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.Conference Object Citation - Scopus: 11Energy Transfer in Two-Level Quantum Systems via Speed Gradient-Based Algorithm(Elsevier B.V., 2015) Pechen, Alexander N.; Borisenok, S.We develop the speed gradient-based algorithm for controlled transfer of energy in a two-level quantum system towards a predefined value of energy using as control spectral density of incoherent photons. The algorithm can stabilize energy at a value less than one half of the energy gap between the two system states and is shown to be more effective for cooling than for heating. © 2021 Elsevier B.V., All rights reserved.Article Citation - Scopus: 63Energy Efficient and Reliable Data Gathering Using Internet of Software-Defined Mobile Sinks for WSNS-Based Smart Grid Applications(Elsevier B.V., 2019-10) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıThe smart grid is an emerging concept that introduces innovative ways to handle the power quality and reliability issues for both service provider and consumers. The key aims of the smart grid (SG) in smart cities (SCs) is to preserve a certain level of residents’ life quality and support the entire spectrum of their economic activities. In this paper, we present a novel Energy Efficient and Reliable Data Gathering Routing Protocol (ODGRP) for wireless sensor networks (WSNs)-based smart grid applications. The developed scheme employs a software-defined centralized controller and multiple mobile sinks for energy efficient and reliable data gathering from WSNs in the SG. The extensive simulation results conducted through the EstiNet 9.0 show that the designed scheme outperforms existing approaches and achieves its defined goals for event-driven applications in the SG. © 2019 Elsevier B.V., All rights reserved.Article Citation - Scopus: 14CoviDetector: A Transfer Learning-Based Semi Supervised Approach to Detect COVID-19 Using CXR Images(Elsevier B.V., 2023-06) Chowdhury, Deepraj; Das, Anik; Dey, Ajoy; Banerjee, Soham; Golec, Muhammed; Kollias, Dimitrios; Arya, Rajesh Chand; Uhlig, SteveCOVID-19 was one of the deadliest and most infectious illnesses of this century. Research has been done to decrease pandemic deaths and slow down its spread. COVID-19 detection investigations have utilised Chest X-ray (CXR) images with deep learning techniques with its sensitivity in identifying pneumonic alterations. However, CXR images are not publicly available due to users’ privacy concerns, resulting in a challenge to train a highly accurate deep learning model from scratch. Therefore, we proposed CoviDetector, a new semi-supervised approach based on transfer learning and clustering, which displays improved performance and requires less training data. CXR images are given as input to this model, and individuals are categorised into three classes: (1) COVID-19 positive; (2) Viral pneumonia; and (3) Normal. The performance of CoviDetector has been evaluated on four different datasets, achieving over 99% accuracy on them. Additionally, we generate heatmaps utilising Grad-CAM and overlay them on the CXR images to present the highlighted areas that were deciding factors in detecting COVID-19. Finally, we developed an Android app to offer a user-friendly interface. We release the code, datasets and results’ scripts of CoviDetector for reproducibility purposes; they are available at: https://github.com/dasanik2001/CoviDetector © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 6Amelioration Potential of Synthetic Oxime Chemical Cores Against Multiple Sclerosis and Alzheimer's Diseases: Evaluation in Aspects of in Silico and in Vitro Experiments(Elsevier B.V., 2024-12) Yilmaz, Anil; Koca, Murat; Ercan, Selami; Acar, Özden Ozgun; Boǧa, Mehmet; Sen, Alaattin; Kurt, AdnanAlzheimer disease (AD) and multiple sclerosis (MS) are inflammatory neurological disorders. The main symptom of AD is dementia, and the main symptoms of MS are vertigo, sexual dysfunction, cognitive problems, and fatigue. Today, millions of people are affected by AD and MS, and the number is growing day by day. However, there are not any accurate remedies for both disorders. For this reason, discovering novel drug molecules against neurological disorders such as AD and MS is essential and precious. Oximes and benzofurans exhibit many pharmacological effects including anti-inflammatory and neurological activities. Thus, several novel compounds bearing oxime and benzofuran chemical cores were designed and synthesized, and their in vitro anticholinesterase activities were investigated in our previous study. A number of the synthesized molecules showed excellent anticholinesterase activity against both AChE and BChE enzymes. The mentioned study constituted a background for this study. In this study, we picked different chemical skeletons among all the synthesized molecules to conduct further in silico and in vitro experiments. In order to support our in vitro anticholinesterase findings, we also examined in silico anti-Alzheimer activity of the selected molecules. In addition, in silico and in vitro activities against MS disease of the synthesized molecules were investigated. Molecule 4 extraordinarily showed outstanding activity against AD disease both in silico and in vitro, as well as in silico activity against MS disease. This feature makes molecule 4 a possible drug lead molecule which is very limited in the market. On the other hand, molecule 1, a less substituted oxime skeleton, demonstrated the strongest in vitro activity against MS disease through in vitro anti-inflammatory effect. As an observation, molecule 4 was determined to be the most promising molecule to focus on in the further steps. © 2024 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Data-Driven Discovery and DFT Modeling of Fe4H on the Atomistic Level(Elsevier B.V., 2024) Zagorac, Dejan; Zagorac, Jelena; Djukic, Milos B.; Bal, Burak; Schön, Johann ChristianSince their discovery, iron and hydrogen have been two of the most interesting elements in scientific research, with a variety of known and postulated compounds and applications. Of special interest in materials engineering is the stability of such materials, where hydrogen embrittlement has gained particular importance in recent years. Here, we present the results for the Fe-H system. In the past, most of the work on iron hydrides has been focused on hydrogen-rich compounds since they have a variety of interesting properties at extreme conditions (e.g. superconductivity). However, we present the first atomistic study of an iron-rich Fe4H compound which has been predicted using a combination of data mining and quantum mechanical calculations. Novel structures have been discovered in the Fe4H chemical system for possible experimental synthesis at the atomistic level. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 5Citation - Scopus: 5Formation of a Very High-Density Amorphous Phase of Carbon and Its Crystallization into a Simple Cubic Structure at High Pressure(Elsevier B.V., 2021) Durandurdu, M.We report a direct computational evidence of a two-step transformation sequence for tetrahedral amorphous carbon (ta-C) with increasing pressure. First, ta-C gradually transforms into a very high-density amorphous phase (VHDA) phase. Second, the VDHA phase converts into a simple cubic (SC) crystal. The structural defects formed during the high-pressure treatment play important roles for the formation and stabilization of the SC structure, rather than favorable the SC4 crystal. These phase transformations are reversible. © 2021 Elsevier B.V., All rights reserved.