Yazar "Kinaci, Ahmet Cumhur" seçeneğine göre listele

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    Cost Reduction in Thyroid Diagnosis: A Hybrid Model with SOM and C4.5 Decision Trees
    (Springer International Publishing Ag, 2015) Kinaci, Ahmet Cumhur; Yucebas, Sait Can
    The main objective of this paper is to introduce a hybrid model of Self Organizing Maps (SOM) and C4.5, to reduce the costs while maintaining an acceptable diagnostic performance. In this hybrid model, SOM is used first to form clusters and then C4.5 trees specific to each cluster is constructed. The proposed hybrid model is tested on multiclass Thyroid Data and compared to standalone C4.5 tree. Costs were reduced by 22%-27% and performance results vary between 88% and 97% in terms of accuracy and 90%-97% in terms of sensitivity. Cost and performance differences between the hybrid model and standalone C4.5 found to be statistically significant according to Wilcoxon signed-rank test.
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    Öğe
    Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+
    (Mdpi, 2022) Akcay, Ozgun; Kinaci, Ahmet Cumhur; Avsar, Emin Ozgur; Aydar, Umut
    In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.