Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Celik, Kemal(2)
- CELIK, Ayse(1)
- CELIK, Fatih(1)
- CELIK, Kemal(1)
- Celik, Fatih(1)
- Nihal Özkan(1)
Yayınlar
- MakaleYalnız künye2026A Dense and Sparse Attention in Vision Transformer for Remote Sensing Image Classification
Celik, Kemal
Satellite imagery plays a crucial role in various applications, such as environmental monitoring, urban planning, and disaster response. Previously, Convolutional neural networks have played a crucial role in improving remote sensing scene classification by effectively capturing spatial features. However, they often struggle with long-range dependencies and complex spectral patterns in satellite images. Recently, transformer-based architectures, initially designed for natural language processing, have been adopted in computer vision to leverage self-attention mechanisms for capturing long-range dependencies. To effectively extract the long-range dependencies and complex patterns, this paper proposes a novel method combining dense and sparse attention mechanisms to enhance remote sensing scene classification in different scales. The dense attention mechanisms capture comprehensive connections within satellite images, followed by sparse attention mechanisms applied at both tile and patch scale levels. This multi-scale attention strategy allows the model to efficiently focus on important spatial and spectral features while managing computational complexity. Extensive experimental evaluations on three benchmark datasets, such as Merced, AID, and Optimal-31, demonstrate significant improvements in classification accuracy, achieving 98.9, 97.5, and 97.8, respectively. Experimental results show that the proposed framework outperforms CNN and Transformer baselines, enhancing the accuracy and robustness of remote sensing scene classification for geospatial analysis.
- MakaleYalnız künye2026Multi level throttled attention for accurate and efficient classification of retinal diseases in OCT images
Celik, Kemal; Celik, Fatih
The classification of retinal diseases using Optical Coherence Tomography images has gained significant attention in recent years. However, due to the heterogeneity of retinal disorders, achieving high classification accuracy remains challenging. While deep learning has improved the identification of various ophthalmic conditions, accurate and early diagnosis is still critical for effective clinical decision-making. Additionally, reducing computational complexity is essential for building scalable diagnostic systems. To address these challenges, we propose a novel Multi-level Throttled Attention (MLTA) model that first applies attention to extract important features, followed by throttling to filter out redundant or less useful features. This process is applied at multiple stages, operating on both Convolutional Neural Network layers and Transformer architectures, and then fuses them finally. This approach optimizes feature selection by preserving the most informative retinal features while discarding redundant ones from multi-level feature maps. As a result, the model enhances classification performance while reducing computational overhead. We evaluate our proposed model on two benchmark OCT datasets: OCT2017 and OCTID, achieving accuracy scores of 99.59 ± 0.04% and 99.71 ± 0.03%, respectively. Our method outperforms existing CNN-based and Vision Transformer-based models, demonstrating superior accuracy and efficiency through this hierarchical attention.
- MakaleAçık Erişim2024Enhancing 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.
- Yüksek Lisans TeziAçık Erişim2020Televizyon reklamlarında kullanılan korku çekiciliğine yönelik dikkatin nörobilimsel yöntemler ile ölçümlenmesi
Nihal Özkan
Reklam pahalı bir iletişim çalışmasıdır ve büyük bütçeler gerektirmektedir. Özellikle de televizyon reklamları prodüksiyonun profesyonelleşmesinden dolayı pahalı olmaktadır. Ayrıca medya satın alma maliyetleri de devamlı artmaktadır. Bugüne kadar reklamlara karşı olan tutum, davranış ve düşünceler geleneksel yöntemlerle test edilip öğrenilmeye çalışılmıştır. Ama artık reklamcılar için bu tek başına yeterli gelmemektedir. Çünkü tüketiciler, gerçek düşüncelerini söylemeyebilmektedirler. Bu sebeplerden dolayı reklamcılar, tüketicilerin gerçek düşüncelerini öğrenebilmek için nörobilime odaklanmaya başlamışlardır. Çünkü nörobilim kullandığı araçlar vasıtasıyla tüketicilerin gerçek düşüncesine yönelik daha yakın bilgiler öğrenebilme imkanını vermektedir. Dolayısıyla da korku çekicilikli televizyon reklamlarına yönelik dikkatin ve hislerin incelenmesinde ve ölçümlenmesinde nörobilimsel yöntemin kullanıldığı bir araştırmaya rastlanılmamaktadır. Bu çalışmada korku içerikli televizyon reklamlarına karşı olan tutum ve dikkati ile korku içermeyen sadece ürün ile ilgili bilgilendirici reklamlara karşı olan tutumu ve dikkati nörobilimsel yöntemlerle inceleyebilmek, ölçmek hedeflenmiştir. Bu doğrultuda araştırmada tasarlanan deneyde katılımcılara belirlenen korku reklam filmleri ve korkutucu olmayan reklam filmleri izletilirken eş zamanlı olarak göz izleme ve deri iletkenliği yöntemleri kullanılmıştır. Ayrıca göz izleme ve GSR yöntemiyle ölçülen kadın ve erkek katılımcıların dikkat seviyeleri arasında istatistiksel olarak anlamsal bir farklılık olduğu da gözlenmiştir.