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Review Citation - WoS: 247Citation - Scopus: 446Transformative Effects of Iot, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges(Elsevier, 2019) Gill, Sukhpal Singh; Tuli, Shreshth; Xu, Minxian; Singh, Inderpreet; Singh, Karan Vijay; Lindsay, Dominic; Garraghan, PeterCloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing. (C) 2019 Elsevier B.V. All rights reserved.Review Citation - WoS: 6Citation - Scopus: 9Monkeypox: a Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications(Shaheed Beheshti University of Medical Sciences and Health Services, 2024) Rahmani,E.; Bayat,Z.; Farrokhi,M.; Karimian,S.; Zahedpasha,R.; Sabzehie,H.; Farrokhi,M.Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections. © This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).Article Factors Affecting Dentists' Intention To Adopt Artificial Intelligence: An Extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model(Emerald Group Publishing Ltd, 2025) Alqaifi, Faten; Tengilimoglu, DilaverPurposeAdvancements in science and technology have integrated artificial intelligence (AI) into dentistry, improving treatment processes, operational efficiency, and clinical outcomes. However, AI adoption among dentists remains underexplored, hindering progress in oral healthcare. This study aims to identify key barriers to AI adoption and examine factors influencing dentists' intention to use AI.Design/methodology/approachA quantitative cross-sectional approach was employed, utilizing self-administered questionnaires distributed online and across various dental clinics and hospitals in Ankara, Turkey. A total of 440 dentists participated in the study. Data analysis was conducted using SPSS and SmartPLS.FindingsThe study found that AI-anxiety negatively affects the intention to adopt AI in dentistry, showing a medium (almost large) effect that is stronger than other UTAUT factors such as performance expectancy, effort expectancy, and social influence, which demonstrated only small effects. Dentists with higher anxiety about learning and sociotechnical blindness are less likely to adopt AI, while concerns about job replacement and AI-configuration have less but still significant impact.Research limitations/implicationsThese results contribute to the growing body of knowledge on technology adoption in oral healthcare and provide practical implications for technology developers, policymakers, and other stakeholders seeking to facilitate AI integration in dentistry.Originality/valueThis study provides novel insights into AI adoption in dentistry, offering guidance for future development and integration, and addressing a critical research gap in a growing field-particularly in Turkey, where implementation is still in its early stages.Article Citation - WoS: 1Citation - Scopus: 2Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review(MDPI, 2025) Ayhan, Cagri; Mekhaeil, Marina; Channawi, Rita; Ozcan, Alp Eren; Akargul, Elif; Deger, Atakan; Soliman, OsamaAcute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA.Article Artificial Intelligence Based Resuscitation Simulation: A Pilot Study of a Novel Approach to Team Leadership Training(BMC, 2026) Kanbakan, Altug; Berikol, Goksu Bozdereli; Ilhan, Bugra; Altintas, Emel; Doganay, FatihIntroduction Team leadership training is essential alongside with technical training for effective resuscitation management. Addressing this gap, we developed a novel simulation system leveraging Large Language Models (LLMs) to create Artificial Intelligence (Al) agents simulating team members in Advanced Cardiovascular Life Support (ACLS) scenarios. This pilot study aimed to to develop a novel LLM-based ACLS simulation training platform and evaluate its performance in simulated resuscitation scenarios on established protocols.
Method Using the Claude 3.5 Sonnet API, we designed a simulation system with four Al agents assigned specific roles as healthcare staff within an ACLS team. Each agent strictly followed the 2020 American Heart Association (AHA) ACLS guidelines while interacting with an ACLS certified emergency medicine specialist user. The ten patient scenario transcripts were evaluated with three blinded emergency medicine specialists whether all the recommended steps are completed. Inter-rater reliability was assessed using Kendall's W and Krippendorff's Alpha statistics to evaluate agreement both within raters and the model.
Results Al agents consistently adhered to the AHA 2020 ACIS algorithm across scenarios, with a high inter-rater reliability (Kendall's W > 0.75 ) . Krippendorff's Alpha values for agreement ranged from substantial (0.84) to almost perfect (0.99), indicating robust compliance with guidelines and effective simulation of resuscitation responses.
Conclusion This study highlights the potential of LL.M-powered simulations as an adjunct to traditional resuscitation training. The system effectively supported team leadership training by providing consistent and guideline-compliant responses. While the results are promising, further research with larger participant samples is necessary to evaluate the long-term educational impact and scalability of such systems.Article An Empirical Study of the Technoparks in Turkey in Investigating the Challenges and Potential of Designing Intelligent Spaces(Mdpi, 2023) Erisen, SerdarThe use of innovative technologies in workspaces, such as the Internet of Things (IoT) and smart systems, has been increasing, yet it remains in the minority of the total number of smart system applications. However, universities and technopoles are part of open innovation that can encourage experimental IoT and smart system projects in places. This research considers the challenges and advantages of developing intelligent environments with smart systems in the Technology Development Zones (TDZs) of Turkey. The growth of Silicon Valley has inspired many technopoles in different countries. Thus, the article includes first a comprehensive survey of the story of Silicon Valley and the emerging technological potential of open and responsible innovation for intelligent spaces and technoparks with rising innovative interest. The study then conducts empirical research in inspecting the performance of TDZs in Turkey. In the research, machine learning and Artificial Intelligence (AI) models are applied in the analyses of critical performance indicators for encouraging incentives and investments in innovative attempts and productivity in TDZs; the challenges, potential, and need for intelligent spaces are evaluated accordingly. This article also reports on the minority of the design staff and the lack of innovation in developing intelligent spaces in the organization of the creative class in Turkey. Consequently, the research proposes a set of implementations for deploying intelligent spaces to be practiced in new and existing TDZs by considering their potential for sustainable and responsible innovation.Article Citation - WoS: 11Citation - Scopus: 13Choice Functions for Autonomous Search in Constraint Programming: Ga Vs. Pso(Univ Osijek, Tech Fac, 2013) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Palma, Wenceslao; Monfroy, Eric; Castro, Carlos; Paredes, Fernando; Computer EngineeringThe variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.
