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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Beketova, Gulzhanat
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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.
Adaptive mathematical modeling for predicting and analyzing malware Beketova, Gulzhanat; Manapova, Ainur
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1698-1707

Abstract

In this paper, we propose and investigate an improved mathematical model of malware propagation in network structures based on a modification of the well-known raw-immune-response susceptible-infected-recovered (SIR) model. For detailed numerical analysis, our study introduces the fourth-order Runge-Kutta method, which provides higher accuracy in determining fundamental parameters such as infection, recovery and immunity loss coefficients of network nodes. The obtained simulation results demonstrate that the peak of the epidemic occurs when 34.7% of all nodes are infected, with a peak after 32.5-time units. The main contribution of this work is the in-depth understanding and quantification of cyber threats, which emphasizes the importance of prompt response, regular system software updates, and continuous monitoring of network activity. This research makes a significant contribution to cybersecurity applications by providing quantitative tools and strategies to help strengthen network defenses against malicious attacks. The identified patterns and their numerical interpretation can be integrated into processes for optimizing measures to prevent the widespread spread of malware, thereby enhancing the overall security and stability of networked systems.
Co-Authors Azamatova, Zhanerke Belginova, Saule Kalpebayev, Azamat Koishiyeva, Dina Manapova, Ainur Sydybayeva, Madina Yeskendirova, Damelya