Scientific Journal of Astana IT University

THE IMPACT OF AI AND PEER FEEDBACK ON RESEARCH WRITING SKILLS: A STUDY USING THE CGSCHOLAR PLATFORM AMONG KAZAKHSTANI SCHOLARS

Raigul Zheldibayeva — Sun, 30 Mar 2025 00:00:00 +0500

This research studies the impact of AI and Peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform − the product of cutting-edge research and development into collaborative learning, big data, and artificial intelligence developed by educators and computer scientists at the University of Illinois Urbana-Champaign (UIUC). The study aimed to find out how familiarity with AI tools and peer feedback processes affects participants’ openness to incorporating feedback into their academic writing. The study involved 36 Bolashak scholars enrolled in a scientific internship focused on education at the University of UIUC. A survey with 15 questions with multiple-choice, Likert scale, and open-ended questions was employed to collect a data. The survey was conducted via Google Forms in both English and Russian to ensure linguistic accessibility. Demographic information such as age, gender, and first language were collected to provide a nuanced understanding of the data. The analysis revealed a moderate positive correlation between familiarity with AI tools and openness to making changes based on feedback, and a strong positive correlation between research writing experience and expectations of peer feedback, especially in the area of research methodology. These results show that participants are open minded to AI-assisted feedback, however they still highly appreciate peer input, especially regarding methodological guidance. This study demonstrates the potential benefits of integrating AI tools with traditional feedback mechanisms to improve research writing quality in academic settings. Further research is recommended to evaluate the long-term impact of AI and peer feedback on academic writing skills, particularly through longitudinal studies that assess skill retention over multiple feedback cycles. Additionally, expanding the study to include a more diverse academic audience will provide deeper insights into how feedback mechanisms function across different research cultures and disciplines.

DEVELOPMENT OF METHODOLOGY FOR GIS ASSIGNMENTS IN FLOOD MAPPING USING BLOOM’S TAXONOMY AND SCAFFOLDING APPROACH

Sun, 30 Mar 2025 00:00:00 +0500

This paper presents a study on designing and implementing a series of GIS assignments for an educational course on flood mapping, structured using Bloom’s Taxonomy and the scaffolding teaching method. Geographic Information Systems (GIS) education often involves the acquisition of complex technical skills, requiring a structured learning approach to ensure a progressive mastery of concepts. In this study, a sequence of practical assignments was developed at increasing levels of complexity corresponding to Bloom’s cognitive levels, from basic knowledge acquisition to higher-order evaluation tasks. The scaffolding approach was utilized to facilitate student learning, wherein extensive guidance was provided in early tasks and gradually removed in later ones as students gained competence. The research was conducted in an upper-level undergraduate course, “Methodology for Mapping Flood Emergency Areas”, at the Sarsen Amanzholov East Kazakhstan University, with 21 enrolled students. The assignments integrated real-world flood mapping scenarios using GIS tools such as ArcGIS Pro and QGIS, enabling students to apply theoretical knowledge in practical settings. Results from the study indicated that a structured, scaffolded approach significantly improved student performance and confidence in GIS skills. Quantitative analysis of assignment grades showed steady improvement as students progressed to more complex tasks, while qualitative feedback revealed high engagement and perceived learning value. The findings underscore the effectiveness of combining Bloom’s Taxonomy with scaffolded instruction in GIS education, providing a practical framework for curriculum design. This approach has the potential to enhance learning outcomes in technical subjects, particularly in geospatial analysis, and offers recommendations for educators on implementing scaffolded assignments effectively. Further research could explore long-term skill retention and the application of this methodology in other technical disciplines.

CONSTRACTION OF DISTRIBUTION MODELS OF THE UNIVERSITY EDUCATIONAL WORK VOLUME

Diana Chigambayeva, Gulzhan Soltan — Sun, 30 Mar 2025 00:00:00 +0500

With the advent of new time requirements for the quality of educational services, which is influenced by management in functioning business processes, existing research in the field of resource allocation in the management of complex processes, namely the calculation of the teaching load of university teaching staff, was studied. The purpose of the research in this article is to develop functional and mathematical distribution models of the university educational work volume, as well as an algorithm for optimizing the generation of educational flows and initialization of academic groups, taking into account the specifics of disciplines and classroom fund. The algorithm is based on the construction of all business processes implemented during the formation of educational streams and groups. The functional model described for the process of distributing the volume of educational work includes the definition of the main functions, their relationships, input and output data, as well as the criteria and restrictions that govern this process. The mathematical model is based on the representation of all types of educational work of departments of educational programs as a discrete set of resources that must be distributed between educational departments in accordance with the assumptions and restrictions accepted at the university. Data mining and operations research techniques were used to write the functional model. Empirical and quantitative methods were used to write a mathematical model. Thus, a new methodology has been developed for solving complex optimization problems that arise when modeling and optimizing the distribution of the volume of educational work of a university. It should be noted that comparative experiments under labor-intensive and time-limited conditions confirm the effectiveness of this technique in solving problems of distributing the amount of educational work among departments of educational programs, which in turn contributes to the implementation of high-quality software.

COMPARATIVE ANALYSIS OF VARIOUS FORECAST MODELS OF ELECTRICITY CONSUMPTION IN SMART BUILDINGS

Akylbek Tokhmetov, Kenzhegali Nurgaliyev, Liliya Tanchenko — Sun, 30 Mar 2025 00:00:00 +0500

The rapidly growing field of smart building technology depends heavily on accurate electricity consumption forecasting. By anticipating energy demands, building managers can optimize resource allocation, minimize waste, and enhance overall efficiency. This study provides a comprehensive comparative analysis of various models used to forecast electricity consumption in smart buildings, highlighting their strengths, limitations, and suitability for different use cases. The investigation focuses on three major categories of forecasting models: statistical methods, machine learning techniques, and hybrid approaches. Statistical models, such as the Moving Average Method, leverage historical data patterns to predict future trends. These models enable analysts to utilize predictive analytics, simulating real-world environments and helping them make more informed decisions. The study offers a detailed comparison of several predictive models applied to Internet of Things (IoT) data, with a particular emphasis on energy consumption in smart buildings. Among the short-term forecasting models examined are gradient-enhanced regressors (XGBoost), random forest (RF), and long short-term memory networks (LSTM). The performance of these models was evaluated based on prediction errors to identify the most accurate one. Time series, machine learning, and hybrid models used to predict energy consumption are considered and analyzed. The focus is on the accuracy of forecasts and their applicability in real-world conditions, considering factors such as climate change and data obtained from Internet of Things (IoT) sensors. The analysis shows that hybrid models combining machine learning and time series provide the best prediction accuracy over different time horizons. It also highlights the importance of integrating user behavior data and using IoT technologies to improve model accuracy. The results can be applied to create energy-efficient control systems in smart buildings and optimize energy consumption.

DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS

Sun, 30 Mar 2025 00:00:00 +0500

In the rapidly evolving real estate market, the application of machine learning (ML) is crucial for understanding and predicting price trends. This study evaluates and compares seven ML models, including multiple linear regression, random forest regression, support vector regression (SVR), decision tree regression, and XGBoost, to determine the most effective predictor of real estate prices in Astana, Kazakhstan. The study focuses on the Yesil district, a key area in the city, utilizing a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan. Through rigorous experimentation, model performance was assessed using statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results indicate that the Random Forest Regressor and XGBRegressor models outperformed others, achieving the highest R-squared values (99.55% and 99.18%, respectively) and the lowest MAE and RMSE values. These findings highlight their robustness in predicting housing prices with high accuracy. The primary objective of this study was to develop a precise ML model capable of accurately forecasting real estate prices in Astana based on key market attributes. The superior predictive performance of the Random Forest and XGBRegressor models justifies their selection for deployment in real-world applications. Their high predictive accuracy suggests their potential utility for real estate professionals, policymakers, and investors seeking data-driven insights into market dynamics. This research expands knowledge on the applications of ML in the real estate sector, reinforcing the importance of evidence-based decision-making within the industry.

FEATURE SELECTION METHODS FOR LSTM-BASED RIVER WATER LEVEL AND DISCHARGE FORECASTING

Almas Alzhanov, Aliya Nugumanova — Sun, 30 Mar 2025 00:00:00 +0500

Accurate forecasting of river discharge and water levels is essential for effective water resource management, flood mitigation, and public safety. This study compares correlation-based and PCA-based feature selection methods for LSTM forecasting models in the study area at Uba River basin, within Shemonaiha city in the East Kazakhstan region. The dataset spans from 1995 to 2021, with 1995 to 2019 used for training and validation and 2020 to 2021 for testing. Both feature selection methods reduced the original predictor set to 13 features while generally maintaining predictive accuracy. An ensemble of 10 LSTM models was trained using 60-day input sequences to forecast discharge and water levels over a 10-day horizon, reducing variance from random initialization and stabilizing predictions. Performance was evaluated using the Nash-Sutcliffe Efficiency. Results showed that correlation-based selection performed comparably to the full-feature baseline in 2020 test set, suggesting that removing highly correlated predictors did not decrease short-term forecasts capacity of the model. The model with PCA-based selected features, while slightly lagging at longer lead times in 2020, exhibited advantages in most lead times with 2021 forecasts. However, overall predictive performance declined in 2021 compared to 2020, indicating that the hydrological conditions deviate more from the historical training record, and suggesting the need for model updates with relevant historical training data. Both feature selection methods successfully reduced dimensionality, while preserving performance capacity, though neither was universally superior across all forecast lead times. These results emphasize the value of systematic feature selection in hydrological modeling and highlight the importance of model adaptability to evolving environmental conditions.

EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN

Sun, 30 Mar 2025 00:00:00 +0500

This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas in Kazakhstan and its neighboring regions. By leveraging a comprehensive dataset encompassing significant earthquake data from 1900 to 2023, various machine learning algorithms were employed, including RandomForest, GradientBoosting, Logistic Regression, Support Vector Classification (SVC), K-Nearest Neighbors (KNeighbors), Decision Tree, XGBoost, LightGBM, AdaBoost, and MLPClassifier. The primary objective was to analyze and compare the performance of these models in predicting earthquake magnitudes and frequencies. The results reveal that certain algorithms significantly outperformed others in terms of accuracy, underscoring the potential of machine learning techniques to enhance earthquake prediction capabilities. Notably, XGBoost and RandomForest demonstrated the highest predictive accuracy, suggesting their suitability for application in seismic risk assessment. These findings offer valuable insights for governmental agencies engaged in disaster management and prevention planning, highlighting the practical implications of integrating advanced analytical techniques in their strategies. In addition to model performance analysis, a visual heatmap was generated to illustrate the geographical distribution of earthquake occurrences across the studied regions. This visual representation effectively identifies high-risk areas, serving as a crucial tool for local authorities and researchers in making informed decisions regarding safety measures and emergency preparedness. This research contributes to the expanding body of knowledge on earthquake prediction utilizing machine learning, emphasizing the necessity for continuous improvement in predictive models by incorporating additional environmental and geological factors. The implications of these findings extend beyond academic discourse, holding significant potential for enhancing public safety in regions vulnerable to seismic activity. As such, this study advocates for the integration of machine learning methodologies in disaster management frameworks to mitigate risks and enhance preparedness in earthquake-prone regions.

NEW APPROACH TO ADDRESSING CLASS IMBALANCE IN MEDICAL DATASETS CONSIDERING SPECIFICS

Zholdas Buribayev, Ainur Yerkos, Zhibek Zhetpisbay — Sun, 30 Mar 2025 00:00:00 +0500

Currently, the popularization of the integration of machine learning into the field of medicine for data processing and analysis is being traced, but at the same time difficulties such as class imbalance and noisy datasets arise. Due to the prevalence of the problem, there are already existing solutions, but in all of them there is an abstraction from the field of medicine, namely, gender, racial and other differences are not taken into account. It is this side of the problem that is solved in our resampling algorithm. A feature of our algorithm is the use of splitting the dataset by an important feature through the p-value of Spearman correlation, which helps to consider subgroups of observations without losing their unique characteristics and removing noise data using LOF and Z-score separately for minority and majority classes, respectively. Synthetic data is generated in a flexible way, adapting to the data set using algorithm parameters. Work is provided with both quantitative and nominative features. The algorithm was tested on datasets for heart attack, chronic kidney disease, and liver disease, and the Random Forest ensemble method was used to train the model. After applying this class balancing method, improvements were recorded on average in Accuracy by 36%, in AUC by 15-25%, in Precision by 39-42%, and in Recall by 21-37% compared with SMOTE, ADASYN algorithms and the data set before balancing. Applying the algorithm on medical data can improve the accuracy of the algorithm and reduce the loss of reliability compared to other resampling methods.

ARTIFICIAL INTELLIGENCE-ENHANCED MOBILE DIAGNOSTICS USING DECISION TREES FOR EARLY DETECTION OF RESPIRATORY DISEASES

Sun, 30 Mar 2025 00:00:00 +0500

This article is devoted to the identification of early diagnosis of respiratory lung diseases, such as chronic obstructive pulmonary disease and pneumonia, to reduce mortality and prevent complications. One of the most effective methods of structuring data is the Decision Tree model. The research focuses on the development and evaluation of a decision tree model, which is used to obtain data in the form of questionnaires, text files from patients, where they describe in detail the entire process of the disease, describing their symptoms and general condition at different time periods. There are a few criteria that patients must answer for a more accurate diagnosis. The developed methodology will allow processing relevant data with various symptoms to obtain reliable identification of the signs of the disease, as well as the stages of its progression; this can be done without the use of complex and high-tech devices that make diagnosis very accessible and feasible in the shortest possible time, if resources and time are limited. The article describes the model, carefully collected, and processed the necessary data, and then the results will be described in detail, covering many indicators such as accuracy, responsiveness, F1 score and ROC-AUC. The results of this analysis strongly suggest that this model is effective enough to provide a high level of accuracy combined with extensive capabilities, which determines its practical importance for use in real conditions. It is noted that the decision tree model can significantly improve the quality of diagnostics, since it is possible to structure a large amount of data and thus collect many years of human experience.

USING MLOPS FOR DEPLOYMENT OF OPINION MINING MODEL AS A SERVICE FOR SMART CITY APPLICATIONS

Sun, 30 Mar 2025 00:00:00 +0500

This paper presents the MLOps strategy, which adapts the automation principles of DevOps to the deployment and lifecycle management of artificial intelligence (AI) models. By leveraging high-performance automation, MLOps ensures seamless AI development and operations integration, enabling efficient and reliable model deployment. The study demonstrates this approach by implementing the Astana Opinion Mining macro-service customized for sentiment analysis. This macro-service evaluates public opinions based on a criteria taxonomy for assessing the urban environment's sustainable development. As a smart city application, the system facilitates the collection and analysis of citizen feedback to assess the performance of city services and inform urban planning decisions. Technologically, the MLOps strategy employs containers and microservices to construct robust data and process pipelines. Four core pipelines were developed in this research: data collection, feature engineering, experimentation, deployment, and maintenance. The data collection pipeline is achieved through automated crawling from diverse sources such as social media and other internet platforms. The feature engineering pipeline ensures data preprocessing by removing noise, identifying message languages, categorizing topics, and preparing data for further analysis. The experimentation pipeline incorporates services for data labeling, model training, and performance evaluation customized to sentiment analysis tasks. Finally, the deployment pipeline and maintenance pipeline deliver trained models to end-users, ensuring their continual improvement and adaptation. Using this MLOps framework, four models of sentiment analysis were tested in Russian: "Blanchefort," "Sismetanin," "MonoHime," and "Dostoevsky." The "Blanchefort" showed an accuracy of 71,43%. The resulting MLOps framework is fault-tolerant, scalable, and enables real-time urban environment assessments. By automating workflows, the architecture enhances operational efficiency, offering practical applications for smart city initiatives and sustainable urban development, contributing to better decision-making.

ASSESSMENT OF THE STATE OF DISRUPTIONS IN THE POWER SUPPLY SYSTEM OF A MOBILE COMMUNICATION BASE STATION

Dilmurod Davronbekov, Muhammad Muradov — Sun, 30 Mar 2025 00:00:00 +0500

This study provides an in-depth analysis of power supply interruptions at mobile communication base stations (BS) operated by the Khorezm branch of Uzbekistan’s Uzmobile national mobile operator. The primary objective of this analysis is to evaluate the duration of power supply interruptions and their impact on the operational performance of base stations. In the case of the Khorezm region, data on power supply interruptions collected over one year from all districts were examined. According to statistical data, 13 base stations with the highest number of interruptions were selected for detailed analysis. The frequency, duration, and causes of these interruptions were studied to assess the reliability of the power supply system. The assessment results revealed distinct characteristics of power interruptions across different areas of the region. Special attention was given to evaluating the reliability of base stations from the perspective of power supply stability. The stability of the power supply system was used as the primary criterion in the analysis. The resilience of base stations to interruptions and the efficiency of their service were compared based on the frequency and duration of power outages. Additionally, the geographic location of the stations, the reliability of their connection to the electric grid, and other external factors were analyzed. The analysis identified significant differences in the intensity of power interruptions between districts in the Khorezm region. In some areas, the high frequency of interruptions was attributed to issues within the local energy infrastructure or natural conditions. Furthermore, the data enabled an assessment of how power supply interruptions affect the uninterrupted operation of base stations. This study draws important conclusions regarding the reliability of mobile communication infrastructure components, particularly base stations, in the Khorezm region. The findings emphasize the need for further research into eliminating energy supply issues, improving the efficiency of base stations, and enhancing the quality and continuity of communication services. The results of the analysis pave the way for developing technical and technological solutions to improve the reliability of base stations. Specifically, the implementation of alternative energy sources, such as supercapacitor banks or backup batteries, is recommended to provide rapid responses to power interruptions. Additionally, the advancement of monitoring and automated control systems is identified as an effective means to ensure the stability of base stations. This research serves as a crucial scientific and practical foundation for devising measures to improve the reliability of power supply systems in mobile communication networks.

LEVERAGING BIG DATA FOR DOG HEALTH ANALYSIS: AN EXPLORATORY STUDY USING "TANBA" IN KAZAKHSTAN

Sun, 30 Mar 2025 00:00:00 +0500

In the era of artificial intelligence, collecting and analyzing data about dog health through electronic medical cards and passports has become a key factor in improving the quality of life for pets. In this study there was analyzed 93,922 records about dogs contained in Kazakhstan's pet registration information system “Tanba”. The research focused on the demographic characteristics of dogs, including breed, age, and region of residence. Explanatory Data Analysis was conducted using descriptive statistics, and Natural Language Processing (NLP) methods were applied to standardize breed names, improving data consistency. Additionally, an ANOVA test was performed to assess the impact of factors such as gender, region, breed, and breed size on dogs' lifespan. Based on the data analysis, there are highlights of key aspects such as the predominance of young dogs (average age 5.52 years), the high proportion of dogs without breed, and the high concentration of stray animals in some regions, which emphasizes the need for increased efforts to control the population and improve living conditions for stray dogs. This study presents an analysis of the dog population for 2024 based on data from the Tanba national registration system. Unlike previous studies that focused on the prevalence of individual diseases or were limited to data from specific regions, this study covers the entire country and provides a general overview of the dog population. The findings indicate a high proportion of mixed-breed and stray dogs in Kazakhstan, as well as significant regional differences in canine lifespan. Breed and regional factors have a statistically significant impact on lifespan, emphasizing the importance of considering these characteristics when developing programs to improve animal welfare and veterinary care. In the future, it is planned to improve data processing algorithms and expand the use of additional sources of information, which will allow for more accurate assessment of dog health risks and development of more effective preventive measures.

ANALYSIS OF TECHNICAL FEATURES OF DATA ENCRYPTION IMPLEMENTATION ON SD CARDS IN THE ANDROID SYSTEM

Sun, 30 Mar 2025 00:00:00 +0500

This article provides a detailed analysis of data encryption mechanisms for removable storage devices in the Android operating system. Two main information protection technologies are examined: file-based encryption when using an SD card as portable storage and full-disk encryption when using a memory card as an extension of the device's internal storage (Adoptable Storage). The technical implementation features of each method are investigated, including the encryption algorithms used, the structure of encrypted data, and key storage mechanisms. The research was conducted using Sony Xperia XZ and Xiaomi Redmi 5 Plus devices, employing tools for working with file systems and encryption based on Linux and Android. The analysis has established that full-disk encryption is utilized the dm-crypt kernel module in plain mode with AES-256-CBC-ESSIV:SHA256 cipher. The partition encryption key is stored in the device's internal memory. File-based encryption employs the eCryptFS kernel module. The file structure includes information about the original file size, format marker, flags, number of extents, their size, and the encryption key. Comparative analysis has shown that Adoptable Storage mode provides more comprehensive data protection through full-disk encryption, while Portable Storage mode with file-based encryption offers greater flexibility in use but may be less secure due to the possibility of analyzing the file system structure and file metadata. Research has revealed the implementation of encryption mechanisms depends on the device manufacturer and Android operating system version. The research findings have practical significance for understanding the level of data protection using different modes of removable storage operation in the Android system and are useful for both developers and information security specialists, as well as ordinary users.

DEVELOPMENT OF A SOUND-BASED MOBILE APPLICATION FOR ROAD ACCIDENT DETECTION USING MACHINE LEARNING AND SPECTROGRAM ANALYSIS

Sun, 30 Mar 2025 00:00:00 +0500

Road accidents continue to pose a serious threat to public safety, underscoring the need for innovative, automated emergency response systems. This study presents the development of a mobile application that detects road accidents by analyzing audio signals in real time and immediately sends SMS alerts with GPS coordinates to emergency services and user-specified contacts. The system comprises two parts: a user-facing Android application and a server-side component for data processing. To build and train the detection models, we leverage the MIVIA Road Audio Events dataset and applied preprocessing techniques including amplitude normalization, background noise filtering, and data augmentation. Feature extraction involved zero-crossing rate, spectral centroid, spectral flux, energy entropy, short-time Fourier transform (STFT), and Mel-frequency cepstral coefficients (MFCCs). Two classification approaches were investigated: traditional machine learning models (Support Vector Machine, Random Forest, Gradient Boosting) and a deep learning model based on convolutional neural networks (CNNs) using Mel spectrogram inputs. Experimental results demonstrate that the CNN model achieved the highest performance with 91.2% accuracy, 89.5% recall, and an F1-score of 90.3%, outperforming the best classical model (Random Forest), which achieved 85.1% accuracy. The system also reduced the average accident alert time from 5–7 minutes to 1–2 minutes, representing a 60–80% improvement in emergency response speed. These results confirm the system’s reliability and practical benefit, particularly in regions like Kazakhstan, where timely medical intervention is critical. Limitations include reliance on smartphone availability, internet access, and environmental sound conditions. Future work will explore real-world testing, integration of accelerometer and gyroscope data, and deployment of edge computing for faster on-device processing. Overall, the proposed solution is a cost-effective, scalable approach for improving road safety and saving lives through rapid, automated accident detection.