2. Theses and Dissertations

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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
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 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
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
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.
Open Access
Analysis and the development of a mathematical model of the children mortality
(Faculty of engineering and natural sciences, 2019) Zhumabek D.
The mortality rate depends on many different factors: the socio-economic development of the country, the environmental situation. the well-being of the population. the level of stress and much more. After fertility. it takes the second place in its importance in the processes of reproduction of the population. has a serious impact on the population size. its structure. and is closely interconnected with all socio-demographic processes. The causes of mortality in Kazakhstan are classified by the main groups: infectious discases. diseases of the respiratory system. circulatory system, neoplasms. accidents, poisonings and injuries. Mortality of the population is a mirror reflection of the level of socio-economic development of society.
Open Access
Analysis of Course Schedule Generation Tool and Its Integration to University Automation System
(Faculty of Engineering and Natural Science, 2020) Zhuniskhanov M.
This research examines the university course schedule generation problem. It begins with the definition of the inconveniences of the old system. Then determine the importance of reducing the resource usage while integrating with the university automation system. This discussion integrates with an overview of the problem itself both from practical and academic perspectives. Then course schedule generation algorithms discussed with the ways of implementing it. This is followed by benchmark and resource usage comparison.
Open Access
Analysis of Creation Scientific Library System with MARC21
(2021) Abubakirova A.
Automation solutions come to centralization and internalization in every part of processes in different spheres. The reason is that the philosophy of humanity changed to “win to win”, assist each other, be a team and develop systems that give effectiveness, focus and open source. In this research work analyzed CIS Library systems and their formats, advantages and disadvantages from the development part. In the result identified 4 criterias in the development of the corresponding software in the creation of the Library System. For a more detailed analysis of problems between different formats, we used OCLC WSKey and found authority tags. Analyzed timeline of creation of basic modules of library system and influence in each other, accent was in analyzing and implementing cataloging modules with comparing desktop and web software and the additional task possibility of transferring metadata with XML files between different formats. In future work was testing and comparison of analysis.
Open Access
Analysis of data to improve system of an educational organization
(2020) Serek A.
In this thesis, there was done a set of computational experiments on the datasets of educational organizations. In the first part of the work, there were executed lots of experiments with decision tree ID3 algorithm on the educational camp dataset (”Educon”) that automatically predicts a participant’s feedback using Scikit-learn library and extrapolatory data analysis of that was done using Pandas library and Python programming language. The experimental results showed that the most optimal maximum depth (which is the number of edges starting from the root till the leaf) for the decision tree is 3 and the most optimal minimum number of splits (which is the minimum amount of samples of the dataset that are required to split an internal node of the decision tree) is 192. Based on that, there was achieved optimum results of precision, recall, and f1 score machine learning metrics that vary between 75 to 98 depending on the change of tunable variables of the ID3 algorithm. In the subsequent parts of the thesis, the information extraction system was built based on an educational camp dataset and recommendations for hackathon improvement were derived. The datasets are not open-source and were collected manually through the use of surveys.
Open Access
Analysis of effective machine learning techniques for improvement of student performance
(2023) Tolbassy B.
It is advantageous to use machine learning (ML) algorithms to assess student performance based on their prior performances and current behaviour because they can project both positive and negative outcomes at different educational levels. Learning outcomes can be improved by early performance prediction for students. The prediction of a student’s academic performance is important because it shapes changes in university academic policies, guides instructional strategies, evaluates the efficacy and efficiency of learning, provides teachers and students with pertinent feedback, and modifies learning environments. All of these elements support higher graduation rates. There is currently no clear winner among the various machine learning techniques for predicting student performance while enhancing learning outcomes. Therefore, this study is going to present the most effective machine learning techniques using and analysing open-source data from the Kaggle platform.
Open Access
Analysis of students’ behavior and progress on Learning Management System using Machine Learning
(Faculty of Engineering and Natural Science, 2024) Kalekes D.
Many students do not put in sufficient effort at the beginning of the academic year, leading to grades that are insufficient for completing courses or obtaining scholarships. This study aims to analyze and predict student performance on the Moodle platform to provide early interventions and improve academic outcomes. The analysis focused on various courses from the 2023-2024 academic year at SDU University, selected due to their high average number of students and well-established structures. The research involved collecting data on three predictive factors: the number of completed assignments, the total time spent on the course, and the number of actions on the platform. Six machine learning algorithms were applied to predict student performance: k-Nearest Neighbor, Random Forest, Decision Tree, Logistic Regression, Naive Bayes, and Support Vector Machine. The study compared the effectiveness of early prediction at 5, 10, and 15 weeks into the courses. Key findings indicate that student activities on Moodle are significantly correlated with higher academic performance. The Support Vector Machine model showed the best results in the early weeks, while the Random Forest model demonstrated stable results over longer periods. These findings highlight the potential of machine learning models to identify at-risk students early, allowing for timely support and interventions. The implications of this research are significant for educators and administrators. The ability to predict student performance early can facilitate timely interventions, helping students improve their academic results and reduce withdrawal rates. This study contributes to the growing body of knowledge in educational data analysis and learning analytics, providing a foundation for future research to refine and expand predictive capabilities in educational institutions.
Open Access
Analysis of students’ interest in programming
(2023) Saimassay G.
The proportion of women working in and studying computer science (CS) remains much lower than that of men. Young women’s perceptions of computer science as a career are highly impacted by their sense of self and identity. We believe that if young girls are introduced to software programming in a way that enables them to explore their identities early on, they will be more likely to pursue careers in CS. This research looks at the role of gender diversity and cultural stereotypes in affecting girls’ job choices outside of information technology (IT). The study attempts to explore the underlying causes of this trend by investigating gender inequalities and identifying the primary factors impacting girls’ views and attitudes about computers and ICT education. Furthermore, the research intends to establish an optimum atmosphere that fosters girls’ interest in IT and encourages women’s engagement in IT careers. The study looks at the relationship between confidence and satisfaction levels using a case study technique using before and post questionnaires. The obtained data is evaluated to identify the influence of these elements on the vision and interest of females in IT. The results help to explain gender discrepancies in job choices and provide suggestions for developing a supportive and inclusive atmosphere to enhance girls’ interest in IT. The report emphasizes the necessity of tackling cultural preconceptions and encouraging gender diversity in order to create an atmosphere that encourages girls to pursue careers in IT and promotes women’s participation in IT occupations.
Open Access
ANALYSIS OF SYSTEMS PYTHON VS. RUBY
(2013) Kopbayeva Zh.
Nowadays web development programming is very popular. Using object- oriented programming (OOP) languages in it made it fun to design overall architectures, functionality and ease of usability. As the scripting languages did not lose their popularity in this process, on their own field they also were making progress for making web development interesting for a programmer. Also scripting languages’ popularity is stable because of their compatibility and ease of use with the other languages. Considering these in this paper the comparison of basic items is made, that exist in Ruby and Python. Python itself was already popular, but coming of Ruby into the world of developers made them to begin many discussions. This work will give you basic view to make yourself a conclusion which one to choose and here it’s assumed that you already know one of the OOP languages or at least have some basic understanding of it. It won’t be just the basic interview with Python and Ruby, but from the introduction part the main concepts for language differences like dynamic versus static typing, strong and weak typing, compiled versus interpreted properties’ overview will be made. At the end developers will be able to conclude for themselves how to start and continue, while making clear reasoning. Because the work was made to do a rational overview of both Ruby and Python so that there won’t be any prejudice of a particular individual.
Open Access
ANALYSIS OF SYSTEMS PYTHON VS. RUBY
(Faculty of Engineering and Natural Science, 2013) Kopbayeva Zh.
Nowadays web development programming is very popular. Using object- oriented programming (OOP) languages in it made it fun to design overall architectures, functionality and ease of usability. As the scripting languages did not lose their popularity in this process, on their own field they also were making . progress for making web development interesting for a programmer. Also scripting languages’ popularity is stable because of their compatibility and ease of use with the other languages. Considering these in this paper the comparison of basic items is made, that exist in Ruby and Python. Python itself was already popular, but coming of Ruby into the world of developers made them to begin many discussions. This work will give you basic view to make yourself a conclusion which one to choose and here it’s assumed that you already know one of the OOP languages or at least have some basic understanding of it. It won’t be just the basic interview with Python and Ruby, but from the introduction part the main concepts for language differences like dynamic versus Static typing, strong and weak typing, compiled versus interpreted properties’ overview will be made. At the end developers will be able to conclude for themselves how to start and continue, while making clear reasoning. Because the work was made to do a rational overview of both Ruby and Python so that there won’t be any prejudice of a particular individual.
Open Access
Analyze and Development System with Multiple Biometric Identification
(2020) Dadakhanov Sh.
In the case of quick progress in technological improvement, growing shopper cheating, fraud, the threat to private knowledge is additionally increasing daily. Ways developed earlier to confirm personal info from the crimes weren't effective and safe. Statistics were introduced once it had been needed technology for more practical security of personal info. Recent ancient procedures like Personal identification number, keys, passwords, login ID may be forgotten, stolen, or lost. During a biometric identification system, the user might not get any keys or transfer any keys. In biometric authentication system, user may not remember any passwords or carry any keys. As people they recognize each other by the physical appearance and behavioral characteristics that biometric systems use physical characteristics, such as fingerprints, facial recognition, voice recognition, in order to distinguish between the actual user and scammer. In order to increase safety in 2005, biometric identification methods were developed government and business sectors, but today it has reached almost all private sectors as Banking, Finance, home security and protection, healthcare, business security and security etc. Since biometric samples and templates of a biometric system having one biometric character to detect and the user can be replaced and duplicated, the new idea of merging multiple biometric identification technologies has so-called multimodal biometric recognition systems have been introduced that use two or more biometric data characteristics of the individual that can be identified as a real user or not.
Open Access
Application for predicting the business orientation based on analysis of user desires
(2023) Anefiyayev N.
In today’s competitive business landscape, understanding customer desires and preferences is crucial for the success of any organization. Customer churn is a significant problem for companies and describes moving out of customers to competitors. Predicting such behavior in advance offers companies valuable insights, empowering them to take measures to retain their customer base and potentially d it. One key decision informed by data analysis involves identifying the development. Machine learning algorithms can data and predict business direction. This helps expand the most promising areas for business to be leveraged to analyze customer organizations, make data-driven decisions and tailor their products, services, and marketing strategies to meet customers expectations. The thesis work describes studies using their feedback, and analyzed parte for the company. To solve the problem, hms from different areas were used to how customer preferences and transactions to predict income several stages and machine learning algorithm ect for further use- Neural networks showed the best result in comparison and self business and linear regression to find out a profit. determining the direction. After that, a website and a motion of the company was visualized, and a page page where the saved model was used to predict a new partner and his income.
Open Access
Application of loT Technologies for Managing the Educational Process in University
(2019) Mamatnabiyev Zh.
Recently, applications of Internet of Things (IoT) technologies have been established in many organizations offering low-costed, low-powered, automatic systems. In addition, loT systems are secure, less time-consuming, and controlled remotely. Implementing loT technologies in managing the educational process makes huge changes by creating digital classrooms and automation systems. However, taking students’ absence report is still critical element or issues in the education sector that is more paperwork, which is time-consuming, requires much workforce aud efforts, and imposes inefficiency. Various automatic identification technologies have been developed using Radio Frequency Identification (RFID). Many research works and projects are produced to take maximum benefits of using this technology. RFID is an automatic technology and identifies tagged objects from an environment through radio waves. RFID reads data from RFID tag and sends it to server or cloud using IoT hardware platforms like microcontrollers and microprocessors. The current work proposes an automatic attendance monitoring system (AMS) using IoT technologies including RFID and hardware platforms. The objectives of the proposed system are to check attendance of students automatically with human interface, inform students about gaps in attendance, and monitoring instructors whether they come to lessons on time. Based on the results, the proposed AMS time-effective, economically available, and has not any power consumption. The system is analyzed and criticized respect to other authors' works. Future works are also discussed and identified.
Open Access
Applications of computer vision in examination proctoring
(2022) Sapargali N.
In 2019, a disease called COVID-19 hit the whole world and with the appearance of this disease, a new era of distance learning has begun. Learning has moved to apps like Google Meet, Microsoft Teams, Zoom, Webex and messengers like Whatsapp, Telegram, etc. Almost all universities and schools changed their courses to reflect what is going on in the world now. With all of this going on, their grades and scores should be going down, but many students did better than the average. This is because there has never been a way to do a well-organized test online without using different methods for each student. To solve the problem at hand, we need a system that can help us figure out how students are cheating. When it comes to online tests, the use of proctoring procedures is a big problem for the research community. This work shows us how to make a full multi-model system using computer vision so that people don’t have to be there during the inspection. We propose a system with many features that students can use during the test object identification, and estimating head posture using facial landmarks and face detection(is it the same student or another).