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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Konyrkhanova, Assem
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Computer Science & IT Electrical & Electronics Engineering

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Forecasting stock market prices using deep learning methods Ismailova, Aisulu; Beldeubayeva, Zhanar; Kadirkulov, Kuanysh; Doumcharieva, Zhanagul; Konyrkhanova, Assem; Ussipbekova, Dinara; Aripbayeva, Ainura; Yesmukhanova, Dariga
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.pp5601-5611

Abstract

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.
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.
Application of deep learning methods for automated analysis of retinal structures in ophthalmology Kassymova, Akmaral; Konyrkhanova, Assem; Issembayeva, Aida; Saimanova, Zagira; Saltayev, Alisher; Ongarbayeva, Maral; Issakova, Gulnur
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.pp1987-1995

Abstract

This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods Kassymova, Akmaral; Aibatullin, Tolegen; Yelezhanova, Shynar; Konyrkhanova, Assem; Mukhanbetkaliyeva, Ainur; Tynykulova, Assemgul; Makhazhanova, Ulzhan; Azieva, Gulmira
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10458

Abstract

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning
Co-Authors Abdikerimova, Gulzira Abdykerimova, Lazzat Aibatullin, Tolegen Aripbayeva, Ainura Azieva, Gulmira Bazarova, Madina Beldeubayeva, Zhanar Bersugir, Mukhamedi Doumcharieva, Zhanagul Ismailova, Aisulu Issakova, Gulnur Issembayeva, Aida Kadirkulov, Kuanysh Kaldarova, Mira Kassymova, Akmaral Makhazhanova, Ulzhan Mukhanbetkaliyeva, Ainur Nurova, Gulsara Ongarbayeva, Maral Saimanova, Zagira Saltayev, Alisher Tynykulova, Assemgul Ussipbekova, Dinara Yelezhanova, Shynar Yerzhanova, Akbota Yesmukhanova, Dariga