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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Kaldarova, Mira
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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.
Predicting and detecting fires on multispectral images using machine learning methods Aitimov, Murat; Kaldarova, Mira; Kassymova, Akmaral; Makulov, Kaiyrbek; Muratkhan, Raikhan; Nurakynov, Serik; Sydyk, Nurmakhambet; Bapiyev, Ideyat
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1842-1850

Abstract

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.
Image noise reduction by deep learning methods Uzakkyzy, Nurgul; Ismailova, Aisulu; Ayazbaev, Talgatbek; Beldeubayeva, Zhanar; Kodanova, Shynar; Utenova, Balbupe; Satybaldiyeva, Aizhan; Kaldarova, Mira
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6855-6861

Abstract

Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.
Analysis of the emotional coloring of text using machine and deep learning methods Abdykerimova, Lazzat; Abdikerimova, Gulzira; Konyrkhanova, Assem; Nurova, Gulsara; Bazarova, Madina; Bersugir, Mukhamedi; Kaldarova, Mira; Yerzhanova, Akbota
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.pp3055-3063

Abstract

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.
Modelling a neural network for analysing the results of segmentation of satellite images Kaldarova, Mira; Akanova, Akerke; Naizagarayeva, Akgul; Kazanbayeva, Albina; Ospanova, Nazira
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp614-621

Abstract

The study's relevance lies in addressing inaccuracies within satellite image segmentation, necessitating the development and implementation of neural network models for automated segmentation. The purpose of study is to develop a model of a neural network for training with data obtained from the segmentation of satellite images. The basis of the methodological approach in study is a combination of methods of system analysis of neural networks, which have had a substantial impact on the development of the computer vision industry, with an empirical study of the general principles of neural network modelling for the training on satellite images segmentation. In this study, the results were obtained, indicating that there is a fundamental possibility of developing and practical implementation of a neural network model to determine the quality of the obtained segmentation of images of agricultural fields. Satellite images of agricultural fields of the Republic of Kazakhstan are obtained, and segmentation of field images is performed using the developed neural network model for learning segmentation results. The practical importance of the results obtained in study lies in the possibility of their use in the development of functional models of neural networks for training the results of the segmentation of satellite images.
Co-Authors Abdikerimova, Gulzira Abdykerimova, Lazzat Aitimov, Murat Akanova, Akerke Alimova, Zhanar Ayazbaev, Talgatbek Bapiyev, Ideyat Bazarova, Madina Beldeubayeva, Zhanar Bersugir, Mukhamedi Ismailova, Aisulu Kassymova, Akmaral Kazanbayeva, Albina Kodanova, Shynar Konyrkhanova, Assem Makulov, Kaiyrbek Mukhanova, Ayagoz Mukhomedyarova, Ainagul Muratkhan, Raikhan Naizagarayeva, Akgul Nurakynov, Serik Nurova, Gulsara Ospanova, Nazira Satybaldiyeva, Aizhan Smailova, Ulmeken Sydyk, Nurmakhambet Tasbolatuly, Nurbolat Tynykulov, Marat Tynykulova, Assemgul Utenova, Balbupe Uzakkyzy, Nurgul Yerzhanova, Akbota