Constructing Knowledge Graph in Blockchain Teaching Program Using Formal Concept Analysis Madina Mansurova, Assel Ospan, Dinara Zhaisanova International Journal of Advanced Computer Science and Applications, 2024 —The rapid evolution of blockchain technology calls for innovative educational frameworks to effectively convey its complex principles and applications. This paper investigates the use of Formal Concept Analysis (FCA) for constructing knowledge graphs as part of a blockchain teaching program. FCA, grounded in lattice theory, provides a mathematical foundation for analyzing relationships between concepts, making it an ideal tool for organizing and visualizing knowledge structure within blockchain education. This study aims to develop an interactive, context-based graph that captures the intricate interrelations among blockchain topics. The methodology includes mapping key blockchain concepts and their applications into a structured graph, which enhances both the understanding and the systematic delivery of educational content. The research demonstrates that FCA not only facilitates the creation of scalable and adaptable educational materials but also enhances students' conceptual understanding by presenting the interconnected nature of blockchain concepts in an accessible format. Knowledge graph aids in identifying interconnected learning outcomes that cover overlapping subjects. It serves as a valueable resource for educators focusing on cryptocurrencies, making it easier to create a thorough list of key topics related to particular cryptocurrency characteristics.
AI-Powered Traffic Management for Busy Intersections Didar Moldakhmetov, Madina Mansurova, Baurzhan Belgibayev, Zhanel Baigarayeva, Talshyn Sarsembayeva, et al. Proceedings 2024 20th International Asian School Seminar on Optimization Problems of Complex Systems Opcs 2024, 2024 A modern system of intelligent dispatching for a complex road infrastructure facility in the metropolis of Almaty is being presented. To improve the existing automated traffic management system (ATMS) in the city and to gradually implement digital traffic technologies with autopilot vehicles, a crucial and pressing task for IT is to replace the traffic police dispatcher-employee with a neural network-based artificial intelligence. The work demonstrates the methods and results of implementing a neural network into a mobile server program for the AnyLogic environment using the SUMO application. This approach, using the built-in TraCI connector, established connections with Python scripts of the server program AnylogicRaspberryServer.py. Testing and analysis of the research results showed the adequacy of virtual dispatching, which allowed reducing the load on the traffic police employee under moderate and average traffic conditions. It is noted that manual control of the situation at the Abay/Saina intersection is more preferable during peak hours. The obtained software products and the semi-industrial prototype of the local ISU with AI are protected by copyright certificates and have important practical significance.
The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development Assel Ospan, Madina Mansurova, Vladimir Barakhnin, Aliya Nugumanova, Roman Titkov Data, 2023 The development of knowledge graphs about water resources as a tool for studying the sustainable development of a region is currently an urgent task, because the growing deterioration of the state of water bodies affects the ecology, economy, and health of the population of the region. This study presents a new ontological approach to water resource monitoring in Kazakhstan, providing data integration from heterogeneous sources, semantic analysis, decision support, and querying and searching and presenting new knowledge in the field of water monitoring. The contribution of this work is the integration of table extraction and understanding, semantic web rule language, semantic sensor network, time ontology methods, and the inclusion of a module of socioeconomic indicators that reveal the impact of water quality on the quality of life of the population. Using machine learning methods, the study derived six ontological rules to establish new knowledge about water resource monitoring. The results of the queries demonstrate the effectiveness of the proposed method, demonstrating its potential to improve water monitoring practices, promote sustainable resource management, and support decision-making processes in Kazakhstan, and can also be integrated into the ontology of water resources at the scale of Central Asia.
Ontology-Driven Semantic Analysis of Tabular Data: An Iterative Approach with Advanced Entity Recognition Madina Mansurova, Vladimir Barakhnin, Assel Ospan, Roman Titkov Applied Sciences Switzerland, 2023 This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the semantic analysis of tables. An iterative algorithm has been proposed that can parse the contents of a table and determine cell types based on the ontology. The study presents an automated method for extracting data in various languages in various fields, subject to the availability of an appropriate ontology. Advanced techniques such as cosine distance search and table subject classification based on a neural network have been integrated to increase efficiency. The result is a software application capable of semantically classifying tabular data, facilitating the rapid transition of information from tables to ontologies. Rigorous testing, including 30 tables in the field of water resources and socio-economic indicators of Kazakhstan, confirmed the reliability of the algorithm. The results demonstrate high accuracy with a notable triple extraction recall of 99.4%. The use of Levenshtein distance for matching entities and ontology as a source of information was key to achieving these metrics. The study offers a promising tool for efficiently extracting data from tables.
Fine-Tuning the Wav2vec2 Model for Kazakh Speech: A Study on a Limited Corpus Kairatuly Bauyrzhan, Mansurova Madina, Ospan Assel Sist 2023 2023 IEEE International Conference on Smart Information Systems and Technologies Proceedings, 2023 In this study, we developed a model for automatic recognition of Kazakh speech by fine-tuning the XLSR-Wav2Vec2 pre-trained model to a corpus of Kazakh speech. Our results show that fine-tuning the wav2vec2 model on a small corpus of Kazakh speech allows a significant increase in recognition accuracy. However, larger datasets are needed to further evaluate the effectiveness of this approach. The results of this study contribute to ongoing efforts to improve speech recognition technology for low-resource languages such as Kazakh.
Development of an Application for Monitoring and Analyzing the Dynamics of the Tuyuk Su Mountain Glacier Mansurova Madina, Ospan Assel, Yerkin Kakimzhanov, Resnik Boris, Tyulyubayev Daniyar Sist 2022 2022 International Conference on Smart Information Systems and Technologies Proceedings, 2022 The Tuyuk Su glacier is a source of fresh water and is of crucial importance for the Almaty region from both an environmental and social point of view. However, the Tuyuk Su glacier continues to shrink at an alarming rate, and this will reduce the inflow of fresh water. This article presents an application for monitoring this glacier. Our approach is based on digital mapping from Landsat 7 and 8 satellite images. Remote sensing allows estimation of parameters such as snow cover, glacier height and ice index on large geographic and temporal scales. Tabular data on the area of the glacier and the balance of snowfall and melting on the glacier are also given. The result is published in a web application that allows you to visualize, select the desired boundaries of the glacier and build a graph based on the received data. The application is not yet able to automatically select the desired areas of the glacier, so the polygon tool is used here. With the help of the Timelapse tool in the application, an animated visualization of the change in the glacier has been added, which once again confirms the reduction of the glacier every year.
KazRivDyn: Toolkit for Measuring the Dynamics of Kazakhstan Rivers with a Graphics Based on Google Earth Engine Assel Ospan, Madina Mansurova, Erkin Kakimzhanov, Baurzhan Aldakulov Sist 2021 2021 IEEE International Conference on Smart Information Systems and Technologies, 2021 Now it is possible to control the change in the width of the rivers of Kazakhstan using remote sensing. This article presents a platform called KazRivDyn, developed on the Google Earth Engine cloud computing platform, to monitor changes in the width of Kazakhstan's rivers over the past 20 years, with a graph for more accurate data. Due to the fact that in Kazakhstan there is a problem of reducing the volume of water in rivers, identify the general trend of changing the volume of water, as well as turn prevention to prevent such phenomena as drought and pollution. This platform has been applied to the pool. This platform flows through two countries, the darkest as the width of the river has changed since 1984. KazRivDyn is a publicly available tool and can be used to solve scientific problems related to rivers, as well as to create applications for operational water resources management. The results obtained are close to measurements taken using manual methods, and the application works for all rivers in Kazakhstan.
