Abstract. Noise pollution has become a primary concern, as it often disrupts a person’s activities or lifestyle balance. The work is devoted to the analysis of the regulatory framework in the field of the introduction of standards for the maximum permissible levels of sound and ultrasonic signals. The relevance of the work is related to the lack of a comprehensive study of the issues of regulatory support for the measurement of ultrasonic signals, as well as the procedure for identifying sources of ultrasound dangerous to human health. The subject of the study is the regulatory framework and standards for measuring the levels of sound and ultrasonic signals. Ultrasound processing is attracting more and more attention from people, as ultrasound technology can provide a flexible “green” alternative for energy-efficient processes, but powerful sources of ultrasound are harmful to human health. They are especially dangerous because a person may not know or feel about the effects of ultrasound sources on him. The objectives of the study were to identify differences in acceptable signal levels and the measurement features of these levels. The purpose of the work is to determine the completeness of compliance of Kazakhstani regulatory documents with the International Standard. The maximum permissible standards of sound and ultrasonic pressure, legally established in a number of countries, are given. Millions of people around the world are exposed to potentially dangerous levels of noise, and therefore there is an urgent global need for legislation to adequately protect workers’ hearing health. Attention is drawn to the wide variation in the levels of established norms and the reasons leading to the ambiguity of these values. The fact that there are no regulatory and methodological documents on the identification of sources of powerful ultrasonic signals, whose increased danger lies in their inaudibility to humans, is noted. It is concluded that it is necessary to study ultrasound levels in the cabin and cabin of jet aircraft.
Keywords: acoustics, sound, sound pressure, norm, danger, airplane, standard, ultrasound.
Abstract. This article presents the development of an anti-drone system based on artificial intelligence (AI) for detection, radio-frequency jamming, and destruction of unmanned aerial vehicles (UAVs). Drawing on the analysis of UAV use in modern conflicts (Syria, Ukraine) and national regulations of the Republic of Kazakhstan, system requirements were defined, taking into account real sensor parameters (X-band radar with RCS 0.01 m², IR sensor sensitivity 0.1 K), jamming power (50 W in the 400–6000 MHz range), operational conditions (+50 °C, dust storms), and external factors (weather reducing probabilities by 20%). A probabilistic model with AI coordination (neuro-symbolic approach) was proposed, providing novelty through adaptation to Central Asian conditions. The simulation, implemented using Monte Carlo with 1000 iterations, is published in an open GitHub repository with replication instructions. Modeling shows validated results: detection probability of 95.8%, jamming effectiveness of 54–78%, and destruction probability of 70.7–84.6% for guided and autonomous drones at distances of 5–8 km. Comparative analysis with analogs (“Drone Dome”, “Pantsir-S1”) demonstrates superiority in range (10 km vs. 3.5 km), cost efficiency, and adaptability. Computational complexity analysis (O (1) per drone) and optimization pathways (ML for trajectory prediction, distributed data processing) confirm practical applicability. The system is expected to strengthen Kazakhstan’s defense capacity and reduce dependency on foreign technologies.
Keywords: anti-drone complex, artificial intelligence, probabilistic model, RF suppression, verification, defense capability.
Abstract. The article presents the results of an analysis of regulatory documents governing the application of ultrasonic diagnostic methods for machines and mechanisms, as well as a comparison with current standards in the field of acoustic frequencies. This approach made it possible to identify differences between areas of application and to determine the possibilities of practical use of existing standards in the development of new diagnostic devices. Special attention is given to the review of ultrasonic frequency measuring instruments currently available on the market. It was found that most of these devices have limited functional capabilities and a high cost. For example, some instruments are capable of detecting ultrasonic signals and indicating the direction of their source; however, visualization is limited to displaying a localized point, which does not allow the recording of physical parameters of the signal or the dynamics of its propagation in the medium. These limitations significantly reduce the potential for comprehensive diagnostics. The main objective of the article is to establish requirements for the characteristics of a next-generation device for ultrasonic diagnostics of mechanisms, which should combine the functions of recording frequency parameters and spatial visualization of the ultrasonic field. It is shown that existing devices are focused only on detecting the signal source. The need for developing new-generation instruments is substantiated. Such instruments would enable the visualization of the ultrasonic field in space and ensure more comprehensive diagnostics of machines and mechanisms, including early detection of hidden defects and potential failures. For the aviation industry, meeting these requirements is of particular importance, as it is directly related to ensuring a high level of flight safety.
Keywords: ultrasonic diagnostics, nondestructive testing (NDT), aviation safety, MEMS microphones, technical diagnostics, coustic visualization, instrument requirements, mechanism reliability.
Abstract. This article examines current challenges in occupational safety and health in civil aviation in the Republic of Kazakhstan, including the growth of traffic volumes, increasing number of flights, and increasing complexity of technological processes. This demonstrates that traditional approaches that focus on monitoring violations and mitigating the consequences of incidents are limited in effectiveness and do not systematically prevent industrial risks. This article substantiates the need to transition to a “smart” occupational safety and health management system based on digitalization and intellectualization, the use of predictive analytics, automated monitoring tools, and the creation of a unified database for risk factor analysis. The proposed concept enables a shift from reactive to proactive occupational safety management, which reduces injury rates, improves the reliability of enterprise operations, and optimizes labor-resource utilization. It is emphasized that the implementation of a “smart” occupational safety and health system that complies with international standards will enhance the sustainability and competitiveness of Kazakhstan’s aviation industry and will ensure its adaptation to global trends in technological development and increasing workload.
Keywords: occupational safety, civil aviation, digitalization, predictive analytics, intelligent monitoring, society 5.0.
Abstract. This article focuses on organizational measures to ensure aviation security during the launch of new international routes by Kazakhstani airlines. With the rapid expansion of Kazakhstan’s air transportation network, new destinations are often associated with emerging threats linked to geopolitical conditions, terrorism risks, and stricter requirements for passenger screening. The aim of the study is to identify, systematize, and scientifically substantiate organizational measures for aviation security, and to evaluate their effectiveness using the cases of Kazakhstani airlines Air Astana and SCAT. The methodology is based on the analysis of international (ICAO, EASA, IATA) and national (Civil Aviation Committee of Kazakhstan) regulatory documents, combined with qualitative and quantitative risk assessment methods. The probability (P) and severity (C) of potential threats were assessed using the formula R = P × C. This approach enabled the comparison of international practices and the identification of the most effective measures, including enhanced pre-flight screening, adaptive staff training, and passenger behavior monitoring. The findings show that the most effective measures were integrated passenger flow control (93%) and systematic route risk analysis (90%). Comparative analysis demonstrated the importance of combining international standards with national regulations within a unified security management system. The scientific novelty of the study lies in developing a comprehensive framework for aviation security when introducing new routes. Its practical significance is reflected in the applicability of the results for airlines in planning international operations, reducing security risks, and ensuring compliance with global standards, thus supporting the safe growth of civil aviation in Kazakhstan.
Keywords: aviation security, new routes, airlines of Kazakhstan, inspection, threats, security measures.
Abstract. This study investigates the development and performance evaluation of transformer-based models for the automatic classification of public transportation passenger reviews, aiming to enhance feedback processing while optimizing issue resolution. Efficient handling of passenger feedback is crucial for improving public transportation services, as unresolved complaints or operational inefficiencies can decrease passenger satisfaction and create logistical challenges. Traditional text classification approaches, such as keyword-based methods or classical Machine Learning (ML) algorithms, struggle with multilingual and heterogeneous textual data, particularly in low-resource languages. This study addresses this gap by systematically comparing transformer-based architectures for review classification in Russian and Kazakh, demonstrating their effectiveness in real-world applications. A key contribution of this research lies in evaluating both language-specific and multilingual transformers on passenger-generated feedback, offering insights into their generalization capabilities. Unlike previous studies, which predominantly focus on English-language datasets, this work introduces a newly created, manually labeled dataset covering diverse real-world scenarios in Russian and Kazakh, enabling an objective comparative analysis. Three transformer models DeepPavlov/rubert-base-cased, XLM-RoBERTa-base, and XLM-RoBERTa-large were trained and tested to assess their ability to process complex multilingual input. Experimental results indicate that XLM-RoBERTa-large achieves the highest classification accuracy (90%), particularly for code-mixed and multilingual reviews, whereas rubert-base-cased performs consistently well for Russian-language feedback (87.667%), reinforcing its suitability for monolingual classification tasks. XLM-RoBERTa-base exhibits a balanced trade-off between accuracy and robustness, making it a viable option for heterogeneous review processing (89.5%). Despite their effectiveness, transformer-based models still encounter challenges related to data balancing and the handling of underrepresented classes, particularly in scenarios with uneven language distributions or domain-specific terminology. These findings confirm that transformer models significantly enhance the automation of passenger feedback classification, providing a scalable solution for public transportation providers.
Keywords: Natural language processing, text classification, Transformers, BERT, DeepPavlov, XLM-RoBERTa, passenger feedback review,
Abstract. Among cryptographic algorithms, block encryption algorithms are used to reliably protect confidential information from unauthorized users. Many countries have established their own standards for block encryption algorithms. This, in turn, ensures reliable protection of information. And this is despite the fact that in Kazakhstan, standards for such information protection algorithms have not been approved. There, the creation of block encryption algorithms and the study of their cryptographic strength are always among the most pressing issues. This article presents the results of differential and statistical analysis of the block encryption algorithm «EM Chiper». One of the main methods for studying the strength of block encryption algorithms is statistical analysis of strength. If good mixing and diffusion is successfully implemented during the transformations commonly used in block ciphers, then a high level of cryptographic security of the algorithm can be achieved. To conduct statistical analysis, the algorithm was implemented in software and ciphertexts of various lengths were obtained. According to the conducted research, the statistical analysis of the proposed algorithm showed high results, that is, according to the requirements recommended by NIST, it was determined that the values in column A are greater than the values in C, and the values in B are greater than the values in D. The results of the differential analysis showed that the proposed algorithm has high cryptographic strength. That is, the probability of finding the key from round 16 is 2^(-126). In addition, the article conducted a comparative analysis of the results of the differential analysis of signature algorithms, and found that they showed comparable results with the well-known Camellia 128 and AES 128 algorithms. In future works, a comprehensive study of the cryptographic strength of other algorithms will be conducted, and the results will be presented in the form of an article.
Keywords. Block cipher, cipher, cryptography, key, differential cryptanalysis, statistical analysis, EM Chiper, S-block.
Abstract. In modern medicine, the analysis of texture features of images plays an important role in the diagnosis of various diseases. This study is devoted to the methods of classifying texture features of burn injury images, which allow identifying informative characteristics and increasing the accuracy of diagnosis. Modern machine learning algorithms used for automated image analysis are considered. A comparative analysis of various classification methods is carried out, the most effective approaches to processing texture features are identified. The results of the study can be used to develop intelligent systems to support medical decision-making. The study used 1,500 burn images (500 for each class) obtained from open sources Roboflow, Kaggle. Texture features extracted using MaZda software were used for further classification. Initially, 279 features were extracted, from which 21 most significant features were selected using 6 selection methods: ANOVA, Fisher, Relief, SBS, SFS and RFE. A review of current approaches to automated burns analysis, including image processing and machine learning methods, published in the last five years is performed. The work demonstrates the promising application of machine learning in medicine and the need for further research to improve classification accuracy and practical implementation of the developed algorithms.
Keywords: texture features, MaZda, automated burn analysis, medical diagnosis, image processing, burn classification.
Abstract. The development of information technology continues to highlight the importance of ensuring the security of information resources. The increasing number of various types of information threats complicates the detection of attacks. The objective of the study is to apply artificial intelligence methods for attack detection while minimizing the number of traffic features to achieve the required detection quality. To train AI, it is necessary to create a high-quality dataset that allows for the accurate identification of attack features in network traffic. The proposed approach uses AI trained on the UNSW-NB 15 dataset, which includes nine types of network attacks: Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms. For implementation, Python is used with the Pytorch and Pandas libraries for data processing. An analysis of the software module’s performance was conducted, along with the application of binary evaluation methods such as the Kappa Coefficient and the Jaccard Index. The effectiveness of the proposed AI model is evaluated using classification metrics: Accuracy, Precision, Recall, and F1 Score. Testing of the developed model with different sets of features revealed that the model achieves high-quality prediction of anomalous traffic when using five selected features. The performance of the AI model was assessed using the Kappa Coefficient and the Jaccard Index. Effective classification thresholds were calculated based on the results, improving the quality of anomalous traffic prediction. The evaluation results show that the developed model, trained on the UNSW-NB 15 dataset, can accurately detect traffic anomalies, thereby contributing to the information security of information resources.
Keywords: network traffic, artificial intelligence, neural networks, attack detection, dataset, UNSW-NB, Kappa Coefficient, Jaccard Index.
Abstract. This document provides a comprehensive overview of the future of cybersecurity through Large Language Models (LLM). We present an overview of the evolution of LLM and its current state, focusing on advances in models such as GPT-4, GPT-3.5, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities such as rapid deployment, insecure output processing, data poisoning, DDoS attacks, and adversarial instructions. We will take a detailed look at mitigation strategies to protect these models, providing a comprehensive overview of potential attack scenarios and methods to prevent them. This analytical data is aimed at improving real-time cybersecurity protection and increasing the complexity of LLM applications for threat detection and response. Our document provides a fundamental understanding and strategic direction for integrating LLM into future cybersecurity systems to protect against evolving cyber threats.
Keywords: LLM, cybersecurity, large language models, language modeling, machine learning, NLP, natural language processing.
Abstract. The proposed article examines methods for modeling semantic relationships of aviation terms using the BERT and RoBERTa language models. The relevance of the study lies in the use of a pre-prepared and annotated corpus of aviation terms that align with international practice and are drawn from documents of international regulatory bodies. The developed language corpus provides the basis necessary for assessing the semantics of aviation terminology in the context of real aircraft operation. The research methodology involves fine-tuning language models trained on an aviation corpus of terms using cosine similarity, rank correlation, and cluster metrics of measurements. The experiments demonstrate the main differences between the two models in tracking synonyms, variability, and shifts in aviation discourse. The results of the study demonstrate that fine-tuning the models enhances their ability to cluster related terms, distinguish closely related but distinct concepts, and align the results with expert judgments. These results provide a methodological basis for the development of aviation terminology resources, enabling the application of lexicography transformer models and ontology construction.
Keywords: semantic proximity, aviation terminology, language models, corpus linguistics, transformers, embedding, natural language processing.
Abstract. The article presents an intelligent information system (IIS) developed to automate the formation of competencies and learning outcomes based on professional standards. A distinctive feature of the IIS is the integration of professional standards with the Atlas of New Professions, which allows adapting educational programs to the dynamically changing requirements of the labor market and technological transformations. The key functional capabilities of the system are described, including user authentication, generation of competencies and learning outcomes for the design of educational programs. The implementation of the system includes an interactive JavaScript interface with support for asynchronous sending of requests to the server using AJAX technologies. OpenAI generative models are used to automatically generate competencies and learning outcomes. The presented system has a wide range of potential applications: from designing curricula based on competencies to creating career guidance systems, analyzing and forecasting changes in the labor market, as well as adapting educational programs to the requirements of high-tech industries. Thus, the developed model contributes to the digitalization of education, improving its quality and ensuring that educational standards meet the modern challenges of the knowledge economy.
Keywords: Competency management, Learning outcomes, Curriculum development, Intelligent information system, Professional standards, Atlas of New Professions.