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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Azamatova, Zhanerke
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Development of an algorithm for integrated UAV groups using visible light communication technology Alibekkyzy, Karygash; Keribayeva, Talshyn; Koshekov, Kayrat; Baidildina, Aizhan; Bugubayeva, Alina; Azamatova, Zhanerke
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.pp41-52

Abstract

Our research group dedicated its idea in developing and analyzing an algorithm for transforming integrated unmanned aerial vehicle (UAV) groups (IUGs) using visible light communication (VLC) technology. This innovative approach is designed to enhance UAV network coordination, addressing the complex challenges of communication within these networks. The primary issue addressed is the pressing need for advanced communication mechanisms within UAV networks to ensure efficient. This is a robust data transfer and complex coordination between UAVs. The existing systems lack the required adaptability and efficiency, leading to operational inefficiencies and reduced effectiveness in UAV applications. The main results of the study are concluded in the design and implementation of the conversion algorithm. Which provides efficient and reliable data transmission and sophisticated coordination between UAVs. Through careful mathematical modeling of UAV group dynamics and extensive MATLAB simulations, the study demonstrates the algorithm's ability to effectively control UAV formations. This method gives adaptability to different operational requirements and supports collision-free maneuvers. The algorithm's innovative design and the comprehensive approach adopted in the study, including the use of VLC technology and the integration of advanced restructuring methods, enable the effective resolution of the identified communication challenges within UAV networks.
Cell nuclei image segmentation using U-Net and DeepLabV3+ with transfer learning and regularization Koishiyeva, Dina; Sydybayeva, Madina; Belginova, Saule; Yeskendirova, Damelya; Azamatova, Zhanerke; Kalpebayev, Azamat; Beketova, Gulzhanat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1986-2000

Abstract

Semantic nuclei segmentation is a challenging area of computer vision. Accurate nuclei segmentation can help medics in diagnosing many diseases. Automatic nuclei segmentation can help medics in diagnosing many diseases such as cancer by providing automatic tissue analysis. Deep learning algorithms allow automatic feature extraction from medical images, however, hematoxylin and eosin (H&E) stained images are challenging due to variability in staining and textures. Using pre-trained models in deep learning speeds up development and improves their performance. This paper compares Deeplabv3+ and U-Net deep learning methods with the pre-trained models ResNet-50 and EfficientNetB4 embedded in their architecture. In addition, different regularization and dropout parameters are applied to prevent overtraining. The experiment was conducted on the PanNuke dataset consisting of nearly 8,000 histological images and annotated nuclei. As a result, the ResNet50-based DeepLabV3+ model with L2 regularization of 0.02 and dropout of 0.7 showed efficiency with dice coefficient (DCS) of 0.8356, intersection over union (IOU) of 0.7280, and loss of 0.3212 on the test set.
Co-Authors Alibekkyzy, Karygash Baidildina, Aizhan Beketova, Gulzhanat Belginova, Saule Bugubayeva, Alina Kalpebayev, Azamat Keribayeva, Talshyn Koishiyeva, Dina Koshekov, Kayrat Sydybayeva, Madina Yeskendirova, Damelya