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• MakaleAçık Erişim2025/r/gumushane/14032
  Kurtun Dam oscillate characterization with landslide possible effect detection using InSAR observations

  Celik, Fatih; Sanli, Fusun Balik; Celik, Kemal; Celik, Ayse

  Observing and analyzing areas prone to landslides is crucial due to the extensive destructive capacity of landslides. To monitor landslide activities across diverse areas, remote sensing data and image processing methods are employed. This study proposes a method that integrates Multi-temporal Interferometric Synthetic Aperture Radar and advanced temporal tracking technologies to intensify surveillance of the Kurtun Dam and its valley. According to Sentinel-1 A data, the net displacement rates of the dam crest varied between − 12 and 2 mm/year (LOS). InSAR technology surveys the entire dam catchment area, detecting critical infrastructure such as slopes, spillways, and access roads. The crest of the Kurtun Dam experiences oscillatory deformation during filling activities, but the lateral slopes and downstream abutment remain stable. As reservoir water levels rise, the dam structure exhibits downstream deformations, whereas declining water level results in upstream displacements. Monitoring points located the opposite Ozkurtun settlement and within the reservoir area, both facing the dam, exhibited displacement rates of up to -50 mm monthly. In regions susceptible to landslides, the greatest annual displacement rates were observed to be -54 and − 127 mm/year. This study suggests that integrating 3D modeling with InSAR technology into dam monitoring systems can significantly enhance their effectiveness. At Kurtun Dam and other crucial locations within the catchment area, the integrated approach combined with topographic monitoring, ensures reliable landslide detection and analysis. By advancing monitoring technology for embankment dams, this strategy prioritizes safety and structural integrity assessments.

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• MakaleAçık Erişim2024/r/gumushane/11775
  Enhancing brain tumor classification through ensemble attention mechanism

  CELIK, Fatih; CELIK, Kemal; CELIK, Ayse

  Brain tumors pose a serious threat to public health, impacting thousands of individuals directly or indirectly worldwide. Timely and accurate detection of these tumors is crucial for effective treatment and enhancing the quality of patients’ lives. The widely used brain imaging technique is magnetic resonance imaging, the precise identification of brain tumors in MRI images is challenging due to the diverse anatomical structures. This paper introduces an innovative approach known as the ensemble attention mechanism to address this challenge. Initially, the approach uses two networks to extract intermediate- and final-level feature maps from MobileNetV3 and EfficientNetB7. This assists in gathering the relevant feature maps from the different models at different levels. Then, the technique incorporates a co-attention mechanism into the intermediate and final feature map levels on both networks and ensembles them. This directs attention to certain regions to extract global-level features at different levels. Ensemble of attentive feature maps enabling the precise detection of various feature patterns within brain tumor images at both model, local, and global levels. This leads to an improvement in the classification process. The proposed system was evaluated on the Figshare dataset and achieved an accuracy of 98.94%, and 98.48% for the BraTS 2019 dataset which is superior to other methods. Thus, it is robust and suitable for brain tumor detection in healthcare systems.

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