209 results
Search Results
Now showing 1 - 10 of 209
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 MICRO-TRAINING IN FLASH FOR KAZAKH-TURKISH HIGH SCHOOLS(2013) Hamzin A.In this dissertation work was considered a new conception of education methods like Micro-training in Flash for Kazakh-Turkish high schools. Educational system of our country is developing by Memorized Learning methods that are why I used to practice this method of educating. The last point of Memorized knowledge is illiteracy of our students at all. Starting up of kinder garden we must teach our kids not to use memorized learning. I created web site for teaching students to self-studying. I was using this web page in Astana KazakTurkish high school for gifted girls and the result was very attractive and useful. Kazak-Turkish high School educating system is very suitable for Micro-training education system.Item Open Access General Framework for implementation of Open Source ERP system in Kazakhstan’s SME’s(Faculty of Engineering and Natural Science, 2013) Zhunussov A.This research has been divided into six main parts. Listed below are the chapters with a brief description of each. Introduction to study chapter gives an introduction to this research. The history of ERP, as a background to the research and introduction to the OS ERP systems, is given as a key target of this study. All the necessary points of research, such as the problem statement, aim, and objectives, are each briefly described. Finally, the methodology, limitations and definition of terms are also given. The purpose of literature review chapter is to provide and review the available literature on ERP. A brief definition and benefits of ERP will be given in this chapter. As the focus of this research is OS ERP systems, this chapter will include a review of the OS ERP system as proposed by other researchers. This will be followed by identifying the selection criteria of OS ERP systems and implementation critical success factors. ‘ Methodology chapter will describe the methodology used for the collection of primary and secondary data for this research. It will give the reader complete and appropriate information about research methods, approaches and strategy, with a brief description of why these have been chosen. Besides, it will include a description of research procedures, sampling and methods of data collection, and data analysis. The chapter will conclude with an ethical consideration and conclusion. In presentation and analysis of data chapter, the result of the questionnaire survey is given. There will be an analysis of the data, which will be shown in the form of tables and charts. Each question’s results will be provided and analysed. All the necessary findings for the research will be highlighted during the analysis and in the conclusion. Artefact chapter will review and summarize the data analysis and evaluate the findings. In order to address the research question, the chapter will also show how the data analysis is used to create an artefact. It will be followed by an outline of literature review findings to support the artefact. Analysing the artefact will conclude the chapter.Item Open Access Development of transcript-Driven IT Specialization Recommendation System using ML(Faculty of Engineering and Natural Science, 2024) Myrzabayeva A.Recommendation systems in education are pivotal for guiding students through their academic and career paths. However, traditional systems often fail to address the unique challenges and rapid changes within the Information Technology (IT) sector. This study proposes a machine learning-driven approach to enhance the precision and personalization of IT career guidance.This research develops a sophisticated machine learning model using a variety of algorithms, including Random Forest, Logistic Regression and Decision Trees, to analyze and process detailed student transcripts. The study aims to predict and align students’ IT specializations with both their capabilities and market demands. A robust validation framework, including cross-validation and algorithm comparison, ensures the accuracy and reliability of the recommendation system. The model demonstrates a high degree of predictive accuracy, outperforming traditional recommendation systems. It effectively identifies individual strengths and market opportunities, providing tailored recommendations that improve educational outcomes and job market readiness. Integrating machine learning with educational recommendation systems offers a promising avenue for addressing the specialization needs within the IT sector. By leveraging detailed transcript data and advanced predictive analytics, the proposed system aligns educational paths with professional demands, enhancing student employability and meeting industry needs.Item Open Access Development of an Exam application using Adaptive Learning algorithms(Faculty of Engineering and Natural Science, 2013) Syzdykov R.Web-based exams are becoming increasingly popular in the last years; however, most of the exams developed using static exam content, so that students access to the same content irrespective of different learning backgrounds, learning styles, knowledge levels and abilities. It's a challenge to develop advanced Web-based exam application that can offer both adaptivity and intelligence. This study presents a novel approach to design an exam application which includes major adaptive features. The student, domain and exam content are separately designed to support adaptive learning. Application tracks students’ answers during the exam phase. The results are analyzed according to the Item Response Theory (IRT) in order to calculate students” abilities. The student model is updated based on exam results. The updated student model is used to generate learning style and knowledge level of each learner.Item Open Access Creating three dimensional internal organs for scanning using simulator(2019) Tursynbekova A.This thesis is a simulation of scanning the human heart in real time. which will be useful in medical and educational practice in the diagnosis of various diseases of the cardiovascular system. Currently there is an extreme importance of the problem of cardiovascular diseases. According to statistics. they confidently occupy the first place among the causes of death and disability of the population. Diagnosis of cardiovascular diseases is a serious problem. In medicine. modeling of individual organs and the body as a whole is an important task. the successful solution of which will allow you to quickly, accurately and timely identify pathologies, as well as determine the optimal therapeutic effect and predict its consequences. In this thesis, the structure of the human heart was studied and a model of the human heart was created on the basis of Blender software as a basis for simulating the scanning process. From the existing various methods of segmentation of a three-dimensional object, the mesh slicing method was taken as the basis. For the implementation of this project. the Unitv3D game development platform was chosen. The end of this work is an original simulator application for scanning a person’s heart.Item Open Access University course recommendation system(2022) Anefiyayeva D.In universities with a credit system, students are given a choice of subjects - elective courses. There is a whole list of subjects of different profiles from which they must choose themselves. This decision will affect the student’s future career, as he will choose not only the subject studied but also what he will do in the future. Understanding how difficult a choice needs to be made, and how difficult it is to choose a subject, it was decided to create a recommendation system for subjects. One of the most used applications of artificial intelligence is the recommendation engine. Since the advent of the Internet, recommender systems have become more and more common in everyday life. Recommender systems can be implemented using various machine learning methods, but this task attracts a large number of scientists from all over the world. Let’s start with how to classify k-means while comparing two different approaches. The system was developed using machine learning technology and cross-platform application development. The algorithm was based on data that was collected from various public sources. The relevance of this topic led us to create an application, two types of recommender systems: 1) Entering the subject that the student liked, the system will give him a list of subjects with similar skills 2) Entering your interests and abilities, the system gives out an item that is most suitable for your preferencesItem Open Access Identification of Students at Risk of Not Completing the Course Using Machine Learning(2023) Bairamova D.This dissertation is dedicated to the topic of identifying students at risk of not successfully completing a course at an early stage of their education using machine learning algorithms. In this study, the final exam score, academic performance category, and the risk group of students failing to complete the course are determined for accurate and detailed identification of students at risk. Research shows that machine learning algorithms such as LightGBM Regressor (for the final exam score prediction), Logistic Regression (for identifying two groups of students - those who will complete and those who will not complete the course), and K-Means (for identifying the academic category) can help identify students who need assistance from teachers with high accuracy of prediction. Detecting this group of students at an early stage of their education can enhance students’ motivation for further learning and assist teachers in individually and timely identifying which student requires help.Item Open Access Image scene understanding by using Recurrent Neural Networks(2020) Aldabergen А.This work proposes an implementation of Recurrent Neural Networks (RNN) for image scene understanding. Task is clear: given an image and the system should provide an accurate description for the given image. The novelty of the work is that this system is realized on Telegram Bot. Fine tuned model learns where to look, its focus is shifted across the image by the help of attention mechanism. Thus the model was able to find the most relevant parts of the image and find out most relevant words that describe the scene. It has an encoder-decoder architecture. As own contribution, transfer learning is implemented on pre-trained model. The significance of the work is that this kind of system can be easily implemented in bunch of areas of our life rather than other capturing applications.Item 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.