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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Tynykulova, Assemgul
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Computer Science & IT Electrical & Electronics Engineering

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Integrating numerical methods and machine learning to optimize agricultural land use Tynykulova, Assemgul; Mukhanova, Ayagoz; Mukhomedyarova, Ainagul; Alimova, Zhanar; Tasbolatuly, Nurbolat; Smailova, Ulmeken; Kaldarova, Mira; Tynykulov, Marat
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5420-5429

Abstract

In the current context, optimizing the utilization of agricultural land resources is increasingly vital for production intensification. This study presents a methodological approach employing numerical methods and machine learning algorithms to analyze and forecast land use optimality. The objective is to develop effective models and tools facilitating rational and sustainable agricultural land resource management, ultimately enhancing productivity and economic efficiency. The research employs data dimensionality reduction techniques such as principal component analysis and factor analysis (FA) to extract key factors from multidimensional land data. The simplex method is utilized to optimize resource allocation among crops while considering constraints. Machine learning algorithms including extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) are employed to predict optimal land use and yield with high accuracy and efficiency. Analysis reveals significant differences in model performance, with LightGBM achieving the highest accuracy of 99.98%, followed by XGBoost at 95.99%, and SVM at 43.65%. These findings underscore the importance of selecting appropriate algorithms for agronomic data tasks. The study's outcomes offer valuable insights for formulating agricultural practice recommendations and land management strategies, integrable into decision support systems for the agricultural sector, thereby enhancing productivity and production efficiency.
Development of a decision-making module in the field of real estate rental using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Ignatovich, Artyom; Bayegizova, Aigulim; Tanirbergenov, Adilbek; Tynykulova, Assemgul; Bapiyev, Ideyat; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5430-5442

Abstract

The research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype is an innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.
Assessing risk factors for heart disease using machine learning methods Maxutova, Natalya; Tussupov, Jamalbek; Kedelbayeva, Kamilya; Tynykulova, Assemgul; Balabayeva, Zulfiya; Yersultanova, Zauresh; Khamitova, Zhainagul; Zhunussova, Kamila
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6734-6742

Abstract

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.
Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods Kassymova, Akmaral; Aibatullin, Tolegen; Yelezhanova, Shynar; Konyrkhanova, Assem; Mukhanbetkaliyeva, Ainur; Tynykulova, Assemgul; Makhazhanova, Ulzhan; Azieva, Gulmira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10458

Abstract

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
Generating data for predicting court decisions in Kazakhstan using machine learning Ignatovich, Artyom; Yessengeldina, Anar; Baidullayeva, Gulzhakhan; Ussipbekova, Dinara; Jakhanova, Baktykul; Saduakassova, Gulmira; Serimbetov, Bulat; Tynykulova, Assemgul
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10490

Abstract

This study presents the development of a synthetic dataset and machine learning models for predicting court decisions in Kazakhstan. The dataset contains 100,000 cases generated from the Code of the Republic of Kazakhstan, covering both administrative and criminal offenses. Each record includes attributes such as the age of the accused, offense type and severity, and mitigating or aggravating factors. Regression models were applied to estimate offense severity, level of guilt, and likelihood of penalties, while classification models predicted the offense category, relevant law articles, and sentencing type. Predictions addressed both general outcomes—classifying cases as criminal or administrative—and specific judicial decisions, including fines, imprisonment terms, and other penalties. Classification models achieved 92% accuracy in determining offense category and sentencing type, and regression models reached a root mean squared error (RMSE) of 0.12 for offense severity. Using synthetic data preserves confidentiality while enabling pattern discovery for decision support. The results demonstrate the potential of artificial intelligence (AI) to improve sentencing prediction, prioritize case processing, and enhance transparency in Kazakhstan’s judicial system. Beyond transparency in decision support, the proposed approach also shows potential in crime prevention, workload optimization, and fostering digital transformation within judicial operations.
Co-Authors Aibatullin, Tolegen Alimova, Zhanar Azieva, Gulmira Baidullayeva, Gulzhakhan Baitemirov, Madiyar Balabayeva, Zulfiya Bapiyev, Ideyat Bayegizova, Aigulim Ignatovich, Artyom Jakhanova, Baktykul Kaldarova, Mira Kassymova, Akmaral Kedelbayeva, Kamilya Khamitova, Zhainagul Konyrkhanova, Assem Makhazhanova, Ulzhan Maxutova, Natalya Mukhamedrakhimova, Galiya Mukhanbetkaliyeva, Ainur Mukhanova, Ayagoz Mukhomedyarova, Ainagul Saduakassova, Gulmira Serimbetov, Bulat Smailova, Ulmeken Tanirbergenov, Adilbek Tasbolatuly, Nurbolat Tussupov, Jamalbek Tynykulov, Marat Ussipbekova, Dinara Yelezhanova, Shynar Yersultanova, Zauresh Yessengeldina, Anar Zhunussova, Kamila