Aigul Adamova.
DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS
// Scientific Journal of Astana IT University. V.19. - Astana. September, 2024. P. 16-27. - ISSN 2707-904X.

Авторы: Aigul Adamova, Tamara Zhukabayeva

Аннотация: The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

MODELING OF BLOOD FLOW IN LAMINAR MODE
// Scientific Journal of Astana IT University. V.19. - Astana. September, 2024. P. 5-15. - ISSN 2707-904X.

Авторы: Sultan Alpar, Fatima Tokmukhamedova, Bakhyt Alipova, Yevgeniya Daineko, Nazerke Rysbek, Diyar Abdrakhman

Аннотация: This article presents a detailed analytical evaluation and comprehensive description of a mathematical model designed to simulate blood flow within the human cardiovascular system. The primary objective of this research is to develop a computational model capable of accurately simulating blood flow dynamics and to assess the variations in results using different numerical methods for solving the Navier-Stokes equations, which govern fluid motion. To achieve this, the study begins with an in-depth examination of the anatomy of the cardiovascular system, including various cardiovascular diseases such as stenosis and atherosclerosis, which significantly affect blood flow. The model incorporates important characteristics of blood, treating it as a viscous fluid under laminar flow conditions. Using the Navier-Stokes equations, it was developed a Python-based model to simulate these flow conditions and solve for different flow variables, such as velocity and pressure fields, under both normal and pathological conditions. The computational model was developed using two numerical methods: the Euler method and the Alternating Direction Implicit (ADI) method, which were compared in terms of their computational efficiency and accuracy. The simulations generated insights into how plaque buildup (stenosis) affects blood flow by altering wall shear stress and velocity profiles. This model, while built on foundational fluid dynamics principles, serves as an essential step towards creating a virtual reality (VR) surgical simulator for cardiovascular procedures. This simulator aims to assist surgeons in visualizing and planning surgical interventions by providing an interactive and realistic environment for studying blood flow and related complications.

METHODOLOGY FOR ASSESSING THE LEVEL OF METHODOLOGICAL COMPETENCE OF IT TEACHERS
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 120-130. - ISSN 2707-904X.

Авторы: Sapar Toxanov*, Dilara Abzhanova, Aidos Mukhatayev, Andrii Biloshchytskyi, Khanat Kassenov*

Аннотация: The article presents the results and analysis of an experimental study on the development of methodological competence of teachers of IT disciplines in the framework of their advanced training on the online. The purpose of the article is to disseminate certain results of the study from the point of view of theoretical and methodological aspects. The task of the experiment stage was to diagnose the level of formation of methodological competence of teachers of IT disciplines of the control and experimental groups according to four key criteria of methodological competence: didactic, design, monitoring and personal. The determination of the level of formation of methodological competence skills was made using five levels: advanced, high, medium, acceptable and low, for each key criterion of the teacher’s methodological competence. The experimental part of the study was conducted with teachers of IT disciplines of universities in Kazakhstan during the 2023-2024 academic year. As a result of the experimental work, a diagnosis of the development of methodological competence of teachers of IT disciplines was developed, which is of a complex nature: substantive – in the form of tests, tests of various nature on the content of course training; activity – in the form of assessment for each type of practical work of a methodological orientation (drawing up work programs for the subject, technological map of the lesson, problem tasks, etc.) ; communicative – on the basis of questionnaires, tests, analysis and self-analysis (on the development of speech culture, behavior in society, skills to organize attention, students’ activities, work in pairs and groups, etc.). In the formation of a set of diagnostic methods, the emphasis was placed on methods based on self-diagnosis, which contributes to the process of self-knowledge and self-analysis of a particular person. The authors also note that despite the variety of definitions, all studies have a common semantic basis - correlation with the labor market and social policy of society. These two parameters play an important role in determining the content of competencies, based on the psychological and biological characteristics of the individual. In the course of the study, the validity of the methods used was checked, and the results were presented, which indicate the need to develop recommendations aimed at improving the level of methodological competence of teachers of IT disciplines.

STATISTICAL PROPERTIES OF THE PSEUDORANDOM SEQUENCE GENERATION ALGORITHM
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 107-119. - ISSN 2707-904X.

Авторы: Ardabek Khompysh, Kunbolat Algazy*, Nursulu Kapalova , Kairat Sakan , Dilmukhanbet Dyusenbayev

Аннотация: One of the most important issues in the design of cryptographic algorithms is studying their cryptographic strength. Among the factors determining the reliability of cryptographic algorithms, a good pseudorandom sequence generator, which is used for key generation, holds particular significance. The main goal of this work is to verify the normal distribution of pseudorandom sequences obtained using the generation algorithm and demonstrate that there is no mutual statistical correlation between the values of the resulting sequence. If these requirements are met, we will consider such a generator reliable. This article describes the pseudorandom sequence generation algorithm and outlines the steps for each operation involved in this algorithm. To verify the properties of the pseudorandom sequence generated by the proposed algorithm, it was programmatically implemented in the Microsoft Visual C++ integrated development environment. To assess the statistical security of the pseudorandom sequence generation algorithm, 1000 files with a block length of 10000 bits and an initial data length of 256 bits were selected. Statistical analysis was conducted using tests by D. Knuth and NIST. As shown in the works of researchers, the pseudorandom sequence generation algorithm, verified by these tests, can be considered among the reliable algorithms. The results of each graphical test by D. Knuth are presented separately. The graphical tests were evaluated using values obtained from each test, while the chi-squared criterion with degrees of freedom 2k – 1 was used to analyze the evaluation tests. The success or failure of the test was determined using a program developed by the Information Security Laboratory. Analysis of the data from the D. Knuth tests showed good results. In the NIST tests, the P-value for the selected sequence was calculated, and corresponding evaluations were made. The output data obtained from the NIST tests also showed very good results. The proposed pseudorandom sequence generation algorithm allows generating and selecting a high-quality pseudorandom sequence of a specified length for use in the field of information security

DEVELOPMENT OF AEROSPACE IMAGES PRELIMINARY PROCESSING METHOD FOR SUBSEQUENT RECOGNITION AND IDENTIFICATION OF VARIOUS OBJECTS
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 96-106. - ISSN 2707-904X.

Авторы: Assiya Sarinova, Alexandr Neftissov , Leyla Rzayeva , Alimzhan Yessenov , Lalita Kirichenko , Ilyas Kazambayev

Аннотация: Nowadays, the application of hyperspectral images is vital for every section of the humanity life such as agrotechnical research for the field condition state and water security. This article presents a new lossless data compression algorithm focused on the processing of hyperspectral aerospace images. The algorithm takes into account inter-band correlation and difference transformations to effectively reduce the range of initial values. correlation allows you to find the best reference channel that defines the sequence of operations in the algorithm, which contributes to a significant increase in the compression ratio while maintaining high data quality. The practical implementation of the algorithm lies in the process of the transfer the lower size file with high efficiency for unmanned aerial vehicle and satellites to save more computational resources. This method demonstrates high computational efficiency and can be applied to various tasks that require efficient storage and transmission of hyperspectral images. The importance of processing hyperspectral data and the problems associated with their volume and complexity of analysis were described. Current approaches to data compression are considered and their limitations are identified, which justifies the need to develop new methods. The relevance and necessity of effective compression algorithms for aerospace applications is emphasized. An analysis of existing methods and algorithms for compressing hyperspectral data was carried out. Particular attention is paid to approaches that use cross-channel correlation and difference transformations. The effectiveness of current methods is evaluated and their shortcomings are identified, which serves as a justification for the development of a new algorithm. A developed lossless data compression algorithm based on the use of inter-band correlation and difference transformations was described. The stages of forming groups of channels and the selection of optimal compression parameters are considered in detail. The method of determining the reference channel, which sets the sequence of operations in the algorithm, which provides more efficient data compression, is especially noted. The advantages and possible limitations of the new approach, as well as its potential for practical use, are discussed. It was noted that the developed method successfully solves the problems associated with the volume of hyperspectral data, providing a high compression ratio without quality loss. The prospects for further development of the algorithm and its application in various fields of science and technology are discussed.

DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 75-95. - ISSN 2707-904X.

Авторы: Samat Mukhanov*, Raissa Uskenbayeva , Abdul Ahmad Rakhim, Young Im Cho, Aknur Yemberdiyeva, Zhansaya Bekaulova

Аннотация: Currently, an increasing amount of research is directed towards solving tasks using computer vision libraries and artificial intelligence tools. Most common are the solutions and approaches utilizing machine and deep learning models of artificial neural networks for recognizing gestures of the Kazakh sign language based on supervised learning methods and deep learning for processing sequential data. The research object is the Kazakh sign language alphabet aimed at facilitating communication for individuals with limited abilities. The research subject comprises machine learning methods and models of artificial neural networks and deep learning for gesture classification and recognition. The research areas encompass Machine Learning, Deep Learning, Neural Networks, and Computer Vision. The main challenge lies in recognizing dynamic hand gestures. In the Kazakh sign language alphabet, there are 42 letters, with 12 of them being dynamic. Processing, capturing, and recognizing gestures in motion, particularly in dynamics, pose a highly complex task. It is imperative to employ modern technologies and unconventional approaches by combining various recognition methods/algorithms to develop and construct a hybrid neural network model for gesture recognition. Gesture recognition is a classification task, which is one of the directions of pattern recognition. The fundamental basis of recognition is the theory of pattern recognition. The paper discusses pattern recognition systems, the environment and application areas of these systems, and the requirements for their development and improvement. It presents tasks such as license plate recognition, facial recognition, and gesture recognition. The field of computer vision in image recognition, specifically hand gestures, is also addressed. The development of software will enable the testing of the trained model’s effectiveness and its application for laboratory purposes, allowing for adjustments to improve the model.

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 66-74. - ISSN 2707-904X.

Авторы: Sapar Toxanov , Saltanat Sharipova* , Andrii Biloshchytsky, Dilara Abzhanova, Batyrbek Bakytkereiuly

Аннотация: This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies

Aksultan Mukhanbet.
OPTIMIZING QUANTUM ALGORITHMS FOR SOLVING THE POISSON EQUATION
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 55-65. - ISSN 2707-904X.

Авторы: Aksultan Mukhanbet , Nurtugan Azatbekuly , Beimbet Daribayev

Аннотация: Contemporary quantum computers open up novel possibilities for tackling intricate problems, encompassing quantum system modeling and solving partial differential equations (PDEs). This paper explores the optimization of quantum algorithms aimed at resolving PDEs, presenting a significant challenge within the realm of computational science. The work delves into the application of the Variational Quantum Eigensolver (VQE) for addressing equations such as Poisson’s equation. It employs a Hamiltonian constructed using a modified Feynman-Kitaev formalism for a VQE, which represents a quantum system and encapsulates information pertaining to the classical system. By optimizing the parameters of the quantum circuit that implements this Hamiltonian, it becomes feasible to achieve minimization, which corresponds to the solution of the original classical system. The modification optimizes quantum circuits by minimizing the cost function associated with the VQE. The efficacy of this approach is demonstrated through the illustrative example of solving the Poisson equation. The prospects for its application to the integration of more generalized PDEs are discussed in detail. This study provides an in-depth analysis of the potential advantages of quantum algorithms in the domain of numerical solutions for the Poisson equation and emphasizes the significance of continued research in this direction. By leveraging quantum computing capabilities, the development of more efficient methodologies for solving these equations is possible, which could significantly transform current computational practices. The findings of this work underscore not only the practical advantages but also the transformative potential of quantum computing in addressing complex PDEs. Moreover, the results obtained highlight the critical need for ongoing research to refine these techniques and extend their applicability to a broader class of PDEs, ultimately paving the way for advancements in various scientific and engineering domains.

Dana Nurgazina.
SCIENTIFIC ASPECTS OF MODERN APPROACHES TO MACHINE TRANSLATION FOR SIGN LANGUAGE
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 41-54. - ISSN 2707-904X.

Авторы: Dana Nurgazina, Saule Kudubayeva, Arman Ismailov

Аннотация: Scientific research in the field of automated sign language translation represents a crucial stage in the development of technologies supporting the hearing-impaired and deaf communities. This article presents a comprehensive approach to addressing semantic and technical challenges associated with the uniqueness of sign language. The research goal is to create an innovative system that combines semantic analysis, sign synthesis, and facial mimicry for the most accurate conveyance of emotional context. The study focuses on the unique features of the Kazakh language and cultural contexts that influence sign communication. The research centers on the development of a semantic system capable of adequately interpreting metaphors, idioms, and classifier predicates of sign language. The three-dimensional nature of signs is analyzed, and a solution to the formal description problem is proposed. The article introduces a database, analysis algorithm, and a prototype 3D avatar capable of translating textual data into sign language. Special attention is given to the processing of idioms and variability in expressing emotions in sign language. Utilizing machine learning principles and computational linguistics algorithms, the authors present an integrated approach to sign language translation, considering linguistic, cultural, and emotional aspects. The proposed algorithms and formulas facilitate effective interaction between textual information and sign expression. The research results not only provide an overview of current challenges in automated sign language translation but also offer practical approaches to addressing them. The developed approach could be a key step towards creating more efficient communication systems for the hearing-impaired and deaf. Which in the future may solve numerous issues with Kazakh sign language.

Kenzhegali Nurgaliyev.
AN EVALUATION METHOD OF ENERGY CONSUMPTION AS AN OPERATION PARAMETER IN A CYBER-PHYSICAL SYSTEM
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 30-40. - ISSN 2707-904X.

Авторы: Kenzhegali Nurgaliyev, Akylbek Tokhmetov , Liliya Tanchenko

Аннотация: The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device’s lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user.

DEVELOPING GAME THEORY-BASED METHODS FOR MODELING INFORMATION CONFRONTATION IN SOCIAL NETWORKS
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 17-29. - ISSN 2707-904X.

Авторы: Damir Moldabayev, Mikhail Suchkov, Zukhra Abdiakhmetova, Amandyk Kartbayev*

Аннотация: This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network’s status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.

EKMGS: A HYBRID CLASS BALANCING METHOD FOR MEDICAL DATA PROCESSING
// Scientific Journal of Astana IT University. V.18. - Nur-Sultan.June, 2024.-P. 5-16. - ISSN 2707-904X.

Авторы: Zholdas Buribayev, Saida Shaikalamova, Ainur Yerkos, Rustem Imanbek

Аннотация: The field of medicine is witnessing rapid development of AI, highlighting the importance of proper data processing. However, when working with medical data, there is a problem of class imbalance, where the amount of data about healthy patients significantly exceeds the amount of data about sick ones. This leads to incorrect classification of the minority class, resulting in inefficient operation of machine learning algorithms. In this study, a hybrid method was developed to address the problem of class imbalance, combining oversampling (GenSMOTE) and undersampling (ENN) algorithms. GenSMOTE used frequency oversampling optimization based on a genetic algorithm, selecting the optimal value using a fitness function. The next stage implemented an ensemble method based on stacking, consisting of three base (k-NN, SVM, LR) and one meta-model (Decision Tree). The hyperparameters of the meta-model were optimized using the GridSearchCV algorithm. During the study, datasets on diabetes, liver diseases, and brain glioma were used. The developed hybrid class balancing method significantly improved the quality of the model: the F1-score increased by 10-75%, and accuracy by 5-30%. Each stage of the hybrid algorithm was visualized using a nonlinear UMAP algorithm. The ensemble method based on stacking, in combination with the hybrid class balancing method, demonstrated high efficiency in solving classification tasks in medicine. This approach can be applied for diagnosing various diseases, which will increase the accuracy and reliability of forecasts. It is planned to expand the application of this approach to large volumes of data and improve the oversampling algorithm using additional capabilities of the genetic algorithm.

Mary Mojirade AYANTUNJI.
ASSESSMENT OF VIRTUAL TEAM TEACHING APPLICATION AMONG PRE-SERVICE TEACHERS IN FEDERAL COLLEGE OF EDUCATION ABEOKUTA
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 122-135. - ISSN 2707-904X.

Авторы: Mary Mojirade AYANTUNJI, Adekunle Emmanuel MAKANJUOLA, John Olalekan ATANDA

Аннотация: A detailed study of pre-service teachers at the Federal College of Education in Abeokuta examines their collaborative skills and virtual team teaching issues. The major goal is to determine how virtual team teaching affects pre-service teachers’ ability to collaborate and navigate its complexities. The study aims to show how virtual team teaching affects pre-service teachers’ problems and collaboration. The underlying hypothesis posits that participants engaged in virtual team teaching will exhibit heightened levels of collaboration and critical thinking skills compared to their counterparts employing conventional teaching methods. To accumulate robust empirical evidence, a meticulous 20-item Likert scale questionnaire was judiciously administered to a representative sample of pre-service teachers at the Federal College of Education Abeokuta. The questionnaire methodically gauged participants’ perceptions regarding the influence of virtual team teaching on collaborative skills and the challenges encountered. In the subsequent analytical phase, the data underwent rigorous scrutiny using descriptive statistics, meticulously assessing the levels of agreement with each questionnaire item. This study’s discerning discoveries make a substantial scientific contribution, propelling our knowledge of how virtual team teaching molds pre-service teachers’ collaboration skills and navigates challenges. Rooted in scientific rigor, these insights bear potential significance for educational institutions and teacher education programs. They furnish a nuanced understanding of the efficacy of virtual team teaching as a transformative pedagogical approach, offering valuable guidance for the optimization of pre-service teachers’ skills to meet the evolving demands of the modern educational landscape

Andrii Biloshchytskyi.
EXPLORATION OF THE THEMATIC CLUSTERING AND COLLABORATION OPPORTUNITIES IN KAZAKHSTANI RESEARCH
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 106-121. - ISSN 2707-904X.

Авторы: Andrii Biloshchytskyi, Malika Shamgunova, Svitlana Biloshchytska

Аннотация: In today’s academic environment, the rapid growth of research publications calls for advanced methods to organize and understand the extensive collections of academic work. This study aims to systematically categorize a substantial number of research paper abstracts from Kazakhstani institutions, focusing on identifying key themes and potential interdisciplinary collaboration opportunities. The dataset includes 13,356 abstracts from the Scopus database, covering a wide range of academic fields. The methodology of this research goes beyond traditional hand-done analysis by using advanced text analysis tools to organize the text data efficiently. This initial phase is crucial for summarizing each abstract’s core content. The next steps of the analysis use this organized data to find and group similar thematic areas, considering the complex and multi-dimensional nature of academic research topics. The results reveal a diverse array of research themes, highlighting the dynamic academic contributions from Kazakhstan. Significant areas such as environmental science, technological advancements, linguistics, and cultural studies are among the prominent clusters identified. These insights not only provide an overview of current research directions but also highlight the potential for cross-disciplinary partnerships. Moreover, the findings have important implications for decision-makers, scholars, and educational institutions by illuminating key research areas and collaborative possibilities. This thematic overview acts as a guide for shaping research policies, fostering academic connections, and efficiently distributing resources within the scholarly community. Ultimately, this study adds to the academic conversation by offering a way to navigate and utilize the wealth of information in scientific literature, promoting a more collaborative and integrated research environment.

USING STRUCTURAL EUATION MODELING METHODS TO ASSESS THE UNIVERSITY’S DIGITAL ECOSYSTEM
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 95-105. - ISSN 2707-904X.

Авторы: Natalya Denissova, Irina Dyomina, Aizhan Tlebaldinova, Ruslan Chettykbayev, Didar Muratuly, Vitaly Zuev

Аннотация: This paper explores the construction of a model for evaluating the digital ecosystem within a university, with a focus on identifying key factors influencing satisfaction with the implementation of new digital processes in the educational environment. The study employs mathematical methods, specifically factor analysis, to gauge the impact of these digital processes on the overall educational landscape. A questionnaire was designed to collect relevant data, and structural equation modeling, utilizing the asymptotically distribution-free estimation method with Grammian in STATISTICA software, was employed for survey result processing. The proposed model aims to provide insights into the dynamics of a university’s digital ecosystem, offering a systematic approach to assess satisfaction levels and comprehend the implications of integrating novel digital processes within the educational framework. Mathematical methods, including factor analysis, add a quantitative dimension to the evaluation process, enabling a comprehensive understanding of the relationships between various factors. The study’s methodology ensures a rigorous and systematic analysis of survey data, enhancing the reliability of the findings. The developed model and methodology contribute to advancing our understanding of the digitalization of university environments, providing valuable tools for decision-makers in shaping effective strategies for integrating digital processes in education. The study conducted a survey with 350 participants, including university staff and students. A questionnaire with 17 questions, both open and closed-ended, was developed to collect data. The authors employed structural equation modeling, specifically the asymptotically distribution-free estimation method, for data processing. The study’s a posteriori model illustrates the structure of interaction factors influencing satisfaction with the university’s digital ecosystem.

CLASSIFICATION OF KAZAKH MUSIC GENRES USING MACHINE LEARNING TECHNIQUES
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 83-94. - ISSN 2707-904X.

Авторы: Aigul Mimenbayeva, Gulmira Bekmagambetova, Gulzhan Muratova, Akgul Naizagarayeva, Tleugaisha Ospanova , Assem Konyrkhanova

Аннотация: This article analysis a Kazakh Music dataset, which consists of 800 audio tracks equally distributed across 5 different genres. The purpose of this research is to classify music genres by using machine learning algorithms Decision Tree Classifier and Logistic regression. Before the classification, the given data was pre-processed, missing or irrelevant data was removed. The given dataset was analyzed using a correlation matrix and data visualization to identify patterns. To reduce the dimension of the original dataset, the PCA method was used while maintaining variance. Several key studies aimed at analyzing and developing machine learning models applied to the classification of musical genres are reviewed. Cumulative explained variance was also plotted, which showed the maximum proportion (90%) of discrete values generated from multiple individual samples taken along the Gaussian curve. A comparison of the decision tree model to a logistic regression showed that for f1 Score Logistic regression produced the best result for classical music – 82%, Decision tree classification – 75%. For other genres, the harmonic mean between precision and recall for the logistic regression model is equal to zero, which means that this model completely fails to classify the genres Zazz, Kazakh Rock, Kazakh hip hop, Kazakh pop music. Using the Decision tree classifier algorithm, the Zazz and Kazakh pop music genres were not recognized, but Kazakh Rock with an accuracy and completeness of 33%. Overall, the proposed model achieves an accuracy of 60% for the Decision Tree Classifier and 70% for the Logistic regression model on the training and validation sets. For uniform classification, the data were balanced and assessed using the cross-validation method. The approach used in this study may be useful in classifying different music genres based on audio data without relying on human listening.

COMPARATIVE ANALYSIS OF FEDERATED MACHINE LEARNING ALGORITHMS
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 68-82. - ISSN 2707-904X.

Авторы: Alua Myrzakerimova, Kateryna Kolesnikova, Iuliia Khlevna, Mugulsum Nurmaganbetova

Аннотация: The application of diagnostic expert systems in medical technology signifies a notable progression, as they provide a computerized framework for decision-support, assisting healthcare practitioners in the process of disease diagnosis. These systems facilitate the integration of patient data, encompassing symptoms and medical history, with a knowledge base in order to produce a comprehensive compilation of potential diagnoses. Through the utilization of knowledge-based methodologies, they enhance these potentialities in order to ascertain the most probable diagnosis. The present study examines expert systems, investigating their historical development, architectural structure, and the approaches utilized for knowledge representation. There is a significant emphasis placed on the advancement and implementation of these systems within the medical industry of Kazakhstan. This paper provides a comprehensive analysis of the benefits and drawbacks associated with diagnostic expert systems, emphasizing their potential to bring about significant advancements in medical fields. The study places significant emphasis on the necessity of developing and conducting thorough testing of these systems in order to improve the precision and effectiveness of medical diagnostics. The statement recognizes the importance of continuous research in order to enhance the design and implementation of these systems in various healthcare settings. This research makes a notable addition by examining optimization theory in the field of medical diagnosis. This study presents novel approaches for effectively addressing the intricacies and uncertainties associated with the diagnosis of complicated disorders. The work presents methodology for navigating the complex field of medical diagnostics by utilizing mathematical modeling and optimization approaches, specifically the gradient projection method. The utilization of diverse ways to tackle qualitative ambiguities in this approach signifies a significant progression inside the domain of diagnostic expert systems

Gulnara Bektemyssova.
COMPARATIVE ANALYSIS OF FEDERATED MACHINE LEARNING ALGORITHMS
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 57-67. - ISSN 2707-904X.

Авторы: Gulnara Bektemyssova, Gulnaz Bakirova

Аннотация: In this paper, the authors propose a new machine learning paradigm, federated machine learning. This method produces accurate predictions without revealing private data. It requires less network traffic, reduces communication costs and enables private learning from device to device. Federated machine learning helps to build models and further the models are moved to the device. Applications are particularly prevalent in healthcare, finance, retail, etc., as regulations make it difficult to share sensitive information. Note that this method creates an opportunity to build models with huge amounts of data by combining multiple databases and devices. There are many algorithms available in this area of machine learning and new ones are constantly being created. Our paper presents a comparative analysis of algorithms: FedAdam, FedYogi and FedSparse. But we need to keep in mind that FedAvg is at the core of many federated machine learning algorithms. Data testing was conducted using the Flower and Kaggle platforms with the above algorithms. Federated machine learning technology is usable in smartphones and other devices where it can create accurate predictions without revealing raw personal data. In organizations, it can reduce network load and enable private learning between devices. Federated machine learning can help develop models for the Internet of Things that adapt to changes in the system while protecting user privacy. And it is also used to develop an AI model to meet the risk requirements of leaking client’s personal data. The main aspects to consider are privacy and security of the data, the choice of the client to whom the algorithm itself will be directed to process the data, communication costs as well as its quality, and the platform for model aggregation.

APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROPS
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 43-56. - ISSN 2707-904X.

Авторы: Indira Uvaliуeva, Aigerim Ismukhamedova, Saule Belginova, Aigul Shaikhanova

Аннотация: The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system’s capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROPS
// Scientific Journal of Astana IT University. V.17. - Nur-Sultan.March, 2024.-P. 28-42. - ISSN 2707-904X.

Авторы: Aigul Mimenbayeva , Gulnur Issakova, Balausa Tanykpayeva , Ainur Tursumbayeva, Raya Suleimenova, Almat Tulkibaev

Аннотация: Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2 =0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions.