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- MakaleAçık Erişim2027Scopus SJRQ1A hierarchical metaheuristic framework for CPT-based soil liquefaction prediction with Kolmogorov–Arnold Network variants
Günen, Mehmet Akif; Öztürk, Kaşif Furkan
Soil liquefaction is a critical geotechnical hazard that threatens the safety of civil infrastructure under seismic loading, necessitating robust and interpretable prediction models. This study proposes a hierarchical framework for CPT-based soil liquefaction prediction using meta-heuristic-optimized Kolmogorov–Arnold Network (KAN) variants. The framework follows a two-stage optimization strategy. First, six state-of-the-art meta-heuristic algorithms are statistically compared to identify the most effective optimization method. Second, the selected algorithm is employed to optimize six KAN variants, including FastKAN, EfficientKAN, FourierKAN, ChebyKAN, JacobiRKAN, and PadéRKAN. Statistical comparisons based on the Wilcoxon signed-rank test demonstrate that the Colony-Based Search algorithm (CSA) achieves lower objective-function values than the competing meta-heuristics (p<0.01) and is therefore adopted as the core optimization strategy. Performance evaluation reveals that the CSA-optimized ChebyKAN model achieves the highest predictive accuracy, with an F1 Score of 0.8875 ± 0.011 and an AUC of 0.9115 ± 0.013. Pairwise McNemar tests detected no statistically significant difference in classification decisions for any evaluated model pair (p>0.01). SHapley Additive exPlanations analysis identified qc, qc1N, and amax as the variables with the largest model-specific attributions, whereas d, c, and σv received lower attributions. A cost-sensitive error analysis further showed that ChebyKAN produced the lowest false-negative rate and the lowest asymmetric error cost under 5:1 and 10:1 false-negative-to-false-positive cost scenarios, supporting its conservative behaviour for safety-critical liquefaction screening. The results establish the proposed CSA–KAN framework as a reliable, high-accuracy, and interpretable solution for CPT-based soil liquefaction assessment in geotechnical engineering practice.
- MakaleAçık Erişim2025Scopus SJRQ1Advanced brain tumor analysis: a novel strategy for segmentation and classification using modern computational methods
Reis, Hatice Catal; Turk, Veysel
Despite their low incidence, brain tumors are one of the most invasive cancer types, constituting a significant burden of death and disease in all age groups. Early and accurate diagnosis of brain tumors plays a vital role in reducing mortality rates. The heterogeneous nature of brain tumors and the diversity of tumor lesions may make it difficult for radiologists to make the right decision in the manual diagnosis process. This study proposes the use of machine learning methods for the classification of brain tumors (pituitary, meningioma, and glioma) and the use of metaheuristic algorithms graph theory, and random walker algorithms in the segmentation of brain tumors. The classification performed with the proposed method obtained an overall accuracy rate of 98.33%. In addition, the classification accuracy of 99.50%, 99.50%, 98.67%, and 99.00% was achieved for no tumor, pituitary, meningioma, and glioma, respectively. Experiments in the segmentation process show that metaheuristic algorithms and max-flow graph cut approach produce successful results.
AtıfScopus6OpenAlex5
İncele - MakaleAçık Erişim2024Scopus SJRQ1Fusion of transformer attention and CNN features for skin cancer detection
Reis, Hatice Catal; Turk, Veysel
Melanoma is a skin cancer that spreads quickly and has serious risks. Early diagnosis is essential, but since the symptoms of skin lesions in the early stages are vague and similar, they can be difficult for specialists to detect. Therefore, machine learning-based alternative diagnostic methods can be developed in addition to existing ones. This study proposes a new deep learning model, a modified lightweight vision transformer (ViT) architecture, and a hybrid framework developed with an integrated deep learning model and an Ensemble Learning (EL) model for the early-stage diagnosis of skin lesions. The proposed deep learning model was developed based on convolution layers and transformers. The model is called multi-head attention block depthwise separable convolution network (MABSCNET). The proposed hybrid framework was developed by combining modern deep learning and EL models pre-trained with the ImageNet dataset along with the MABSCNET model. In the experimental process, the effectiveness of the proposed methods was evaluated on the ISIC 2020 dataset. Additionally, additional experiments were conducted on ISIC 2018 and a Kaggle dataset to analyze the proposed hybrid framework's classification performance. Image enhancement techniques were used in the datasets. In the ISIC 2020 dataset, the MABSCNET model reached 78.63 % accuracy, the ViT model obtained 76.50 %, and the hybrid framework reached 92.74 % accuracy. Moreover, the proposed hybrid framework achieved 100 % on the ISIC 2018 dataset and 94.24 % on the Kaggle dataset.
AtıfScopus62WoS37OpenAlex51
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