Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

Browse

Filters

2024 - 20266

Settings

Access Right: Closed AccessSubject: search.filters.subject.Artificial Intelligence

Search Results

Now showing 1 - 6 of 6
  • Conference Object
    Arabic Sign Language Paradigm Enhancement
    (American Institute of Physics, 2026) Aljuboori, Mohammed Khaleel Naser; Tasel, Faris Serdar
  • Conference Object
    Leveling Up Software Project Management Education: From Manual Mastery to AI Empowerment
    (Institute of Electrical and Electronics Engineers Inc., 2025) Tunc, Sevgi Koyuncu
  • Book Part
    Artificial Intelligence in Dentistry
    (CRC Press, 2025) Cagiltay, Nergiz Ercil; Kılıçarslan, Mehmet Ali; Basmaci, Fulya
    Today, with advanced technologies, collecting detailed and big data from the environment and analyzing it using intelligent techniques has become possible, providing important insights into phenomena as well as future predictions. Big data is characterized by its high volume, velocity, and variety. Here, the volume is the amount and size of the data, which is measured in terabytes, petabytes, exabytes, or zettabytes. Velocity is the offered form of big data, which can be batch, near-real-time, real-time, or streaming. Finally, variety is the structure of the big data, which can be structured, such as in relational or dimensional models, as in warehouses, or unstructured, which is stored without any organization. It can also be in semi-structured form, where the data is unstructured but there is some meta-data or some tags for describing the data. Today, these forms of data are being collected for different dental purposes in several formats, such as images, raw data, or coordinates. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Propagation of Higher-Order Annular Gaussian Beams in Biological Tissues
    (Optica Publishing Group, 2025) Arpali, Serap Altay; Baykal, Yahya Kemal
    The propagation characteristics of a higher-order annular Gaussian (HOAG) beam in biological tissue turbulence are investigated. Average intensity at the receiver plane is found when the HOAG source field is used as excitation. The effects of the HOAG beam on different tissue types of the upper dermis (human), liver parenchyma (mouse), intestinal epithelium (mouse), and deep dermis (mouse) are studied. Variations of the average intensity versus the source and medium parameters such as the strength coefficient of the refractive-index fluctuations, propagation distance, wavelength, and beam size are presented. The results show that all modes of the HOAG beam can successively transmit beam energy at different levels of turbulence for all tissue types. At the same turbulence strength, HOAG beams having larger mode numbers transmit higher intensity to receivers than the modes with smaller mode orders, which is valid for all the examined tissue types. As the strength of tissue turbulence increases, the HOAG beam slowly turns into a pure Gaussian beam. For the different tissue types, the highest beam intensity at the receiver was observed for the deep dermis (mouse) tissue type. Despite the change in wavelength, refractive-index fluctuations, and source beam size, the highest beam transmission through the tissue in a turbulent environment was also observed for this same tissue type. This research may be useful in understanding the fundamentals of lighttissue interaction of HOAG laser beams, which may improve noninvasive disease detection and therapy methods through tissue in biophotonic technologies. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 6
    Transforming Hospitality: the Dynamics of AI Integration, Customer Satisfaction, and Organizational Readiness in Enhancing Firm Performance
    (Emerald Group Publishing Ltd, 2025) Ali, Muhammad; Khan, Tariq Iqbal; Sener, Irge
    PurposeThis study aims to explore the interconnectedness between artificial intelligence (AI) integration, customer satisfaction, process task efficiency and organizational readiness within the hospitality and tourism sector, elucidating their combined influence on firm performance.Design/methodology/approachThe research sample comprises 790 owners, supervisors, managers, customers and employees from 158 firms from hospitality and tourism firms in Guangzhou. This study uses a multimodel approach to analyze the relationships between AI integration, customer satisfaction, process task efficiency, organizational readiness and firm performance.FindingsModel 1 indicates a positive correlation between AI integration and firm performance. Model 2 introduces customer satisfaction as a mediator, revealing its partial mediation effect on the relationship between AI integration and firm performance. Model 3 expands to demonstrate the moderating effect of process task efficiency on the AI integration-firm performance relationship. Finally, Model 4 incorporates organizational readiness as a predictor, enhancing the model's fit and emphasizing its significance in driving firm performance alongside other factors.Research limitations/implicationsThis study's scope is limited to the hospitality and tourism sector in Guangzhou, potentially restricting the generalizability of findings to other industries or regions. Future research could explore diverse contexts to ascertain broader implications.Practical implicationsThe findings underscore the multifaceted impact of AI integration on organizational outcomes, highlighting strategic opportunities for firms to enhance performance through investments in AI integration and organizational preparedness.Originality/valueThis study contributes to the understanding of how AI integration, along with factors like customer satisfaction, process task efficiency and organizational readiness, collectively shape firm performance within the hospitality and tourism sector, offering valuable insights for strategic decision-making and resource allocation. (sic)(sic)(sic)(sic)(sic)(sic)(sic):AI(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic),(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)158(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)790(sic)(sic)(sic),(sic)(sic),(sic)(sic),(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic),(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)1(sic)(sic), AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)2(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)3(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)4(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)AI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Advancing Nanomaterials Research: a Comprehensive Review of Artificial Intelligence Applications in Geotechnical Properties
    (Techno-Press, 2024) Cemiloglu, A.; Zhu, L.; Arslan, S.; Nanehkaran, Y.A.; Azarafza, M.; Derakhshani, R.
    This article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI’s ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI’s capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI’s capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry. © 2024 Techno-Press, Ltd.