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All Journal International Journal of Electrical and Computer Engineering Journal of Applied Data Sciences
Tasbolatuly, Nurbolat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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.
Approach to automating the construction and completion of ontologies in a scientific subject field Sadirmekova, Zhanna; Murzakhmetov, Aslanbek; Abduvalova, Ainur; Altynbekova, Zhanar; Makhatova, Valentina; Akhmetzhanova, Shynar; Tasbolatuly, Nurbolat; Serikbayeva, Sandugash
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3064-3072

Abstract

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.
Computer Simulation of Control of High-Order Nonlinear Systems using Feedback Bakhadirova, Gulnaz; Tasbolatuly, Nurbolat; Tanirbergenova, Alua; Dautova, Aigul; Akanova, Akerke; Ulikhina, Yuliya
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.275

Abstract

The relevance of the topic stated in this research is the need to develop and implement methods of computer modelling of these control systems. The purpose of this research is the search for opportunities for controlling nonlinear systems and the creation of a computer model for controlling nonlinear systems. The basis of the methodological approach in this research work is a combination of methods of theoretical and applied research of general principles of construction of computer models of control of high order nonlinear systems by means of feedback. In the course of the research work, the results were obtained, indicating the effectiveness of the development of an algorithm for finding the control of tracking a given reference signal of nonlinear systems and nonlinear systems with time delay. An algorithm has been developed to find a control that can effectively track the output signal of a nonlinear system behind a given reference signal. In addition, a scientific analysis of the tracking and stabilization errors of nonlinear systems and time-delayed nonlinear systems has been carried out depending on the control parameters, and graphical representations of a computer model of numerical experiments performed according to the control algorithms have been presented. It is established that the output control problem for a nonlinear system is to obtain a feedback control that forces the controlled output signal of the nonlinear system to asymptotically track the reference signal. The practical significance of the obtained results lies in the possibility of their use in the creation of computer models of process control with feedback.
Modelling and controlling outputs of nonlinear systems using feedback Bahadirova, Gulnaz; Tasbolatuly, Nurbolat; Akanova, Akerke; Muratova, Gulzhan; Sadykova, Anar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp540-549

Abstract

This study aimed to analyze methods for modeling and controlling the output of nonlinear systems using feedback, analytical methods, mathematical modeling, and differential equation theory. Key findings include the mathematical characterization of equations and the analysis of system stability and asymptotic behavior. The study explored various methods for addressing problems in nonlinear systems, emphasizing the importance of identifying effective solutions. The research highlights the significance of developing effective approaches to solving complex problems involving nonlinear systems. Feedback is essential for controlling and correcting dynamic processes in systems with nonlinearities. The study’s key finding is the mathematical characterization of equations describing nonlinear systems, providing insight into system structure and behavior under different parameters. Analyzing stability and asymptotic behavior allows for assessing system reliability and predicting long-term stability. This study contributes to the scientific understanding and development of methods for modeling and controlling nonlinear systems using feedback.
Markov processes in Bayesian network computation Shayakhmetova, Assem; Tasbolatuly, Nurbolat; Akhmetova, Ardak; Abdildayeva, Assel; Shurenov, Marat; Sultangaziyeva, Anar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2181-2191

Abstract

The article examines the influence of Markov processes on computations in Bayesian networks (BN), an important area of research within probabilistic graphical models. The concept of Bayesian Markov networks (BMN) is introduced, an extension of traditional Bayesian networks with the addition of a Markov constraint, according to which the probability in a node can only depend on the state of neighboring nodes. This constraint makes the model more realistic for many practical tasks, as most graphical models that reflect real-world processes possess the Markov property. The article also discusses that Bayesian networks, in the absence of evidence, actually exhibit the Markov property. However, when evidence (additional information) is introduced into the model, challenges arise that require more complex computational methods. In response, the article proposes algorithms adapted for working with Bayesian Markov networks in the presence of evidence. These algorithms are aimed at optimizing computations and reducing computational complexity. Additionally, a comparative analysis of calculations in Bayesian networks without Markov constraints and with them is conducted, highlighting the advantages and disadvantages of each approach. Special attention is paid to the practical applications of the proposed methods and their effectiveness in various scenarios.
Co-Authors Abdildayeva, Assel Abduvalova, Ainur Akanova, Akerke Akhmetova, Ardak Akhmetzhanova, Shynar Alimova, Zhanar Altynbekova, Zhanar Bahadirova, Gulnaz Bakhadirova, Gulnaz Dautova, Aigul Kaldarova, Mira Makhatova, Valentina Mukhanova, Ayagoz Mukhomedyarova, Ainagul Muratova, Gulzhan Murzakhmetov, Aslanbek Sadirmekova, Zhanna Sadykova, Anar Serikbayeva, Sandugash Shayakhmetova, Assem Shurenov, Marat Smailova, Ulmeken Sultangaziyeva, Anar Tanirbergenova, Alua Tynykulov, Marat Tynykulova, Assemgul Ulikhina, Yuliya