Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Making Hierarchically Aware Decisions on Short Findings for Automatic Summarisation(Elsevier, 2025) Inan, EmrahAn impression in a typical radiology report emphasises critical information by providing a conclusion and reasoning based on the findings. However, the findings and impression sections of these reports generally contain brief texts, as they highlight crucial observations derived from the clinical radiograph. In this scenario, abstractive summarisation models often experience a degradation in performance when generating short impressions. To address this challenge in the summarisation task, our work proposes a method that combines well-known fine-tuned text classification and abstractive summarisation language models. Since fine-tuning a language model requires an extensive, well-defined training dataset and is a time-consuming task dependent on high GPU resources, we employ prompt engineering, which uses prompt templates to programme language models and improve their performance. Our method first predicts whether the given findings text is normal or abnormal by leveraging a fine-tuned language model. Then, we apply a radiology-specific BART model to generate the summary for abnormal findings. In the zero-shot setting, our method achieves remarkable results compared to existing approaches on a real-world dataset. In particular, our method achieves scores of 37.43 for ROUGE-1, 21.72 for ROUGE-2, and 35.52 for ROUGE-L.Article Digital Transformation in Leather Color Fastness Evaluation: Computer-Assisted Grey Scale Analysis(Korean Fiber Soc, 2025) Efendioglu, Nilay Ork; Mutlu, Mehmet Mete; Inan, Emrah; Ozgunay, HasanLeather is a critical material in the fashion industry, where it is required to meet specific customer demands for color, specifications, and performance, especially regarding color fastness. Traditional methods for assessing color fastness rely on subjective evaluations conducted by professional experts using grey scale standards. However, human evaluation can be inconsistent due to various factors, such as lighting conditions and individual perception. In this study, leather samples were first subjected to expert evaluations and scored using the grey scale system. These evaluations were then compared with color measurement data obtained through a spectrophotometer, which was processed using custom-designed software (written in the Python programming language). This software provided precise grey scale values based on the color measurements, enabling accurate digital assessments. The results of the comparative analysis showed that the computer-assisted grey scale assessment could be completed in a significantly shorter time frame with a minimal margin of error, offering a more reliable and efficient alternative to traditional evaluation methods. This approach not only enhances the accuracy of color assessments but also streamlines the evaluation process in the leather industry.