Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Akhmetzhanova, Shynar
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Ontology engineering of automatic text processing methods Sadirmekova, Zhanna; Tussupov, Jamalbek; Murzakhmetov, Aslanbek; Zhidekulova, Gulkiz; Tungatarova, Aigul; Tulenbayev, Murat; Akhmetzhanova, Shynar; Altynbekova, Zhanar; Borankulova, Gauhar
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.pp6620-6628

Abstract

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
Few-shot brain tumor classification: meta- vs metric-learning comparison Akhmetzhanova, Shynar; Serek, Azamat; Kashayev, Ruslan; Kozhamuratova, Aizhan
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.v14i5.10706

Abstract

Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.
Co-Authors Abduvalova, Ainur Altynbekova, Zhanar Borankulova, Gauhar Kashayev, Ruslan Kozhamuratova, Aizhan Makhatova, Valentina Murzakhmetov, Aslanbek Sadirmekova, Zhanna Serek, Azamat Serikbayeva, Sandugash Tasbolatuly, Nurbolat Tulenbayev, Murat Tungatarova, Aigul Tussupov, Jamalbek Zhidekulova, Gulkiz