6 results
Search Results
Now showing 1 - 6 of 6
Article Citation - Scopus: 4University Librarians’ Perceptions Of Artificial Intelligence, Its Application Areas İn Libraries, And The Future;(University and Research Librarians Association (UNAK), 2024) Cuhadar, Sami; Gurdal, Gultekin; Erken, Mehmet; Mert, Selma; Gezer, Cagatay; Helvacıoğlu, Ece; Atli, SongülGünümüzde kütüphaneler, değişen teknoloji ve yeniliklerden etkilenen kurumlar arasında yer almaktadır. Yapay zeka teknolojilerinin popüler hale gelmesi, kütüphane hizmetlerini de dönüştürmeye başlamıştır. Bu araştırmada, Türkiye’deki üniversite kütüphanelerinin yapay zeka teknoloji ve uygulamalarının gelişim sürecinde yapmış olduğu ve yapmayı planladığı düzenlemeleri tespit etmek ve ilgili döneme özel geliştirdikleri hizmetleri belirlemek amacıyla bir anket uygulanmıştır. Anket, Türkiye’deki 208 üniversite kütüphanesinden 111 üniversite kütüphanesi yöneticisinin katılımıyla gerçekleştirilmiştir. Verilerin analizi ile üniversite kütüphanelerinin yapay zeka teknolojileri ve uygulamaları hakkındaki durumu, bilgi ve farkındalık düzeyleri belirlenmiş, eksik ve zayıf yönlerin geliştirilmesine yönelik önlemler ve öneriler sunulmuştur. İlgili araştırma, yapay zeka konusunda Türkiye’de üniversite kütüphanesi yöneticilerinden görüş ve öneri alarak gerçekleştirilen ilk ve en kapsamlı çalışmadır. Araştırma bulguları, üniversite kütüphanelerinin ChatGPT, Gemini, Grammarly vb. yapay zeka uygulamalarını belirli düzeyde kullandıklarını ancak yapay zeka ile ilgili kurumsal politika geliştirme, personele yetkinlik kazandırma ve planlama konularında ihtiyaçlarının olduğunu ortaya çıkarmıştır.Article An Investigation Into The AI-Assisted Visualization Of Children’s Songs: The Case Of Ali Baba’s Farm(Nilgun SAZAK, 2025) Südor, S.; İpekçiler, B.This study aims to visualize children’s songs, which are part of primary-level music education, using AI-supported tools. The objectives of the Ministry of National Education’s music course curriculum were examined, and both the themes to be emphasized in song selection and the pedagogical functions of children’s songs were analyzed. In the literature review, the Web of Science and Google Scholar databases were used. The obtained source data were analyzed with the VOSviewer software to generate conceptual maps, through which thematic trends in the field were identified. In the practical part of the study, the children’s song “Old MacDonald’s Farm” was visualized in detail using two different AI-supported tools: RunwayML and WZRD.ai. In RunwayML, prompt-based scenes were generated using the “text-to-video” feature, and visuals compatible with the lyrics of the song were created. On the WZRD.ai platform, visuals were automatically generated in response to sound waves, and the limitations of the platform were examined. Based on the findings, it was concluded that RunwayML offers more effective results for pedagogical content production, while WZRD. ai, despite its technical capabilities, falls short in delivering child-appropriate visual stimuli. The study also provides a theoretical foundation on synesthesia and discusses how AI tools can be integrated into music education in early childhood and primary school levels. The findings indicate that AI-supported visualization tools have the potential to provide engaging and flexible educational materials that support learning at the primary school level. It is recommended that teacher training programs develop hands-on modules for these tools, and that future research focus on how these technologies can be adapted to various songs, age groups, and learning domains. © © 2025 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited.Article Citation - Scopus: 1The Rise of Artificial Intelligence in Vascular Surgery: a Bibliometric Analysis (2020-2024)(Turkish National Vascular and Endovascular Surgery Society, 2024) Tosun, Burcu; Demirkılıç, UfukAim: This study aims to perform a comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery, identifying key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field.Material and Methods: The analysis covers 815 documents published from 2020 to 2024, retrieved from the Web of Science Core Collection database. Metrics analyzed include publication growth, citation rates, key contributors, leading journals, prevalent themes, and international collaborations.Results: The research output showed a 15% annual growth rate, peaking in 2023. Despite increasing publications, the average citation rate per article declined. The study identified 5039 contributors with significant international co-authorship. Leading authors included Lareyre F and Raffort J, and the \"Journal of Vascular Surgery\" was the most influential journal. The USA and China led in contributions, reflecting robust research infrastructure. Key themes include risk assessment, diagnostic methods, and patient management, highlighting AI's role in enhancing diagnostic accuracy, treatment planning, and patient outcomes in vascular surgery.Conclusion: The analysis highlights the rapid growth and collaborative nature of AI research in vascular surgery. Key contributors, influential journals, and emerging themes were identified, emphasizing AI's role in improving diagnostics and patient outcomes. Limitations include the focus on one database and a five-year period, suggesting future research should include more databases and a longer timeframe. Exploring high-impact studies and practical applications will further advance the field.Article Revolutionizing Glaucoma Care: Harnessing Artificial Intelligence for Precise Diagnosis and Management(Gazi Eye Foundation, 2025) Ucgul, A.Y.; Aktaş, Z.Glaucoma is a leading cause of irreversible blindness worldwide, necessitating early detection and effective management to prevent vision loss. Recent advancements in artificial intelligence (AI) have revolutionized glaucoma care by enhancing diagnostic accuracy, monitoring disease progression, and personalizing treatment strategies. AI models, including machine learning and deep learning algorithms, have demonstrated exceptional performance in analyzing fundus photography, optical coherence tomography, and visual field data, surpassing traditional diagnostic methods. Convolutional neural networks have shown high sensitivity and specificity in detecting glaucomatous changes, while vision transformers and hybrid AI models further refine risk assessment and prognosis. Additionally, AI-powered monitoring systems utilizing multi-modal data integration allow for more precise prediction of disease progression and the need for surgical intervention. The incorporation of AI into telemedicine and wearable intraocular pressure sensors extends glaucoma management to remote and underserved populations. Despite these advancements, challenges remain, including issues related to algorithm generalizability, data standardization, bias, and ethical concerns regarding AI-driven clinical decision-making. To maximize AI’s potential in glaucoma care, further interdisciplinary research, regulatory oversight, and multi-center validation studies are needed. By addressing these challenges, AI can be effectively integrated into clinical practice, leading to improved early detection, enhanced treatment strategies, and more personalized patient care. The future of AI in glaucoma management holds great promise, paving the way for a more data-driven and patient-centered approach to combating this sight-threatening disease. © 2024 The author(s).Conference Object Hybrid AI-Driven Decision Model for Test Automation in Agile Software Development(Institute of Electrical and Electronics Engineers Inc., 2025) Bon, Mohammad; Yazici, AliTest automation plays an essential role in Agile Software Development (ASD), but its implementation remains complex. This study conducts a Systematic Literature Review (SLR) to identify key points of test automation and recent developments in Artificial Intelligence (AI). Based on 21 factors proposed by Butt et al., we construct a three-phase decision-support model addressing software, tools, tests, human, and economic dimensions. To improve this model, modern AI techniques - including natural language processing (NLP), machine learning (ML), Mabl (a self-healing, AI-based test automation tool) and Parasoft Selenic - are used. These technologies automate test case generation, prioritization, and maintenance, aligning with Agile's fast-paced demands. Our proposed hybrid model applies NLP to identify effecting factors, ML for impact scoring, and reinforcement learning (RL) for guiding automation strategies. The goal is to decrease manual processes, improve decision accuracy, and to adapt to evolving requirements. However, challenges such as data quality and the need for AI expertise remain. Future work should focus on practical validation and explore applications in non-functional testing. This study offers a practical, AI-enhanced framework to support Agile teams in streamlining test automation. © 2025 IEEE.Conference Object AI Trustworthiness and Student Pilots: Exploring Attitudes, Anxieties, and Adaptation Performance(Elsevier B.V., 2025) Ceken, S.; Yilmaz, A.A.; Acar, A.B.This research explores the attitudes of student pilots toward artificial intelligence (AI) applications within the aviation sector, with a focus on their adaptation processes and potential challenges. The recent release of the "EASA AI Roadmap 2.0"by the European Union Aviation Safety Agency (EASA) underscores the growing impact of AI on aviation, driving the emergence of new business models and emphasizing a human-centric approach to AI integration within the aviation industry. This study addresses a significant gap in the literature by examining student pilots' perspectives on AI, specifically focusing on AI trustworthiness, attitudes, anxieties, and adaptation performance. The study utilizes a quantitative research approach, collecting data from 150 student pilots through surveys. Preliminary results from 106 respondents indicate varied attitudes toward AI, with significant implications for AI-supported cockpit assistant systems and the broader aviation industry. The study sample consisted of 106 (Mage = 23.6, SDage= 4.64; 79% male) student pilots from of university pilot training departments and various flight school in Turkey. Collected data were analyzed on SPSS 29. The study revealed that Sociotechnical Blindness AI anxiety is a significant predictor of general attitudes toward AI among student pilots. This finding suggests that higher levels of anxiety related to the perceived complexity and potential unintended consequences of AI are associated with more positive general attitudes toward AI. The findings emphasize the need for a human-centric approach to AI integration, highlighting the importance of trust, transparency, and adaptive training in the successful adoption of AI technologies in aviation. © 2024 The Authors. Published by ELSEVIER B.V.
