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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Kaliyeva, Samal
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Computer Science & IT Electrical & Electronics Engineering

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Fire detection using deep learning methods Bayegizova, Aigulim; Abdikerimova, Gulzira; Kaliyeva, Samal; Shaikhanova, Aigul; Shangytbayeva, Gulmira; Sugurova, Laura; Sugur, Zharkynay; Saimanova, Zagira
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.pp547-555

Abstract

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires.
Exponential smoothing-based forecasting of self-similar internet of things traffic Mukhamejanova, Almira; Chezhimbayeva, Katipa; Kaliyeva, Samal; Lechshinskaya, Eleonora; Tumanbayeva, Kumyssay; Garmashova, Yuliya; Abisheva, Tolganay; Zhumay, Inkar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11219

Abstract

The rapid growth of internet of things (IoT) devices generate highly variable and self-similar traffic patterns, creating challenges for maintaining quality of service (QoS) in modern telecommunication networks. Accurate short-term forecasting of such traffic is essential for efficient resource allocation, yet its fractal characteristics and long-range dependence complicate prediction. This study investigates the use of simple exponential smoothing for short-term forecasting of self-similar IoT traffic by evaluating three smoothing coefficients (a=0.1, 0.5, and 0.9). The Hurst exponent (H=0.5) confirms the presence of self-similarity in the observed traffic. Experimental results show that a=0.1 provides the highest prediction accuracy, achieving a mean absolute percentage error (MAPE) of 25.82% when forecasting traffic values within a 32-minute horizon. The method effectively captures underlying trends while reducing noise sensitivity. These findings demonstrate that exponential smoothing offers a lightweight, interpretable, and practical solution for real-time IoT traffic forecasting, supporting dynamic network load management under highly variable traffic conditions.
Co-Authors Abdikerimova, Gulzira Abisheva, Tolganay Bayegizova, Aigulim Chezhimbayeva, Katipa Garmashova, Yuliya Lechshinskaya, Eleonora Mukhamejanova, Almira Saimanova, Zagira Shaikhanova, Aigul Shangytbayeva, Gulmira Sugur, Zharkynay Sugurova, Laura Tumanbayeva, Kumyssay Zhumay, Inkar