanding of the various methodologies, algorithms, and frameworks that have been developed to enhance the security and reliability of software systems. In paper determines early research in anomaly detection techniques from 2019 and followed methods, approaches and frameworks until recent times. According to research are defined challenges such as high computational costs, false positives, and the need for large, labeled datasets persist. The work specifies ML methods and calculates classic classification metrics such as accuracy, F1 score, precision, recall, geometric mean, etc. This review underscores the importance of continuous advancements in anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities.

This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The proposed model consists of two discrete components: a traditional credit scoring module that employs logistic regression and a supplementary sales analytics module that leverages ensemble machine learning methodologies — random forests and gradient boosting algorithms. The outputs generated by these components are amalgamated through an ensemble strategy, where optimal weighting coefficients are ascertained via cross-validation. An empirical analysis was conducted on a dataset encompassing 41,000 SME records from a prominent Kazakhstan bank alongside daily transactional sales data from 150 SMEs gathered between the years 2021 and 2024. The integrated hybrid model demonstrated a statistically meaningful enhancement in predictive efficacy, as evidenced by an increase in the area under the ROC curve from 0.76 to 0.87 and a decrease in mean squared error from 0.12 to 0.08 relative to the traditional methodology. The investigation delves into the transformative influence of digitalization on innovation within SMEs, elucidating that improved real-time data integration not only sharpens risk assessment processes but also promotes adaptive lending strategies and operational efficiencies.

This paper introduces a hybrid credit assessment methodology tailored specifically to the unique financial, informational, and operational challenges faced by importers. Importers operate at the intersection of cross-border financial risk, fluctuating supply chains, and opaque transactional histories - factors that challenge conventional credit models. While banks rely on structured financial records and established credit histories, P2P platforms harness alternative data and decentralized trust mechanisms such a peer feedback and real-time behavioral signals. Rather than merely merging technologies, the proposed approach integrates differing capabilities: institutional data interpretation dynamical risk modeling, and platform-based investor participation. This synthesis enables a more adaptive, inclusive, and context-aware assessment process. Core components include continuous data validation, alternative trade-data integration, machine learning-driven decision support, and modular feedback loops from lender communities. The methodology is evaluated through a structured literature review and illustrative case applications. By focusing on the informational volatility and operational complexity unique to import-driven businesses, this research contributes a targeted solution to a global financing gap - advancing both credit innovation and trade accessibility.

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