Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
6
Scopus Publications
Scopus Publications
Developing Future Teachers’ Competences in IT and Robotics Using Virtual and Augmented Reality: A Study of Teaching Effectiveness , Nurgul Uderbayeva, Nursaule Karelkhan, , Bakhtiyar Zharlykassov, , Tatyana Radchenko, , Aliya Imanova, and Journal of Technical Education and Training, 2025 The research addresses the need for innovative learning methods to develop competencies in future specialists, driven by rapid digitalization and globalization of social relations.The work aims to study the technologies of virtual (VR) and augmented reality (AR) in the context of developing the information and communication competencies of future teachers.Logical analysis, functional analysis, abstraction, deduction, and induction were utilized.The objects of the study were characterized, their key features were determined, and their role in the formation and development of information and communication competencies was identified.It was noted that using VR and AR technologies in the modern digital age is crucial for enhancing information literacy, communication competence, and motivation in the learning process.During the experiment, which involved senior students, namely 81 students from Kostanay Engineering and Economics University named after M. Dulatov and 60 students from U. Sultangazin Pedagogical Institute, Akhmet Baitursynuly Kostanay Regional University, a program using VR and AR technologies was developed and implemented.It was found that the level of communication competencies at the optimal indicator increased by 40%, and the learning efficiency increased by 31%.The study highlights the importance of structured training in enhancing communication competence and digital readiness among future educators in Kazakhstan.It suggests that teachers need to develop digital competencies, especially in using VR and AR technologies, to adapt to modern educational demands.This research enhances teacher education by equipping educators with essential digital skills for effective teaching.
Development and research of a generating complex for agricultural facilities using renewable energy sources Nurlan Bizhanov, Bakhtiyar Zharlykassov, Duman Utebayev, Almagul Kassymova, Oxana Telegina Polityka Energetyczna, 2025 The study was conducted to determine the possibilities and advantages of introducing a generating complex based on renewable energy sources to increase the sustainability and energy efficiency of agricultural facilities.For this purpose, a model of a generating complex using renewable energy sources to meet the energy needs of agricultural facilities has been evaluated.The impact of the integration of the Bergey Excel 10 wind turbine, the NIBE F1155 geothermal installation, and the JA Solar JAM72S20-405/PR solar panels with the SolarEdge SE40K inverter on the energy systems of agricultural facilities was considered.The integration of solar panels and low-power wind turbines significantly reduced energy costs, achieving savings from 5,500 kWh to 180,000 kWh per year compared to conventional sources.During the evaluation of the effectiveness of the complex, it was revealed that the payback period for investments in such technologies makes them economically feasible.In addition, the findings showed that the productivity of agricultural facilities has increased due to improved working conditions and lower energy costs.An analysis of the environmental impact of the generating complex showed a reduction in the level of polluting CO 2 emissions by 10-25 tonnes per year, which had a positive impact on the health of local ecosystems.The study also revealed that the introduction of renewable energy sources can be an incentive to create new jobs in the agricultural sector, which contributes to the economic development of the region.
The use of color QR codes and blockchain for secure storage and management of biometric and documentary data Kalybek Maulenov, Nazym Kaziyeva, Ruslan Ospanov, Symbat Ospanova, Assem Konyrkhanova, Bakhtiyar Zharlykassov International Conference on Artificial Intelligence Computer Data Sciences and Applications Acdsa 2025, 2025 The rapid development of digital technologies requires the creation of secure and efficient methods for storing and managing confidential personal data. Modern personal documents such as birth certificates, identity cards, and passports are often vulnerable to fraud, forgery, and unauthorized access. This article proposes an innovative system that integrates color QR codes and blockchain technology for the secure storage and management of biometric and documentary information. The proposed approach combines steganography, cryptography, and modern data embedding techniques to enhance document security. The combination of encrypted QR codes with a decentralized blockchain-based database ensures confidentiality, reliable authentication, data transparency and traceability. This solution is especially relevant for storing official documents, such as birth certificates, with the inclusion of biometric data of citizens and their family members for improved identification, as well as enabling effective data extraction and prevention of misuse.
Application of AI Techniques for Asphalt Concrete Mix Production Optimization Maira Uaissova, Bakhtiyar Zharlykassov Journal Europeen Des Systemes Automatises, 2024 In present-day conditions of road infrastructure development, ensuring the high quality of asphalt concrete mixes contributes to the durability and reliability of road pavements.This article investigates the application of artificial intelligence techniques to analyze asphalt quality aimed at optimizing production and improving the reliability of road pavements.This study introduces a pioneering approach to asphalt concrete mix quality enhancement using artificial intelligence (AI) techniques, specifically artificial neural networks (ANN) and least-squares support vector machine (LS-SVM).The application of these methods allows for carrying out efficient analysis of data, reflecting asphalt quality, predicting asphalt characteristics, and optimizing production processes.The authors conducted experiments using real asphalt properties, which were used to train and set ANN and LS-SVM models.The obtained results were compared with existing methods of asphalt quality analysis.The conducted analysis confirmed the effectiveness of using ANN and SVM to analyze asphalt quality.This approach provides an opportunity for accurate prediction of asphalt performance characteristics and production process optimization, contributing to the improvement of the durability and reliability of road pavements.The obtained results have practical significance for engineers and specialists in the field of road infrastructure construction and maintenance.The results of the study validate the superiority of AI-driven models in achieving precise and reliable asphalt mix designs, marking a considerable advancement over traditional methods.
THE USE OF ANN AND MACHINE LEARNING ALGORITHMS TO PREDICT ROAD SURFACE DETERIORATION Maira Uaisova International Journal of Geomate, 2024 Despite advancements in the application of artificial intelligence for monitoring and predicting pavement conditions, current models are not extensively utilized due to their limited adaptability and inadequate consideration of environmental variables.This study focuses on developing enhanced models for predicting the Pavement Condition Index (PCI) using artificial neural networks and the backpropagation algorithm.The aim is to improve the accuracy of the predictions.The models were trained using a dataset of 1,614 samples collected during an experiment conducted on a motorway between Kostanai and Astana.The dataset included information on asphalt pavement thickness, subgrade, traffic loads, temperature, precipitation, and deflectometer data.The architecture model with the highest performance, labeled as 9-9-1, attained peak efficiency with a value of 0.0344 after 22 training iterations.The results demonstrated a high level of accuracy, as indicated by a multiple correlation coefficient (R) of 0.954, a mean absolute error (MAE) of 0.125, and a root mean square error (RMSE) of 0.162.The developed models possess the capability to extrapolate information, adjust to variations, and accurately forecast the rate of roadway deterioration.
