Tourist reviews provide essential insights into travellers experiences and public perceptions of destinations. In Kazakhstan, however, sentiment analysis, particularly using ensemble learning, remains underexplored for evaluating such reviews. This study proposes a hybrid stacking ensemble for sentiment analysis of English-language tourist reviews about Kazakhstan, integrating four complementary approaches: VADER, TextBlob, Stanza, and Local Context Focus Mechanism with Bidirectional Encoder Representations from Transformers (LCF-BERT). Each model contributes distinct analytical capabilities, including lexicon-based polarity detection, rule-based subjectivity evaluation, generalised star-rating estimation, and contextual aspect-oriented sentiment classification. The evaluation utilised a cleaned dataset of 11,454 TripAdvisor reviews collected between February 2022 and June 2025. The ensemble aggregates model outputs through majority and weighted voting strategies to enhance robustness. Experimental results (accuracy 0.891, precision 0.838, recall 0.891, and F1-score 0.852) demonstrate that the proposed method KazSATR outperforms individual models in overall classification accuracy and exhibits superior capacity for aspect-level sentiment detection. These findings underscore the potential of the hybrid ensemble as a practical and scalable tool for the tourism sector in Kazakhstan. By leveraging multiple analytical paradigms, the model enables tourism professionals and policymakers to better understand traveller preferences, identify service strengths and weaknesses, and inform strategic decision-making. The proposed approach contributes to advancing sentiment analysis applications in tourism research, particularly in underrepresented geographic contexts.

Structural calculation scheme of the hydropulse device for vibration cutting with built-in ring with pressure pulse generator (PPG) is considered. On the basis of the structural scheme and cyclogram of the working cycle of the device, its dynamic and mathematical models were developed, in which the hydraulic link is represented by a visco-elastic model of the working fluid (energy carrier) composed of the inertial elastic and dissipative elements (Kelvin-Foyga's body).

This paper reports a systematic study of low-rank adaptation (LoRA)-based fine-tuning applied to Qwen3 language models (4B, 8B, and 14B parameters) for the task of legal question answering within the jurisdiction of the Republic of Kazakhstan. The bilingual dataset comprises 63,114 question–answer pairs (76.2% Russian, 23.8% Kazakh) covering 11 legal domains. Models are evaluated through both automated metrics (BERTScore, citation accuracy, and hallucination rate) and blind expert assessment by a panel of two practising legal experts. Key findings: (1) all fine-tuned models reach BERTScore F1 close to 90% (89.6–90.2%) versus 82.2–83.1% for untuned base models; (2) fine-tuned models outperform GPT-4o (87.2%) and GPT-4o-mini (86.7%) on semantic similarity while exhibiting far lower hallucination rates (27–29% vs. 83–90%); (3) blind expert assessment confirms the advantage of fine-tuned models, with panel mean completeness scores of 4.28/5 versus 1.95/5 for base models (quadratically weighted Cohen’s κ = 0.80–0.95 across rating dimensions, indicating substantial to almost perfect inter-rater agreement); and (4) we identify a practical scaling plateau: paired Wilcoxon tests (n = 500) detect statistically significant but practically small differences across the 4B, 8B, and 14B fine-tuned variants (largest mean gap 0.67 pp on BERTScore F1; Cohen’s |d_z| ≤ 0.34), gains too small to justify the 3.5× parameter increase. These findings show that parameter-efficient adaptation of compact open-source models can match or exceed commercial LLMs for specialised legal QA in a low-resource bilingual context. We note one scope restriction: three domains (administrative, criminal, and housing law) are represented only in Russian, so the model is not validated for Kazakh language queries in these areas.

In this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Air Pollution Forecasting in Almaty using Ensemble Machine Learning Models

The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved.