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Item Open Access Some properties of ordered algebraic structures(Faculty of engineering and natural sciences, 2019) Dauletiyarova A.Quantifier elimination is one of the most important tools in model theory. Indeed, if a theory allows quantifier elimination, then this theory is complete, and the description of all definable subsets can be reduced to describing only those subsets that are defined by a quantifier-free formula. One of the most important mathematical structures is the linearly ordered set of real numbers. On it, you can set an ordered group and field. It is known that the elementary theory of these structures admits quantifier elimination, and since these theories are computably axiomatizable, quantifier elimination implies their solvability.Item Open Access USING THE GINI COEFFICIENT TO BUILD A CREDIT SCORING MODEL(СДУ хабаршысы - 2021, 2021) Sultanova N. ; Tulegenova A. ; Suleimen B.Abstract. The credit scoring model is widely used to predict the likelihood of a customer default. To measure the quality of such scoring models, you can use quantitative indicators such as the GINI index, KolmogorovSmirnov (KS) statistics, Lift, Mahalanobis distance, information statistics. This article discusses and illustrates the practical use of the GINI index.Item Open Access ARTIFICIAL AI IN TEST AUTOMATION: SOFTWARE TESTING OPPORTUNITIES WITH OPENAI TECHNOLOGY - CHATGPT(СДУ хабаршысы - 2023, 2023) Talasbek A.Abstract. One of the most important and significant stages of the software development life cycle is software testing. Automated testing reduces testing costs and increases productivity, resulting in a high-quality and stable end product. To ensure that software is bug-free and delivers the desired user experience, test automation is critical. As technology advances, test automation becomes more complex. However, advanced Al technologies will soon become commonplace thanks to powerful tools like ChatGPT that, among countless other things, can chat with you and teach you how to read and write code like a human. In this article, we will try to consider the possibilities of automation with chatgpt. Starting with writing test plans, and test scripts by using Python and Selenium WebDriver. How ChatGPT can improve our tasks and make them more feasible. By leveraging the capabilities of ChatGPT, software testing engineers can enhance their skills, improve testing capabilities, and achieve better quality and accuracy of test results. The opportunities presented by ChatGPT can lead to improved efficiency, productivity, and overall performance in the field of software testing engineering.Item Open Access COMPARATIVE ANALYSIS OF SORTING ALGORITHMS USED IN COMPETITIVE PROGRAMMING(СДУ хабаршысы - 2021, 2021) Mamatnabiyev Zh. ; Zhaparov M.Abstract. Sorting is one of the most used and fundamental operation in computer science since the first time it had been tried to arrange list of items in some sequence. A large number of sorting algorithms were designed in order to have best performance in terms of computational complexity, memory usage, stability, and methods. In addition, in algorithmic problem solving, not all algorithms have same efficiency. This paper makes comparison research and discusses four different types of sorting algorithms: bubble sort, quick sort, insertion sort, and merge sort. The algorithms are tested and compared for data usage and time spent for sorting given amount of data.Item Embargo The Art of Personalized Student-Supervisor Matchings(ADAL Kitap, 2025) Serek A.G.; Berlikozha B.A.The process of student-supervisor matching is a critical yet complex task in higher education institutions, directly influencing research productivity, student satisfaction, and workload distribution. The ability to assign students to the most suitable supervisors is essential for fostering strong academic relationships, optimizing institutional resources, and improving research outcomes. However, traditional manual assignment methods often lead to inefficiencies, subjective biases, and an imbalance in workload distribution. As a result, automated recommendation systems have emerged as a promising solution to enhance the efficiency and fairness of student-supervisor pairings. This study evaluates three recommendation algorithms—Singular Value Decomposition (SVD)-based collaborative filtering, graph-based matching using the Hungarian Algorithm, and machine learning via Random Forest Regression—to determine their effectiveness in optimizing student-supervisor assignments. A rigorous empirical analysis is conducted across five key performance metrics: accuracy, fairness, stability, scalability, and computational efficiency. The findings reveal that while collaborative filtering performs well with established datasets, it struggles with novel cases due to its dependence on prior interactions. The Hungarian Algorithm guarantees optimal matching but faces scalability challenges, particularly in large academic institutions with thousands of students and supervisors. Meanwhile, Random Forest Regression effectively captures complex compatibility patterns but requires extensive labeled data, limiting its applicability in cases where historical matching data is sparse or unavailable. To overcome these limitations, the study proposes an adaptive hybrid framework that integrates the strengths of all three approaches. The hybrid model leverages collaborative filtering’s ability to recognize patterns in existing data, the Hungarian Algorithm’s precision in optimal pairings, and the predictive power of machine learning. By combining these methodologies, the proposed system enhances match accuracy, ensures fair workload distribution, and remains computationally efficient for large-scale institutional implementation. Additionally, the framework introduces dynamic adaptation mechanisms that allow the system to update recommendations based on real-time changes in student preferences and supervisor availability, making it more practical for real-world applications. The research contribution is a comprehensive, empirically validated hybrid framework that improves student-supervisor matching by balancing accuracy, fairness, and efficiency. This study provides educational institutions with actionable guidelines for scalable and equitable assignment processes, ultimately contributing to more effective mentorship experiences, improved research collaborations, and enhanced academic outcomes.Item Open Access STUDY OF SQUARE EQUATIONS IN THE 8TH GRADE USING MODULAR TECHNOLOGY(СДУ хабаршысы - 2019, 2019) Sabyrbayeva D. ; Borambayeva S.Abstract. The article gives a description of one embodiment of the modular technology in teaching students math. The analysis of the requirements for learning outcomes, the study objectives and data integration capabilities in the process of the study. Just a list of tasks and issues for study in each of the lessons in both integrated classes, and the framework of the modular study. The question of the use and visualization tools as well as the combination on of this material and other items.Item 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.Item Open Access IMPLEMENTING COURSE SCHEDULE GENERATION APPLICATION FOR UNIVERSITY(СДУ хабаршысы - 2021, 2021) Zhuniskhanov M. ; Suliyev R.Abstract. This work is about researching and implementing Course Schedule Generation Application for university. She compares other similar applications in this area and explores important points. Through UML diagrams, important concepts and development progress are explained. Screenshots of using the application will also be provided. Dataset, configuration and rules explained. In the end, we will talk about the importance of such an application and facilitate planning for the university.Item Open Access HANDWRITTEN OPTICAL CHARACTER RECOGNITION: IMPLEMENTATION FOR KAZAKH LANGUAGE(СДУ хабаршысы - 2021, 2021) Kalken M.Abstract. Many documents, including as invoices, taxes, memoranda, and surveys, historical data, and test replies, still require handwriting with the transformation to digital information interchange. Handwritten text recognition (HTR), which is an automatic approach to decode records using a computer, is required in this aspect. For this proposal, I present a study of the implementation of optical recognition algorithms for handwritten text in the Kazakh language, using a recently collected database. The database, called the Kazakh Autonomous Handwritten Text Dataset (KOHTD), contains more than 140,335 segmented images of handwritten exam papers. As an algorithm, I used the proposed model by Harald Scheidl, which consists of several layers of neural networks and an CTC decoder. The trained model by putting an interval of Ir = 0.01 and a batch size of 60 showed effective results with indicators of about 85% accuracy.Item Open Access PARAMETERS OPTIMIZATION OF DECISION TREE AND KNN ALGORITHMS FOR BREAST CANCER PREDICTION(СДУ хабаршысы - 2017, 2017) Meraliyev M.M. ; Orynbekova K.Ye. ; Hasanov D. ; Zhaparov M.K.Abstract. Throughout the 20th century, views about breast cancer have drastically changed. Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. This type of cancer is the second most common cancer overall. There is lot of information and data, which give opportunity for analyzing some processes, make some researches in classification and in data mining fields, test some tools of machine learning and make experiments for tuning main methods of supervised learning. Main part of project is creating useful tool for predicting breast cancer with high accuracy before getting ill or in initial stage of disease. This work is fascinating because the goal is to implement a lot of tools for creating web system, which can make effective prediction analysis. In other word, we can anticipate the future for women diseases.