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p-Index From 2020 - 2025
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All Journal International Journal of Electrical and Computer Engineering Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Zholshiyeva, Lazzat
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical 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.
Towards robust security in WSN: a comprehensive analytical review and future research directions Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Mardenov, Yerik; Adamova, Aigul; Karabayev, Nurdaulet; Abdildayeva, Assel; Baumuratova, Dilaram
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp318-337

Abstract

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).
Comprehensive Study on Detecting Multi-Class Classification of IoT Attack Using Machine Learning Methods Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Adamova, Aigul; Karabayev, Nurdaulet; Mardenov, Erik
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22819

Abstract

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.
Design of QazSL Sign Language Recognition System for Physically Impaired Individuals Zholshiyeva, Lazzat; Zhukabayeva, Tamara; Baumuratova, Dilaram; Serek, Azamat
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23879

Abstract

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing both static and dynamic gestures remains challenging due to variations in gesture speed and length, which affect the effectiveness of the models. We introduce a hybrid approach that merges machine learning and deep learning methods for sign language recognition. We provide new model for the recognition of Kazakh Sign Language (QazSL), employing five algorithms: Support Vector Machine (SVM), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) with VGG19, ResNet-50, and YOLOv5. The models were trained on a QazSL dataset of more than 4,400 photos. Among the assessed models, the GRU attained the highest accuracy of 100%, followed closely by SVM and YOLOv5 at 99.98%, VGG19 at 98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at 78.61%. These findings illustrate the comparative efficacy of each method in real-time gesture recognition. The results yield significant insights for enhancing sign language recognition systems, presenting possible advancements in accessibility and communication for those with hearing impairments.
Tackling the anomaly detection challenge in large-scale wireless sensor networks Zhukabayeva, Tamara; Adamova, Aigul; Zholshiyeva, Lazzat; Mardenov, Yerik; Karabayev, Nurdaulet; Baumuratova, Dilaram
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2479-2490

Abstract

One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Co-Authors Abdildayeva, Assel Adamova, Aigul Aikhynbay, Kulaisha Baizhumanov, Saduakas Baumuratova, Dilaram Kaibassova, Dinara Karabayev, Nurdaulet Kassymova, Akmaral Manbetova, Zhanat Mardenov, Erik Mardenov, Yerik Serek, Azamat Tashenova, Zhuldyz Ven-Tsen, Khu Yerzhanova, Akbota Zhukabayeva, Tamara