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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Shekerbek, Ainur
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Computer Science & IT Electrical & Electronics Engineering

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Analysis of research on the implementation of Blockchain technologies in regional electoral processes Ainur, Jumagaliyeva; Elmira, Abdykerimova; Asset, Turkmenbayev; Gulzhan, Muratova; Amangul, Talgat; Shekerbek, Ainur
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.pp2854-2867

Abstract

Implementation of Blockchain technologies in online voting system is becoming increasingly popular in modern society and has significantly efficiency in governance. This article explores how Blockchain technologies can boost government operations, making them more transparent and effective. It focuses on an in-depth analysis of current research and methods on Blockchain-based electronic voting systems. The aim of this study is investigated and analysis the potential contributions of Blockchain technology to e-voting by drawing insights from global best practices. According to literature review and case studies of Blockchain implementation in government are conducted to identify existing systems and methods of e-voting, identifying their strengths and weaknesses by analyzing European countries and preparing the ground for future alternatives. Additionally, it examined the role of public education in fostering trust and understanding of Blockchain technology and analyzed the legislative landscape in neighboring jurisdictions to solidify Blockchain’s role in decision-making processes. The results of the study provide a comprehensive perspective, and the findings emphasize the relevance of the study, its contribution to understanding the problems and prospects of introducing Blockchain into electoral processes at the regional level.
Detection of lung pathology using the fractal method Abdikerimova, Gulzira; Shekerbek, Ainur; Tulenbayev, Murat; Sultanova, Bakhyt; Beglerova, Svetlana; Dzhaulybaeva, Elvira; Zhumakanova, Kamshat; Rysbekkyzy, Bakhytgul
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.pp6778-6786

Abstract

Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. 
Classification of pathologies on digital chest radiographs using machine learning methods Aitimov, Murat; Shekerbek, Ainur; Pestunov, Igor; Bakanov, Galitdin; Ostayeva, Aiymkhan; Ziyatbekova, Gulzat; Mediyeva, Saule; Omarova, Gulmira
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.pp1899-1905

Abstract

This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using pre-processed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Assessing external factors of the agro-industrial complex efficiency based on data Mauina, Gulalem; Aitimova, Ulzada; Kadyrova, Ainagul; Adikanova, Saltanat; Syzdykpayeva, Aigul; Seitakhmetova, Zhanat; Alimagambetova, Ainagul; Shekerbek, Ainur
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10459

Abstract

Modern agriculture faces the challenge of increasing production efficiency in the context of limited resources and variable climatic conditions. This article presents an approach to assessing the impact of various factors on agro-industrial indicators using machine learning methods. The primary focus is on the development and application of a hybrid analysis that includes techniques such as gradient boosting (GB), mutual information (MI), and recursive feature elimination (RFE). The study was conducted using data from agro-industrial enterprises in the North Kazakhstan region for the period 2020–2022, encompassing production, climatic, and economic indicators. It was found that crop area, average crop weight, and precipitation are the most significant factors, accounting for up to 93% of the correlation with yield increase. The use of the proposed methods made it possible to reduce forecast uncertainty by 28% and increase the accuracy of key indicator predictions by 15–20%. The results of the analysis, visualized as correlation matrices and feature significance maps, confirm the possibility of applying the proposed approach to optimize the management of agro-industrial production. The application of the developed methodology contributes to the development of strategies aimed at the sustainable development of the agro-industrial complex.
Co-Authors Abdikerimova, Gulzira Adikanova, Saltanat Ainur, Jumagaliyeva Aitimov, Murat Aitimova, Ulzada Alimagambetova, Ainagul Amangul, Talgat Asset, Turkmenbayev Bakanov, Galitdin Beglerova, Svetlana Dzhaulybaeva, Elvira Elmira, Abdykerimova Gulzhan, Muratova Kadyrova, Ainagul Mauina, Gulalem Mediyeva, Saule Omarova, Gulmira Ostayeva, Aiymkhan Pestunov, Igor Rysbekkyzy, Bakhytgul Seitakhmetova, Zhanat Sultanova, Bakhyt Syzdykpayeva, Aigul Tulenbayev, Murat Zhumakanova, Kamshat Ziyatbekova, Gulzat