01. Faculty of Engineering & Natural Sciences

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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.
Open Access
1929-1940 ЖЫЛДАРДАҒЫ ЛАТЫН ГРАФИКАЛЫ ҚАЗАҚ ЖАЗУЫНЫҢ ТАРИХИ ТАҒЫЛЫМЫ
(СДУ хабаршысы - 2018, 2018) Күдеринова Қ.Б.; Абдиева М.
Аңдатпа: Бұл мақалада 1929-1940 жылдардағы латын графикалы қазақ жазуының тарихи тағылымы сөз болады. Зерттеу нәтижесі латын графикасымен басылған қазақ кітаптары туралы еңбектердің жоқ екендігін көрсетеді. 1929-1940 жылдары латын графикасымен Қазақстанда жарық көрген кітаптардың тізбесі жасалынып, кестемен көрсетілген. Кітаптардың авторлары, редакторлары, қай қалаларда басылғандығы да көрсетілген. Мақалада латын графикасындағы қазақ әліпбиімен 1929 жылдан емес, 1928 жылдан бастап шыға бастағаны айтылады. 1928 жылы 5000 тиражбен жарық көрген Байділдаұлы Ә. «Жаңа әліппе туралы» еңбегі талданады. Кітаптағы орфографиялық және пунктуациялық ерекшеліктер айтылады. Мәселен, «бас әріпке байланысты қиындықтар», «/в/-ның үлкен әріп нұсқасы», «қысаң езуліктердің емлесі», «и, у дыбыстарының жазылуы», «кейбір дауыссыз дыбыстардың жазылуы», «сөздерді бірге, бөлек жазудағы өзгерістер»,«кейбір тыныс белгілердің қолданылуы» секілді ерекшеліктер талданады.
Open Access
2D face recognition using PCA and triplet similarity embedding
(Bulletin of Electrical Engineering and Informatics, 2023) Bazatbekov B.; Turan C.; Kadyrov Sh.; Aitimov A.
The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it.
Open Access
3D MODELING OF THE OFFSHORE WIND TURBINES INTEGRATED STRUCTURE FOR KAZAKHSTAN REGION
(SDU University, 2017) Amirgaliyev Y. ; Khassanov D. ; Meraliyev M.
Abstract. In this scientific paper we examine meteorological characteristics of the field “Kashagan” located in the north Caspian Sea, the types of offshore oil and gas constructers, offshore wind turbine structure integrated for the middle depths of the Caspian shelf, and the load acting on them during operation on a shelf. Dimensional solid 3D model and 3D animation presentation of offshore wind turbine is built in the program Autodesk Maya 2012. Created the design scheme of supporting jacket for offshore wind turbine. Jacket designed for the field in the middle depths of the Caspian Sea. Carried out joint account of supporting columns and turbine structure on the static load—own weight of construction and the wind turbine weight of the structure, and the wind load by finite-elements. Also the calculations studied the stress-strain state of the structure. The finite element method are mastered and calculated by using a software package Autodesk Inventor Professional 2014. The calculation results can be used in the design of offshore wind turbine structures for oil and gas platforms. Also, the thesis presents the basic calculation of the estimated cost of construction and installation works supporting truss design and offshore wind turbine, its payback period. Sections health and the environment are considered potential risks to personnel and possible threats to the environment, and provide measures for their prevention and reduction.
Open Access
3D modeling of the offshore wind turbines integrated structure for Kazakhstan Region
(2017) Khassanov D.
In this thesis project examined meteorological characteristics of the field “Kashagan” located in the north Caspian Sea, the types of offshore oil and gas constructers, offshore wind turbine structure integrated for the middle depths of the Caspian shelf, and the load acting on them during operation on a shelf. Dimensional solid 3D model and 3D animation presentation of offshore wind turbine is built in the program Autodesk Maya 2012. Created the design scheme of supporting jacket for offshore wind turbine.Jacket designed for the field in the middle depths of the Caspian Sea. Carried out joint account of supporting columns and turbine structure on the static load - own weight of construction and the wind turbine weight of the structure, and the wind load by finite-elements. Also the calculations studied the stress-strain state of the structure. The finite element method are mastered and calculated by using a software package Autodesk Inventor Professional 2014. The calculation results can be used in the design of offshore wind turbine structures for oil and gas platforms.Also, the thesis presents the basic calculation of the estimated cost of construction and installation works supporting truss design and offshore wind turbine, its payback period. Sections health and the environment are considered potential risks to personnel and possible threats to the environment, and provide measures for their prevention and reduction.
Open Access
3D ПРИНТЕРГЕ АРНАЛҒАН ПЛАСТИКТІ ӨҢДЕЙТІН МАШИНАНЫ ҚҰРУ
(Suleyman Demirel University, 2016) Инкаров Б.Ә.; Алиманова М.О.
Әртүрлі салаларда, әсіресе мектептерда және жоғары оқұ орындардың зертханаларында 3D принтермен жұмыс істеу үшін арнайы пластик сатып алу қажет. 3D принтерінен шыққан бұйымдар жасалу бойынша пайдаланылғаннан кейін жарамсыздыққа келеді. Оларды қайта өңдеу мумкіндігі жоқтығынан казіргі заманда экономиялық дағдарыс себебінен сатып алатын мүмкіндігі төмендейді. Осы мәселені толық шешу мақсатымен 3D принтерге арналған пластикті өңдейтін машина құрастырылды. ABS пластикті жасау үшін шредер үгіткішін пайдалану, үгітілген пластикті белгілі бір бұрғыға салып, оның жан-жағынан арнайы қыздырғыштар болады. Қыздырғыштар пластикалық заттарды жоғарғы температурада ерітіп оны сопло арқылы шығарады. Жобаның макеті 3D принтерде, Arduino-да жасалынған.
Open Access
A Career Path Recommendation System For Computer Science Students
(Faculty of Engineering and Natural Science, 2024) Shaikym A.
This thesis presents the design, implementation, and evaluation of the Hybrid Career Path Recommendation System (HCPR), a sophisticated tool tailored specifically for guiding computer science students in their career decisions. The HCPR system innovatively combines Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods into a hybrid model to enhance the accuracy and personalization of job recommendations. This integration addresses the inherent limitations of using either approach in isolation and leverages their combined strengths to improve recommendation quality. The system utilizes a comprehensive dataset that includes detailed user profiles from Stack Overflow and job postings from LinkedIn. The CBF component analyzes user profiles to match students with jobs that align with their skills and educational backgrounds, while the CF component predicts user preferences based on historical interaction patterns, enhancing the system’s ability to recommend jobs that users are likely to find appealing. The HCPR system’s performance is rigorously evaluated using precision, recall, F1-score, and ranking metrics such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). The results demonstrate a significant improvement in recommendation accuracy and user satisfaction compared to standalone filtering approaches. The theoretical contributions of this thesis include advancements in hybrid recommendation system methodologies and a novel application of these systems to career guidance for computer science students. Practically, the HCPR system provides actionable insights that help students navigate the complex job market, potentially improving educational and career outcomes. This thesis concludes with suggestions for future research, emphasizing the potential for further refinement of the system and its adaptation to other fields beyond computer science. This work contributes to the fields of educational technology and recommender systems by demonstrating how integrated data-driven approaches can be effectively applied to personal and professional development tools.
Open Access
A course in information theory
(Almaty, Suleyman demirel university-2009, 2009) Arslanov M.Z.
Abstract. Information Theory is a fundamental field of study that plays a pivotal role in various aspects of modern technology, communication, and data science. This abstract provides an overview of a course in Information Theory, which covers the core principles and applications of this field.This course aims to introduce students to the foundational concepts of Information Theory, including entropy, information content, and coding theory. It explores the mathematical foundations of information and communication, enabling students to quantify and manipulate information in a systematic manner. Topics covered include Shannon's entropy, data compression, channel capacity, and error-correcting codes. Through a combination of theoretical lectures, practical exercises, and real-world applications, students will gain a comprehensive understanding of the principles that underlie the transmission and storage of information in various communication systems. They will also learn how these principles are applied in fields such as data compression, cryptography, and error detection and correction.The course is designed to cater to a diverse range of students, from those with a strong mathematical background to those with a more practical interest in communication and information technology. By the end of the course, students will not only be equipped with a solid theoretical foundation but will also have the skills to apply Information Theory to solve real-world problems, making it an essential part of the curriculum for anyone interested in the intersection of mathematics, computer science, and communication technology.
Open Access
A DECENTRALIZED, LOW COMPUTATIONAL COST STRATEGY FOR COORDINATION AND SEARCH WITH A ROBOT FLOCK
(Journal of Theoretical and Applied Information Technology, 2021) FREDY H.; MARTÍNEZ S.
The coordination of a flock of robots is a high demand application in applications such as motion planning, navigation, herding (tracking and/or tracing), area coverage (exploration, search and rescue, etc.), object transportation (surrounding and moving together), and compound tasks, all of which are currently heavily researched in robotics. Many approaches have been proposed to solve this problem, but they largely compromise system characteristics such as fault tolerance, capacity, efficiency, and in particular cost, since real implementations require special hardware. This paper proposes a coordination strategy for a system composed of small robots of minimalist design under the condition of minimum processing and sensing capacity. The communication requirements have been limited to a local communication strategy sufficient to achieve the relative orientation of each swarm member. The usefulness of the scheme is evaluated by simulation in specialized search tasks in an unknown region. The results show the high capability of the scheme and the ease of implementation on real prototypes.
Open Access
A novel recommender system for adapting single machine problems to distributed systems within MapReduce
(Bulletin of Electrical Engineering and Informatics, 2024) Orynbekova K.; Kadyrov Sh.; Bogdanchikov A.; Oktamov S.
This research introduces a novel recommender system for adapting singlemachine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression model's effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.
Open Access
A PROFESSION RECOMMENDER SYSTEM BASED ON DEEP LEARNING AND MACHINE LEARNING APPROACHES
(СДУ хабаршысы - 2023, 2023) Iskalinov F.
Abstract. The issue of uncertain career path choice among modern schoolchildren has become increasingly prominent, resulting in a substantial decrease in the number of university students. This uncertainty has become a major concern as students and their parents are often unfamiliar with the wide range of available professions, particularly those that have emerged in the last decade. A modern solution is proposed in the form of a web application that uses Deep Learning, Machine Learning, and NLP to recommend suitable specialties based on the competencies required for the profession. The system will analyze and extract implicit features through a supervised classification approach, providing a comprehensive solution for profession search in the Kazakhstan market.
Open Access
A Survey on Multimodal Approaches for Lung Disease Diagnosis using Deep Learning
(SDU University, 2025) Zhaniya Medeuova
Lung disorders are a major global health issue. A quick and accurate diagnosis is essential for proper treatment. In order to increase diagnostic accuracy, recent multimodal techniques are gaining popularity. This study carried out a comprehensive analysis of research articles on multimodal approaches that were published between 2020 and 2024 in Scopus and Google Scholar. The results show that there is limited study on the multimodal approach and on a variety of lung disorders such as asthma, TB, pneumonia, and chronic obstructive pulmonary disease. Several studies concentrated mainly on the detection and binary classification of COVID-19. The field has several challenges, including limited datasets, high computing costs, difficulties in integrating multiple modalities, and lack of accessibility of the models. Future studies should look at a wider range of lung diseases, increase the accessibility of datasets, improve fusion methods for merging data from many sources, and create models that are easier to understand and use fewer resources. Resolving these issues will improve patient outcomes by advancing the real-world use of deep learning in medical diagnosis.
Open Access
A symmetric analysis in LP space in two dimensions
(Faculty of Engineering and Natural Sciences, 2023) Adilkhanova Zh.
The p-Laplacian equation is a nonlinear partial differential equation of third order that arises as Euler-Lagrange equation of the gradient of function in L p norm which was first studied by Gunnar Aronsson in the late 80s [1]. Since then many explicit classical solutions and their generalisations are found. In this paper we find only the classical solutions of p-Laplacian equation in spatial dimensions, i.e. the p-harmonic functions in two dimensions. The p-harmonic functions are found by the use of Lie symmetry analysis method which deals with invariant solutions under some transformations of the solution of the partial differential equations. We obtain Lie algebra generators of the p-Laplacian equation, and the corresponding symmetry reductions of the p-Laplacian equation to ordinary differential equations. Finally, we use the Lie symmetries to construct invariant solutions of p-Laplacian that already known and some new ones in explicit form. Moreover, by using the Lie symmetries we can construct new solutions from known solutions of the p-Laplacian equation. In this article we use Lie symmetry analysis to find two-dimensional a new solution to Laplace’s p-equation. The p-Laplace equation is nonlinear partial differential equation (PDE), arising as an equation Euler-Lagrange expression of the gradient of a function in the L p norm. Using symmetry Lie, we reduce PDEs to ordinary differential equations. We find already known and new solutions of p-Laplace. It turns out using symmetric Lie analysis, we find the symmetry of the given u. We have 8 cases where we ended up getting rid of x,y,u. And in the end what remains is g and s. We were able to find the symmetry for u. In this research article, we employ the Lie symmetry analysis technique to discover two-dimensional solutions of the p-Laplacian equation. The p-Laplacian equation is nonlinear partial differential equation (PDE) that arises as EulerLagrange equation of the gradient of function in L p norm. By using Lie symmetries we reduce PDEs to ordinary differential equations. We find solutions of p-Laplacian that already known and some new. Lie symmetry analysis is a powerful mathematical tool used to investigate symmetries and simplify the solutions of differential equations. In this paper, the authors apply Lie symmetry analysis to the p-Laplacian equation, which is a nonlinear PDE that arises in the context of gradient optimization problems. iv The p-Laplacian equation can be written as: ∇ · (|∇u| p−2∇u) = 0 where ∇ represents the gradient operator and u is the unknown function. The parameter p determines the nonlinearity of the equation. By applying Lie symmetry analysis, the authors are able to identify symmetries of the p-Laplacian equation, which correspond to transformations that leave the equation invariant. These symmetries allow the authors to reduce the PDE to a system of ordinary differential equations (ODEs), which are often easier to solve. Using this approach, the authors are able to find two-dimensional solutions of the p-Laplacian equation. They also compare their results with known solutions in the literature and find some new solutions. Overall, this paper demonstrates the effectiveness of Lie symmetry analysis in simplifying and solving nonlinear PDEs, specifically the p-Laplacian equation. The identified solutions can have various applications in fields such as physics, engineering, and mathematical modeling.
Open Access
A thorough survey into the recognition of face emotion expression:experimental study, practical uses, and recommendations for the future
(Faculty of Engineering and Natural Science, 2024) Kuanyshbayev D.
The growth of the volume of information, as well as the expansion of the range of technically complex decision-making tasks require the systematization of existing methods and the development of new techniques and algorithms for their solution. The master’s thesis examines the possibility of using a neural network to solve the problem of recognizing human emotions. Artificial neural networks offer promising prospects for development, and software has a great advantage in using them. Moreover, each task performed has an unlimited and non-standard set of solution methods. The article considers the possibility of using a neural network to solve the problem of recognizing human emotions. The increasing volume of data, along with the breadth of technologically sophisticated issues with solving, necessitates the systematization of existing approaches and the creation of new techniques and algorithms for their resolution. The master’s thesis investigates the feasibility of utilizing a neural network to tackle the challenge of identifying human emotions. Artificial neural networks provide tremendous growth opportunities, and software can benefit greatly from their use. Furthermore, each challenge contains an infinite and non-standardized collection of solution techniques. The article discusses the feasibility of utilizing a neural network to tackle the difficulty of identifying human emotions.
Open Access
ADAPTATION OF GAMING PROCESS TO IMPROVE HAND REHABILITATION
(Suleyman Demirel University, 2016) Alimanova M.O.; Kozhamzharova D.Kh.; Zholdygarayev A.O.; Meraliyev M.M.
Hands, as the most dexterous part of our body, are of vital importance to our everyday life. However, since hands are extensively used in nearly all tasks, they are exposed in more dangerous environment than any other parts. Overwork, injury and geratic complications, such as stroke can all cause hand function, totally or partially, which directly diminish the q uality of life. Unpleasant effects caused by trauma and overwork to hands results with immediate hand rehabilitation. Trainings for patients’ rehabilitations are normally goes in rehabilitation center in hospitals, with getting some physiotherapy for han ds, making some exercises and etc. However all this may bore patients and not to motivate to sooner recovery. The Leap Motion controller is a small device that senses consumer gestures and is aimed to enlarge a user’s interactive experience with their computer. Using infrared sensors, it is able to collect data about the position and motions of a user’s hands. This allows to use leap motion in different purposes like development of children’s intellect, having fun with playing virtual reality games and etc. One more example for effective usage of such device is in the purpose of medicine.
Open Access
Adaptive traffic control
(Faculty of Engineering and Natural Sciences, 2024) Aitureyeva B.
Adaptive traffic control is a key aspect of modern transport systems, aimed at optimizing the flow of vehicles, increasing safety and reducing congestion on the road infrastructure. This research abstract examines the concepts, methods and technologies of adaptive traffic control and their application to improve traffic efficiency. The paper discusses the basic principles of adaptive control, including the use of real-time traffic data, traffic flow forecasting, as well as methods for optimizing traffic light control and dynamic changes in road speed limits. Modern technologies, such as smart city systems and autonomous vehicles, and their impact on the development of adaptive traffic management are also discussed. The results of the study are of practical interest to city authorities, transport organizations and engineers seeking to make traffic more efficient, safe and sustainable. During the research, an article was published in an international collection on the topic “The implementation of adaptive traffic control system for reducing traffic congestion.” Work was carried out on methodology, data collection and analysis. The experiments were carried out in a simulated environment The SUMO simulator for the intersection of K. Tulemetov and T. Utegenova streets, Shymkent. The SUMO simulator – Simulation of Urban MObility is a discretetime platform for modeling traffic flows and is intended for assessing vehicle mobility models in the context of traffic management, and simulating assessment of traffic video surveillance systems. To simulate the transport situation in the simulator, data from video cameras close to the intersection are used.
Open Access
Advisory system for adapting a single machine problem to a distributed solution
(SDU University, 2024) Orynbekova Kamila
General characteristics of the work. The work encompasses developing an advisory system to recommend solutions for single-machine problems adaptable to distributed systems, mainly focusing on implementation within the MapReduce platform. Methodologically, an experiment evaluated learning effectiveness, while extensive data collection informed model development. Predictive models, including Naive Bayes and Logistic Regression, were optimized and integrated into a recommendation system validated through rigorous evaluation. The aim of the research is to develop an advisory system that recommends single-machine problem solutions that adapt to distributed systems and are suitable for implementation on the MapReduce platform.
Open Access
Algebraic numbers, hyperbolicity, and density modulo one
(Journal of Number Theory, 2012) Gorodnik A.; Kadyrov S.
We prove the density of the sets of the form λm 1 μn 1ξ1 +···+ λm k μn k ξk: m,n ∈ N modulo one, where λi and μi are multiplicatively independent algebraic numbers satisfying some additional assumptions. The proof is based on analysing dynamics of higher-rank actions on compact abelian groups.
Open Access
AN EVALUATION OF UNSUPERVISED OUTLIER DETECTION METHODS FOR UNIVARIATE TIME SERIES DATA IN FINANCIAL TRANSACTIONS
(СДУ хабаршысы - 2023, 2023) Amankossova A.; Turan C.
Abstract. An essential problem in finance application areas is identifying abnormal subsequences in time series data. Despite the wide range of outlier detection algorithms, no substantial research has been conducted to thoroughly investigate and assess the various methodologies, particularly in the financial industry. This study focuses on comparing and contrasting the outcomes of various unsupervised algorithms. The findings reveal that the Local Outlier Factor technique outperforms the other methods in terms of precision, recall, and Fl-score. The research provides valuable insights for financial institutions and businesses looking to improve their identification of abnormalities systems and highlights the importance of choosing the appropriate unsupervised outlier detection method for financial transaction data. The outcomes of this study can be used to inform future research and development in the area of financial unusual case detection.
Open Access
Analysis and development of system for predict psychological portrait of a person
(2020) Atanbekov A.
Text content is one of the widespread media types. A question which we are trying to find out in this study is the following: can we identify a psychological portrait or psychological type of a person by given a short text document written by that person? There are different techniques to identify personality types by given text. In this study, the model has been developed to predict the psychological type of the person based on XGBoost. This study is going to explore machine learning algorithms to predict the psychological portrait of a person based on a given text. The psychological portrait will be described by Myers-Briggs Type Indicators (MBTI) which is the most reliable and popular method. This question ‘5 motivated by essays of students at the beginning of their courses to understand their psychological portrait and how it can be met with the profession they choose. The results of this study will help HR specialists and teachers to better understand their students.