508 results
Search Results
Now showing 1 - 10 of 508
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 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 PUBLIC SCHOOL TEACHERS’ ATTITUDE TOWARD TEACHING MATHEMATICS WITH TECHNOLOGY(2021 International Young Scholars' Conference, 2021) Ualisheva TotyAbstract The 21st century is impossible to imagine without technology. Everyone knows that online learning is not surprising in the current pandemic. However, is the use of these technologies always convenient for the teacher and how effectively can teachers use the technology in their lessons? The article discusses the approach of secondary school teachers to the learning process using digital technologies. The main forms of teaching mathematics using digital technologies are also presented. In particular, I will consider the problems faced by teachers and the benefits of digital learning. In this article, we used a survey tool, during which we see that many secondary school teachers in Kazakhstan have a positive attitude to digital education and that since the beginning of the pandemic period, teachers have developed professionally in the process of using technology. Analyzing the responses to the survey, it was shown that teachers are highly qualified in the use of technology and actively use technology in their lessons.Item Open Access THE WAYS TO IMPROVE INFORMATION AND COMMUNICATION TECHNOLOGY SKILLS OF EFL TEACHERS(СДУ хабаршысы - 2021, 2021) S. Kazybay; D. GaipovAbstract. ICT plays an important role in the maintenance of Education. In the XXI century, ICT has become a central issue for educators to integrate into the curriculum, thus leading to high performance of learners in the classroom. This study focuses on EFL teachers in secondary schools with different years of experience. This paper analyzes challenges/problems teachers face while using ICT, the level of ICT usage among the secondary school English Language Teachers and teachers’ perceptions towards integrating ICT in an English classroom. The application of technology can progress interest and inspiration of learners. Innovation offers second language learners information, outcomes and feedback; it also offers teachers efficient way of coordinating course contents and to communicate with many learners. Nevertheless, the usage of technology is encouraged and instructors can alter their teaching practices and instructional approaches, so that, available tools are utilized in the most efficient manner. It is recommended that caution to be taken when using technology to support language education.Item Open Access Library Digest No 9(SDU University, 2024-12) Scientific LibraryДайджест Научной библиотеки представляет актуальные события, обновления и ключевые инициативы, направленные на поддержку образовательной и исследовательской деятельности университета. Выпуск включает самые важные новости о ресурсах, сервисах, проектах и достижениях библиотеки, обеспечивая читателей удобным и кратким обзором последних изменений.Item Open Access PRINCIPAL COMPONENT ANALYSIS AND A MULTILINGUAL CONSTRUCT TO DETERMINE THE UNDERGRADUATE MAJOR SELECTION FACTORS(СДУ хабаршысы - 2020, 2020) Assanbayeva G. ; Kadyrov Sh.Abstract. In this article, we review mathematics behind well-known Principal Component Analysis from Linear Algebra implemented in various applied fields. As an application, we develop a construct to measure factors that affect college students in their major selection. This is a multilingual construct given in three languages, namely Kazakh, Russian, and English. To this end, we prepare a survey consisting of 27 Likert scale items in three languages and it is conducted among 314 undergraduate students in Kazakhstan. For dimensionality reduction, Principal Component Analysis is carried in python programming language which resulted in 9 major scales with only 22 elements. The overall reliability of the test is calculated to be 0,856. The nine scales are the effect of Uniform National Testing, state grant affect, personal interest affect, skills affect, occupation salary affect, teacher affect, external affect, university cost affect, parent’s affect.