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Tussupov, Jamalbek
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Computer Science & IT Electrical & Electronics Engineering

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Effective detection of breast pathology using machine learning methods Orazayeva, Ainur; Tussupov, Jamalbek; Shangytbayeva, Gulmira; Galymova, Assem; Zhunissova, Ulzhalgas; Tergeussizova, Aliya; Tleubayeva, Arailym; Kenzhebayeva, Zhanat
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.pp5593-5600

Abstract

This work is devoted to the research and development of methods for effectively identifying breast pathologies using modern machine learning technologies, such as you only look once (YOLOv8) and faster region-based convolutional neural network (R-CNN). The paper presents an analysis of existing approaches to the diagnosis of breast diseases and an assessment of their effectiveness. YOLOv8 and Faster R-CNN architectures are then applied to create pathology detection models in mammography images. The work analyzed and classified identified breast pathologies at six levels, taking into account different degrees of severity and characteristics of the diseases. This approach allows for more accurate determination of disease progression and provides additional data for more individualized treatment planning. Classification results at various levels can improve the quality of medical decisions and provide more accurate information to doctors, which in turn improves the overall efficiency of diagnosis and treatment of breast diseases. Experimental results demonstrate high accuracy and speed of image processing, providing fast and reliable detection of potential breast pathologies. The data obtained confirm the effectiveness of the use of machine learning algorithms in the field of medical diagnostics, providing prospects for the further development of automated systems for detecting breast diseases in order to improve early diagnosis and treatment efficiency.
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.
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.
Co-Authors Akhmetzhanova, Shynar Altynbekova, Zhanar Balabayeva, Zulfiya Borankulova, Gauhar Galymova, Assem Kedelbayeva, Kamilya Kenzhebayeva, Zhanat Khamitova, Zhainagul Maxutova, Natalya Murzakhmetov, Aslanbek Orazayeva, Ainur Sadirmekova, Zhanna Shangytbayeva, Gulmira Tergeussizova, Aliya Tleubayeva, Arailym Tulenbayev, Murat Tungatarova, Aigul Tynykulova, Assemgul Yersultanova, Zauresh Zhidekulova, Gulkiz Zhunissova, Ulzhalgas Zhunussova, Kamila