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Item Open Access Қазақстандағы биометриялық технологиялардың интеграциясы: инновацияларды, тәуекелдерді және құқықтық аспектілерді талдау(SDU University, 2025) Тортай Е.К.Қазақстандағы биометриялық технологиялардың интеграциясы елдің цифрлық трансформациясының негізгі бағыттарының біріне айналуда. Ақпараттық технологиялардың қарқынды дамуы және мемлекеттік қызметтердің қауіпсіздігі мен тиімділігін арттыру қажеттілігінің артуының негізінде беттерді, саусақ іздерін және басқа да бірегей биометриялық параметрлерді тануға негізделген жүйелерді пайдалану қоғамдық өмірдің әртүрлі салаларын оңтайландырудың маңызды әлеуетін білдіреді. Алайда, бұл инновацияларды енгізу бірқатар маңызды сын-қатерлермен байланысты. Олардың ішінде дербес деректерді қорғау, құпиялылықты қамтамасыз ету және технологияларды теріс пайдаланудың алдын алу мәселелері шешуші болып табылады. Биометриялық ақпарат көлемінің өсуімен инновациялық мүмкіндіктер мен азаматтардың жеке өміріне қол сұғылмаушылық құқықтары арасындағы тепе-теңдікті қамтамасыз етуге қабілетті нормативтік-құқықтық базаны құру және жетілдіру қажеттілігі туындайды.Item Open Access Creating the Integrative STEM Lesson Unit Plan for 10th Grade Students(SDU University, 2025) Zhumashev A.This master's thesis examines the creation and implementation of an integrative STEM method in the educational process based on 10th grades through the use of a Unit Plan/Curriculum. The main purpose of the master's thesis is to introduce and evaluate the effectiveness of STEM methodology as a methodology capable of being integrated in educational institutions in Kazakhstan. Additionally evaluate and comprehensively study the aspects of the methodology based on the work of other authors in the literature review section. The study contains traditional teaching methods such as the use of modern teaching technologies, the use of digital tools, and practical processes during the lesson. In order to understand how familiar students are with the concept of STEM, a survey was conducted among 179 students of Colleges in Almaty. Also, based on the students' responses, a methodological guideline for teachers on the implementation of STEM with an emphasis on chemistry was created and a comparative analysis of two groups was conducted, one of which was taught the STEM methodology (experimental group) based on the methodological guideline and the group that was trained according to the traditional system. At the beginning and end of the training, two groups were tested to compare changes in the results. The results of the test study showed a positive trend in the assimilation of material and memorization in the experimental group, which was trained using the STEM methodology, as well as higher motivation for lessons and full involvement of students. As a result, this work can serve as a practical guide for integrating STEM methodology into the learning process and subsequent improvement for teachers who want to change the teaching format and try new things in teaching, thereby increasing student engagement in the learning process. According to the hypothesis, the introduction of STEM methodology will cause high student engagement and improve the quality of educationItem Open Access Research on a UAV Distance Prediction System Based on Acoustic Data and Deep Learning(SDU University, 2025) Yembergenova A.Unmanned aerial vehicles (UAVs), also referred to as drones, have become increasingly popular in recent years, posing serious security and privacy issues. Concerns have been raised by their growing presence in public areas and civilian life as a result of incidents involving disturbances, privacy invasion, and unauthorised surveillance. This study aims to address these issues by creating an intelligent, sound-based system that can identify drones and determine how close they are to people or sensitive areas. The primary objective of this study was to assess the viability of classifying drone distance based on sound emissions using deep learning models and audio signals. Three zones—Zone 1, Zone 2, and Zone 3—each denoting varying degrees of proximity—were created from the drone sounds. Convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and a hybrid CNN-BiLSTM model were among the deep learning models examined in the study. With an average classification accuracy of 90%, the hybrid CNN-BiLSTM model outperformed the others. This model is very accurate at predicting drone distance zones because it successfully captured both spatial and temporal features from the audio recordings. These results imply that drone detection systems can be greatly improved by combining deep learning with audio-based classification. Such systems could significantly increase responsiveness and accuracy in detecting unauthorised UAV activity when paired with other sensory inputs in bimodal or multimodal frameworks. All things considered, this research advances acoustic sensing technologies to protect critical infrastructure and public safety from the increasing threat of rogue drone usageItem Open Access Fractal dimension of exceptional sets in semi-regular continued fraction(SDU University, 2025) Duisen S.This thesis investigates the interplay between Diophantine approximation, continued fraction representations, and fractal geometry. We begin by exploring the classical notion of badly approximable numbers-real numbers whose continued fraction expansions have bounded partial quotients. These numbers, while forming a set of zero Lebesgue measure, exhibit full Hausdorff dimension, highlighting their rich geometric structure. Building on this foundation, we introduce and analyze a generalization known as semi-regular continued fractions, wherein a fixed sequence of signs modifies the classical expansion. For such expansions, we define the class of σ-badly approximable numbers and study their distribution and fractal properties. We demonstrate that these generalized expansions preserve many of the geometric complexities of their classical counterparts, while offering new degrees of arithmetic freedom. In the second part of the thesis, we shift our focus to Lehner expansions of real numbers and examine how the statistical behavior of the associated digit sequence (bn) influences the fractal geometry of the corresponding number sets. Specifically, we investigate the impact of the average value of bn on the box dimension-a quantitative measure of geometric complexity. Employing the box-counting method, we perform numerical experiments to estimate the box dimension and uncover how variations in the digit sequence relate to the irregularity and structure of the expansion. By synthesizing the analytical and numerical approaches, this thesis provides a comprehensive view of how modifications to continued fraction representations influence the fractal characteristics of real number sets, contributing to the broader understanding of number-theoretic and geometric interrelations.Item Open Access Mitigating Bias in AI-Based Loan Approval Systems through Fairness-Centric Techniques(SDU University, 2025) Raziyeva S.As artificial intelligence (AI) becomes increasingly embedded in high-stakes decision-making systems, ensuring fairness in algorithmic outcomes has emerged as a critical concern. This thesis investigates bias and fairness in AI-based credit scoring systems, with a particular focus on gender disparities. Using the German Credit Dataset as a case study, the research evaluates the performance and fairness of several supervised machine learning models, including Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine, and Neural Network. The study applies fairness metrics such as Statistical Parity Difference (SPD) and Disparate Impact (DI) to assess group-level inequalities in predicted loan approval outcomes. Results reveal a consistent trade-off between model accuracy and fairness, where high-performing models like Random Forest and XGBoost demonstrate notable biases against female applicants. Even interpretable models, such as Logistic Regression, exhibit fairness issues due to historical and structural biases embedded in the training data. To address these challenges, the thesis highlights the importance of incorporating fairness-aware strategies across the machine learning pipeline, including data pre-processing, fairness evaluation, and potential post-processing mitigation. The use of tools like AIF360 and stratified sampling further strengthens the analysis. This research contributes to the growing discourse on responsible AI by demonstrating that achieving fairness is not merely a technical goal but a sociotechnical imperative. It calls for an interdisciplinary approach that combines ethical reasoning, regulatory compliance, and algorithmic transparency to ensure equitable access to financial services. The findings advocate for the development of AI systems that are not only accurate but also accountable and inclusive.Item Open Access Conservative extensions of NIP non dp-minimal theories(SDU University, 2025) Rassayeva N.This dissertation explores conservative extensions in the context of dependent theories (NIP) that are not dp-minimal. We study the model-theoretic properties of the special Cartesian product of ordered structures, focusing on how the dprank and definability of types behave under such constructions. It is shown that the product of two o-minimal or two weakly o-minimal structures yields a theory of dp-rank 2, which remains NIP but is no longer dp-minimal. Further, we analyze the behavior of 1-conservative and n-conservative extensions in these theories. For o-minimal structures, 1-conservativity implies nconservativity for all finite n, ensuring strong definability of types. However, for weakly o-minimal structures, this implication fails; we construct an explicit example where a 1-conservative extension does not extend to a 2-conservative one. The results provide new insights into how model-theoretic complexity measured by dp-rank affects definability and extendability in NIP theories, contributing to the classification and understanding of dependent but non-dp-minimal structures.Item Open Access Expansion of models of DP-minimal theories(SDU University, 2025) Nurlanova A.This study investigates expansions of models of DP-minimal theories, a main subclass of dependent theories in model theory distinguished by well-controlled combinatorial complexity. Finding the circumstances in which DP-minimality is maintained when structures are extended by more predicates, functions, or relations is the main goal of the project. Following a thorough explanation of fundamental ideas like DP-rank, definability, and quantifier elimination, the study examines several extensions of the group of integers (Z, +, 0) and associated ordered algebraic systems. Expansions by linear orders and additional unary or binary predicates are important instances. The findings show that while some expansions lead to superstable but non-DPminimal expansions, others, like those corresponding to Presburger arithmetic (Z, +, <, 0, 1), preserve DP-minimality. By emphasizing the harmony between increased expressive power and minimality condition preservation, these results advance our knowledge of the relationship between model expansions and classification theory. The final section of the dissertation outlines possible avenues for future study, such as applications to ordered structures and broader classes of expansions.Item Open Access Expansion of models of DP-minimal theories(SDU University, 2025) Nurlanova A.This study investigates expansions of models of DP-minimal theories, a main subclass of dependent theories in model theory distinguished by well-controlled combinatorial complexity. Finding the circumstances in which DP-minimality is maintained when structures are extended by more predicates, functions, or relations is the main goal of the project. Following a thorough explanation of fundamental ideas like DP-rank, definability, and quantifier elimination, the study examines several extensions of the group of integers (Z, +, 0) and associated ordered algebraic systems. Expansions by linear orders and additional unary or binary predicates are important instances. The findings show that while some expansions lead to superstable but non-DPminimal expansions, others, like those corresponding to Presburger arithmetic (Z, +, <, 0, 1), preserve DP-minimality. By emphasizing the harmony between increased expressive power and minimality condition preservation, these results advance our knowledge of the relationship between model expansions and classification theory. The final section of the dissertation outlines possible avenues for future study, such as applications to ordered structures and broader classes of expansions.Item Open Access Estimation of Vaccination Price through Mathematical Epidemic Models to Optimize the Government Cost(faculty of engineering and natural sciences, 2021) Dauzhanov Zh.; Avgustov B.; Shakuova D.These days, humanity is faced with a global Coronavirus pandemic problem, which entails a financial crisis, so the governments want to minimize their financial loss. In this project work by using the epidemic mathematical model we consider on the basic reproduction number, which is important parameter in the epidemiology and also on the optimization problem about how much should be a discount for the vaccination to optimize the government revenue. During this study, we get acquainted with the following topics: mathematical modelling, dynamical systems, epidemic models, stability analysis, optimization methods, simulations on software and etc. Initially, we constructed the epidemic model for COVID-19 and separated infectious individuals by two groups, based on the compartmental SIR model and after that by using two different approaches to analyze the model, namely, Linearization (Hartman-Grobman) and Next Generation matrix method, we obtained the most important formula in epidemiology: the basic reproduction number 1.3. To solve the government cost, we constructed the government cost function which takes into account the cost of vaccination, the cost of treatment, the average wage of citizens. By using the software we solved numerically the system of nonlinear differential equations of our epidemic model, also we optimized the governmental cost function depending on a vaccination discount and obtained the main result of applied part of our project work 1.6, that the government cost is minimized with making the vaccination fully free of charge for citizens. The study will be useful for the Government of Kazakhstan in predicting the number of infectious individuals as well as in planning the income revenue. By changing the initial parameters in our epidemic model, it is easy compute the basic reproduction number and Government cost function for any country.Item Open Access Решение дифференциальных уравнений с помощью исскусственных нейронных сетей(faculty of engineering and natural sciences, 2013) Газизов Т.We should note the special role of differential equations in the solution of many problems in mathematics, physics and engineering, as it is not always possible to establish a functional relationship between the data and the variables, but it is often possible to derive a differential equation that allows you to accurately predict the course of a particular process under certain conditions. Differential equations have great practical importance, being a powerful tool for exploring the many problems of science and technology: they are widely used in mechanics, astronomy, physics, in many problems of chemistry and biology. This is because very often the laws that govern certain processes are recorded in the form of differential equations, and the equations themselves act as a mean of quantitative interpretation of thus laws. To solve thus equations we take the most well suited networks belonging to a class of Hopfield neural networks. These networks have a way of transmitting output signals to the inputs, and the response of such networks is dynamic, i.e. after receive a new input the output is calculated and transmitting by feedback network modifies the input. Then the output is recalculated, and the process is repeated again. For the network, which can be considered as stable, the sequence of iterations lead to smaller changes in outputs, and at the end the output does not become permanent. There is also an unstable network, for which the process of selection of the output may never end. That's the essence of the network settings for gaining the desired result. Of course, there is also a classical numerical methods. But there are situations where these methods may not lead to a solution, or it can be obtained for a very large number of iterations. The neural network is much more flexible in this respect, and generally, an algorithm based on them is more efficient.Item Open Access JORDAN ELEMENTS IN ASSOSYMMETRIC ALGEBRAS(faculty of engineering and natural sciences, 2022) Kudaibergen Y.We consider Jordan brackets in a free assosymmetric algebra. We investigate expansions of left-normed Jordan brackets in free assosymmetric algebra and give a conjecture. In general, we show the proposition and some examples then the proof. For associative algebras P.M. Cohn gave a criterion for Jordanian elements generated by three elements, but we further advanced to assosymetrical algebras not with three elements, but with four, and we showed five elements, but we have the degree and the elements are equal. In general, we have shown a special case, but in the end, there are assumptions that it can work on any dimension n.Item Open Access Cardinality of survivor sets in open dynamical systems(faculty of engineering and natural sciences, 2020) Aitu N.In this thesis, our goal is to learn about open dynamical systems corresponding interval maps. We study the class of dynamical systems with holes: Expanding maps of the interval. In detail, We consider symbolic dynamics with holes. Let H-hole lies in the interval [0, 1) and let T : [0, 1) −→ [0, 1) be a self map. The survivor set Ω(H) := {x ∈ [0, 1) : T nx /∈ H, n ≥ 0}. Depending on location and size of the holes we will characterize and study the survivor set Ω(H) infinite or finite, uncountable or countable and survivor set Ω(H) has positive entropy.Item Open Access The prediction of information security level in the enterprise(faculty of engineering and natural sciences, 2020) Khashimova D.This thesis presents the results of an analysis to identify groups of threats specific to the infrastructure and systems of an enterprise, which is one of the main stages in forecasting. The state of information security at enterprises is considered, the qualifications of security threats and classification methods based on attack methods and the impact of threats are analyzed. Threats for the safe use of the Internet and hacking sites, data theft, phishing attacks and social engineering are assessed; Identification of cloud computing security threats that are encountered in the enterprise's Internet networks. The advantages and disadvantages of Web Application Firewall, which are used to protect attacks, such as DDoS attacks, SQL injections, cross-site scripting, and others, are studied. Works for providing protection using artificial intelligence and machine learning are presented.Item Open Access Приближение интеграла функции с весом на классе Соболева(faculty of engineering and natural sciences, 2013) Нурмагамбетов Б.ТСовременная вычислительная математика ориентирована на использование компьютеров для прикладных расчетов. Любые математические приложения начинаются с построения модели явления, к которому относится изучаемый вопрос. В различных областях науки и техники, экономики математическими моделями служат функции, производные, интегралы, дифференциальные уравнения. Компьютер дает возможность запоминать большие (но конечные) массивы чисел и производить над ними арифметические операции и сравнения с большой (но конечной) скоростью по данной вычислителем программе. Поэтому для использования компьютеров для вычислений эти исходные модели надо приближенно заменить такими, которые описываются конечными наборами чисел с указанием конечных последовательностей действий (конечных алгоритмов) для их обработки. В алгоритмах обработки экспериментальной информации часто возникает необходимость представления в сжатой форме эмпирических зависимостей между параметрами, описывающих поведение сложной системы. Такое сжатие информации в современной математике осуществляется с помощью различных методов приближения функций: интерполирования, аппроксимации, восстановления и др.Item Open Access ЖАЛПЫЛАНҒАН ҮЗІЛІЛІССІЗДІК МОДУЛІМЕН ФУНКЦИЯЛАР КЛАСЫНДА ИНТЕГРАЛДЫ ЖУЫҚТАУ(faculty of engineering and natural sciences, 2013) Намаджанова М.This work is dedicated to the study class of wt(D) of the system Chebyshev on approximation of the integral.The remainder of the quadrature formula is expressed in terms of the Fourier-Chebyshev series.Used definition of the modulus continuity,the Chebyshev polynomials and integration with weight. The errors of quadrature formula power scale estimated.The work is theoretical in nature.Item Open Access НОВИКОВ АЛГЕБРАЛАРЫ ҮШІН ҚАРАСТЫРЫЛҒАН БУХБЕРГЕР АЛГОРИТМІ(faculty of engineering and natural sciences, 2013) Ілияс Д.Т.Given course work describes the Buchberger algorithm to defind Grébner basis for bicommutative algebra.Item Open Access WEIGHTS OF PARTITIONS(faculty of engineering and natural sciences, 2013) Султамуратов Р.С.Studying Sn-module structure of any algebra is the one of the most important problem in algebra. The weight function gives a good classification of Sn-module structure of Novikov algebras. Image ofweight function defines which Specht modules appear in the algebra, moreover, it is a good tool to determine isomorphism between submodules of Novikov algebras and permutation modules. The main part of diploma gives some usefull and interesting properties of weight function. It is defined that if the great common divisor of all parts of a partition is more that one then the partition does not belong to the set of image of the weight. Also, it is found a criteria minimal element with respect to dominance order in the image of weight. That gives huge help to define admissible partitions. However, there remain some important questions in studying this function.Item Open Access THE SOLUTION OF PROBLEMS WITH DISCONTINUOUS COEFFICIENTS OF THERMAL CONDUCTIVITY BY THE INTEGRAL OF THE ERROR FUNCTION(faculty of engineering and natural sciences, 2013) Kospanova G.The present paper attempts to investigate a new effective method of solving problems of thermal conductivity, new methods of solving parabolic equations with moving boundaries. In this paper it was tried to show the use of interdisciplinary connection оп the example of Mathematical Physics course. Using Integral Error Function a new effective method was developed that positively effects on mathematical achievement of students. Approximate and analytical solutions of the boundary-value problems is found using Integral Error Functions and their properties or by IEF method, which enable to solve wide range of heat equations with fixed and moving boundaries. Analytical solution of heat equation with discontinuous coefficients for the thermal conductivity by IEF method is found in this term paper.Item Open Access 12 жылдық мектептегі математикалық білім беруді даралау мен саралау әдістемесі(faculty of engineering and natural sciences, 2013) Кадрушев М.This paper deals with the problem of individualization and differentiation of teaching mathematics in high school. Lack of individualized academic work students hinders the optimal development of their abilities, entails a reduction in the level of knowledge. For effective teaching of mathematics, this problem is of particular importance, in view of the difficulties that typically arise when the students learn it, and due to the increased knowledge of mathematics education in general secondary education. Individualization of learning mathematics and assumes its mandatory differentiation that must be understood as a comprehensive availability and effectiveness of learning for all students and for each of them separately. Individualization of teaching mathematics does not mean abandoning the collective activities of the students in the learning process, it just means the organic unity of individual and collective learning activity of schoolchildren. Methods of individualization and differentiation of teaching mathematics, as a condition for implementing a 12-year education, not well understood in our country.Item Open Access The solution of the heat equation in domains with moving boundaries by the Integral Error Functions method(faculty of engineering and natural sciences, 2013) Temirkul A.ShIn mathematics , development of new analytical methods of solution of the heat transfer problems is very important for various applications because it enables one to analyze an interrelationship of various input parameters on the dynamics of investigating phenomena, while the use of numerical methods is a problem when the number of parameters is great. And from mathematical point of view, most mathematical models based on Verigin, Stefan and inverse Stefan type boundary value problems. Such problems are among the most complicated, formidable and difficult problems in the theory of nonlinear parabolic equations in mathematical physics, since the corresponding integral equations are singular and require new approaches in solving problems analytically and numerically and also which long with the desired solutions of the equations, moving boundaries have to be found. In some cases, heat potentials can be constructed by which the boundaryvalue problems can be reduced to integral equations. However, in the case of domains that are degenerate at the initial time, additional difficulties arise due to the singularity of the integral* equations, which belong to the class of pseudoVolterra equations that are unsolvable inthe general case bythe method of successive approximations. These results are obtained by S.N. Kharin [2]. The method of solving heat transfer problems with moving boundaries and phase transformation is represented'by Integral Error Functions and its properties. The results indicate that Integral Error Functions enable to solve, many practical problems described above in the easier way than classical methods, and could be implemented into the course of teaching mathematical physics, as special methods of solving heat transfer problems with moving boundaries.