Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article The Synergistic Engine of Sustainable Entrepreneurship: Fueling AI-Driven Circular Transformation and Social Entrepreneurial Orientation with Knowledge Integration and Digital Capabilities(Elsevier B.V., 2026-09) Shah, Syed Haider Ali; Murad, Majid; Wang, MansiDrawing on dynamic capability theory, this study examines how AI-driven circular transformation (AIT) and social entrepreneurship orientation (SEO) contribute to sustainable entrepreneurial success (SES). This study further investigates the mediating role of knowledge integration (KNI) and the moderating effect of digital capabilities (DIC) in these relationships. Data were collected from 442 top-level managers working in high-tech manufacturing industries in Guangdong Province, China, and were analyzed using partial least squares structural equation modeling. The empirical findings suggest that both AI-driven circular transformation and SEO have a positive influence on SES. Moreover, KNI is found to significantly mediate the relationships between AIdriven circular transformation, SEO, and SES. Additionally, DIC positively moderate the relationship between KNI and SES. Furthermore, this study offers implications for managers and policymakers seeking to promote sustainable entrepreneurship. The results highlight the importance of integrating AI-enabled circular practices with socially oriented entrepreneurial strategies to enhance long-term entrepreneurial outcomes. Finally, the results suggest that investments in DIC and effective KNI mechanisms can strengthen firms' dynamic capabilities, thereby supporting sustainability-oriented innovation and entrepreneurial success.Article Citation - Scopus: 5Hyperplastic and Tubular Polyp Classification Using Machine Learning and Feature Selection(Elsevier B.V., 2024) Doǧan, Refika Sultan; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentPurpose: The aim of this study is to develop an effective approach for differentiating between hyperplastic and tubular adenoma colon polyps, which is one of the most difficult tasks in colonoscopy procedures. The main research challenge is how to improve the classification of these polyp subtypes applying various focusing levels on the polyp images, data preprocessing approaches, and classification algorithms. Methods: This study employed 202 colonoscopy videos from a total of 201 patients, focusing on 59 videos containing hyperplastic and tubular adenoma polyps. Manually extract key frames and several feature extraction and classification techniques were applied. The influence of different datasets with various focuses as well as data preprocessing steps on the performance of classification was examined, and AUC values were calculated using ten classifiers. Results: The study discovered that the optimal dataset, data preprocessing method, and classification algorithm all had significant effects on classification results. The Random Forest model with the Recursive Feature Elimination (RFE) feature selection approach, for example, consistently outperformed other models and achieved the highest AUC value of 0.9067. In terms of accuracy, F1 score, recall, and AUC, the suggested model outperformed a gastroenterologist, nevertheless precision remained slightly lower. Conclusion: This study emphasizes the importance of dataset selection, data preprocessing, and feature selection in enhancing the classification of difficult colon polyp subtypes. The suggested model offers a promising model for the clinical differentiation of hyperplastic and tubular adenoma polyps, potentially improving diagnostic accuracy in gastroenterology. © 2024 Elsevier B.V., All rights reserved.