Scientific Journal of Astana IT University
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APPLICATION OF MATHEMATICAL MODELS IN THE DIAGNOSIS OF DISEASES OF INTERNAL ORGANS
Get PDFThe 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
EXPLORATION OF THE THEMATIC CLUSTERING AND COLLABORATION OPPORTUNITIES IN KAZAKHSTANI RESEARCH
Get PDFIn 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
EDUCATIONAL PROGRAMMES OF MICROQUALIFICATIONS AS AN EFFECTIVE TOOL FOR IMPLEMENTING THE PRINCIPLE OF CONTINUITY OF EDUCATION IN THE PROFESSIONAL ACTIVITY OF A MODERN UNIVERSITY TEACHER
Get PDFThe article analyses the introduction of educational programmes of microqualifications as an effective tool for implementing the principle of continuing education in the professional activities of university teachers. Special attention is paid to how microqualifications contribute to the development of competences and adaptation of teachers to the changing requirements of the educational environment. Examples of their use in the practice of higher education institutions are presented, and the impact on improving the quality of teaching and competitiveness of staff is assessed. The relevance of the study is conditioned by rapid changes and new challenges faced by the system of higher education in Kazakhstan. In the conditions of reforming the educational system of the country and the growth of international competition, the integration of microqualification programmes as a strategic approach to the continuous professional development of teachers is of particular importance. The aim of the study is to identify the key challenges and prospects for the development of microqualifications based on the analysis of global and regional educational trends. The paper uses the methods of strategic analysis, as well as comparative-historical approach, which allowed to identify opportunities and threats affecting the development of this system in Kazakhstan. Special attention is paid to the strengths and weaknesses of educational programmes, as well as their compliance with modern standards. As a result of the study, recommendations for successful integration of microqualifications into the strategies of HEIs are proposed. Special attention is paid to the creation of strategic partnerships, continuous monitoring of changes in the educational environment and ensuring the high quality of programmes in accordance with international standards. Prospects for the development of microqualifications in Kazakhstan include the development of supra-subject competences and a balance between digital and traditional teaching methods to meet the needs of the target audience and ensure professional development of teachers
A METHOD OF VULNERABILITY ANALYSIS IN WIRELESS INTERNET OF THINGS NETWORKS FOR SMART CITY INFRASTRUCTURES
Get PDFThe article proposes an approach to information security vulnerability analysis and threat modeling in wireless Internet of Things networks for Smart City infrastructures. Currently, such infrastructures are becoming increasingly widespread in a variety of Smart City application areas, including industrial life support systems, pipelines, communication networks, and transportation systems. The wide coverage of end users, the critical nature of such infrastructures and the value of their inherent assets determine the increasing importance of solving problems of determining the security level of such infrastructures and the timely application of protective measures. The ultimate goal of the proposed approach is to assess the security of the infrastructure. This article analyses articles at the intersection of the subject area of vulnerability and attack analysis in information systems and networks and the area of Smart City infrastructure issues. The proposed approach includes the use of an analytical model of an intruder which, together with the analysis of the specification of a specific Smart City infrastructure, allows us to determine the current types of attacks. In order to obtain infrastructure security assessments, the CAPEC database of wireless network vulnerabilities and attack patterns is analysed. In this case, the main attributes of the attacks are identified, unified and transformed into a single format using the numerical values of the considered attributes. The feasibility of the proposed approach is also analysed and its main advantages and disadvantages are considered. In addition, the main areas of further activity and tasks related to testing and improving the proposed approach in practice are identified
KAZAKH SPEECH AND RECOGNITION METHODS: ERROR ANALYSIS AND IMPROVEMENT PROSPECTS
Get PDFThis study offers a detailed evaluation of automatic speech recognition (ASR) systems for the Kazakh, examining their performance in recognizing the phonetic and linguistic features unique to the language. The Kazakh language presents specific challenges for ASR due to its complex phonology, vowel harmony, and the presence of multiple regional dialects. To address these challenges, a comparative analysis of three leading ASR systems were conducted—Kaldi, Mozilla DeepSpeech, and Google Speech-to-Text API—using a dataset of 101 recordings of spoken the Kazakh text. This study focuses on the systems' word error rates (WER), identifying common misrecognitions, especially with the Kazakh-specific phonemes like "қ," "ң," and "ү." Kaldi and Mozilla DeepSpeech exhibited high WERs, particularly struggling with Kazakh’s vowel harmony and consonant distinctions, while Google Speech-to-Text achieved of the lowest WER among the three. However, none of the systems demonstrated accuracy levels sufficient for practical applications, as errors in recognizing Kazakh’s agglutinative morphology and case endings remained pervasive. To improve these outcomes, a series of enhancements are proposed, including adapting acoustic models to better reflect Kazakh’s phonetic and morphological traits, integrating dialect-specific data, and employing machine learning methods such as transfer learning and hybrid models. Additional steps include refining data preprocessing and increasing dataset diversity to capture Kazakh’s linguistic nuances more accurately. By addressing these limitations, the ASR systems can better handle complex sentence structures and regional speech variations. This research thus provides a foundation for advancing Kazakh ASR technologies and contributes insights that are vital for developing inclusive, effective ASR systems capable of supporting linguistically diverse users
USING STRUCTURAL EQUATION MODELING METHODS TO ASSESS THE UNIVERSITY'S DIGITAL ECOSYSTEM
Get PDFThis 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
METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM
Get PDFForecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy
DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET
Get PDFCurrently, 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
FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION
Get PDFThis paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods.
Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology
ADVANCES IN THE DESIGN AND OPTIMIZATION OF SMART IRRIGATION SYSTEMS FOR SUSTAINABLE URBAN VERTICAL FARMING
Get PDFUrban vertical farming has emerged as a sustainable and innovative approach to addressing the increasing global demand for food in rapidly growing and densely populated cities, where traditional agriculture faces significant challenges due to space and resource constraints. A primary issue in these systems is the efficient management of critical resources, particularly water and energy, which are essential for maintaining high crop productivity and environmental sustainability. This study introduces, develops, and evaluates a mathematical model that integrates Internet of Things (IoT) technology to optimize water and energy usage in a hydroponic vertical farming setup. The model utilizes real-time environmental data collected from IoT sensors to dynamically adjust irrigation and energy consumption, ensuring minimal waste while sustaining optimal conditions for plant growth. Extensive simulations conducted using Python demonstrate substantial improvements in Water Use Efficiency (WUE) and significant energy savings, validating the model’s effectiveness. The study also presents practical case studies from regions like Singapore, Qatar, and Malaysia, showcasing how the integration of renewable energy sources, such as solar photovoltaic panels, with advanced smart irrigation technologies can lead to up to 50% growth rate improvements. Despite existing challenges, such as high initial capital investments, technical complexities, and the need for continuous maintenance, the findings indicate that modular and scalable system designs offer a promising path forward. Future research should aim to reduce overall costs and enhance system adaptability for various urban environments. Ultimately, this research provides a scalable and efficient framework for advancing urban agriculture, with the potential to contribute significantly to global food security and promote the sustainability of urban ecosystems