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All Journal International Journal of Electrical and Computer Engineering
Mutovina, Natalya
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Computer Science & IT Electrical & Electronics Engineering

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Detection of heart pathology using deep learning methods Naizagarayeva, Akgul; Abdikerimova, Gulzira; Shaikhanova, Aigul; Glazyrina, Natalya; Bekmagambetova, Gulmira; Mutovina, Natalya; Yerzhan, Assel; Tanirbergenov, Adilbek
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.pp6673-6680

Abstract

In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators, 13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database.
Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies Mutovina, Natalya; Nurtay, Margulan; Kalinin, Alexey; Tomilov, Aleksandr; Tomilova, Nadezhda
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3291-3308

Abstract

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.
Co-Authors Abdikerimova, Gulzira Bekmagambetova, Gulmira Glazyrina, Natalya Kalinin, Alexey Naizagarayeva, Akgul Nurtay, Margulan Shaikhanova, Aigul Tanirbergenov, Adilbek Tomilov, Aleksandr Tomilova, Nadezhda Yerzhan, Assel