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All Journal International Journal of Electrical and Computer Engineering
Yerzhanova, Akbota
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Computer Science & IT Electrical & Electronics Engineering

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Human-machine interactions based on hand gesture recognition using deep learning methods Zholshiyeva, Lazzat; Manbetova, Zhanat; Kaibassova, Dinara; Kassymova, Akmaral; Tashenova, Zhuldyz; Baizhumanov, Saduakas; Yerzhanova, Akbota; Aikhynbay, Kulaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp741-748

Abstract

Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.
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.
Noisy image enhancements using deep learning techniques Daurenbekov, Kuanysh; Aitimova, Ulzada; Dauitbayeva, Aigul; Sankibayev, Arman; Tulegenova, Elmira; Yerzhan, Assel; Yerzhanova, Akbota; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp811-818

Abstract

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
Co-Authors Abdikerimova, Gulzira Abdykerimova, Lazzat Aikhynbay, Kulaisha Aitimova, Ulzada Baizhumanov, Saduakas Bazarova, Madina Bersugir, Mukhamedi Dauitbayeva, Aigul Daurenbekov, Kuanysh Kaibassova, Dinara Kaldarova, Mira Kassymova, Akmaral Konyrkhanova, Assem Manbetova, Zhanat Mukhamedrakhimova, Galiya Nurova, Gulsara Sankibayev, Arman Tashenova, Zhuldyz Tulegenova, Elmira Yerzhan, Assel Zholshiyeva, Lazzat