Bilgisayar Mühendisliği Bölümü Diğer Yayınlar Koleksiyonu
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Öğe Radiotoxic concentrations and risk levels along the world's coastlines during the quarter of a century(Elsevier B.V., 2025) Abbasi, Akbar; Algethami, Merfat; Zakaly, Hesham M.H.; Mirekhtiary, FatemehThe radioactivity contamination of marine environments polluted by natural and anthropogenic radionuclides has been discussed for decades worldwide. However, there is a lack of data on the current situation and trends in this research field. For this reason, this is the first study to report an integrated data statistical analysis of radioactivity concentration mapping and systematic review using the published database from 2000 to date. The study encompassed five continents: Asia, Africa, Oceania, Europe, and America. The primary sources of natural radioactivity were 226Ra, 232Th, and 40K, which are associated with geological features such as coastal structures and the seabed. Also, the contamination of anthropogenic radionuclide 137Cs is reported in some places that are released by nuclear reactions. The annual dose rate was proved to be the basis for assessing the radiation risk of natural radioactivity. This research outcome is useful for pointing out the need for future research and supporting the development of this topic. © 2024 Elsevier B.V.Öğe Shedding light on and comparing three different mathematical models of the optical conductivity concept(Elsevier Ltd., 2025) Alharshan, Gharam A.; Saudi, H.A.; Issa, Shams A.M.; Zakaly, Hesham M.H.; Gomaa, Hosam M.The optical response in materials offers valuable insights into their properties, especially regarding interband transitions, distinct from direct current responses. By adjusting the frequency of electromagnetic radiation, interband transitions and energy band mappings can be explored, even in materials like graphene. Optical conductivity, which measures a material's ability to conduct electricity under the influence of light, is pivotal across physics, materials science, and engineering. It quantifies a material's efficiency in absorbing and transporting electromagnetic energy as photons. Typically described by Drude's model, optical conductivity has applications in diverse fields, from designing specific optical properties in materials to optimizing solar cells and developing photonic devices. Plasmonics, meta-materials, and renewable energy research also benefit from understanding and controlling optical conductivity. The optical conductivity problem centers on comprehending materials’ electrical interactions with light across the optical spectrum, which is vital for various technologies. Theoretical models, simulations, and experiments address this problem, aiming to develop tunable materials and enhance theoretical models for accurate prediction of optical properties. Mathematical models, such as Maxwell's equations, the Lorentz-Drude model, and the Hosam-Heba model, elucidate optical conductivity, aiding in understanding light-material interactions and predicting material behavior under electromagnetic radiation. Each model offers a unique perspective on optical conductivity, with different theoretical foundations and mathematical formulations that can be applied depending on the specific properties of the material being studied. Understanding and manipulating optical conductivity is foundational to utilizing light across various technological applications. © 2024 Elsevier LtdÖğe VM consolidation steps in cloud computing: A perspective review(Elsevier B.V., 2025) Rozehkhani, Seyyed Meysam; Mahan, Farnaz; Pedrycz, WitoldThe proliferation of cloud-based applications, data, and services has significantly transformed digital interactions, driven by the widespread use of powerful smart devices and the expansion of cloud ecosystems. These ecosystems rely on data centers composed of Physical Machines (PMs) and Virtual Machines (VMs). The increasing demand for cloud services has led to extensive use of physical servers, resulting in high energy consumption and inefficient resource utilization. Consequently, optimizing resource allocation and reducing power consumption have become pivotal challenges in data center management. A key strategy to address these challenges is Virtual Machine Consolidation (VMC), which optimizes computing resources by consolidating multiple VMs into fewer PMs. This paper comprehensively reviews the three critical phases involved in VMC: PM detection, VM selection, and VM placement. Through an extensive analysis of literature spanning from 2015 to 2024, this review seeks to provide valuable insights into the current landscape of VMC and its potential ramifications on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics when the emphasis should have been on the three primary steps in VMC. The importance of this categorization lies in its ability to provide clarity, organization, and a structured framework for comprehending the intricate landscape of VMC. VMC is a multifaceted undertaking encompassing numerous subtasks. Categorization simplifies this complexity by breaking it down into manageable components. Researchers can address each category individually, potentially leading to more focused and effective solutions. © 2024 Elsevier B.V.Öğe An assessment of microstructure, dentinal tubule occlusion and X-ray attenuation properties of Nd: YAG laser-enhanced titanium-doped phosphate glass and nano-hydroxyapatite pastes (vol 130, 313, 2024)(Springer heidelberg, 2024) Abou Neel, Ensanya A.; El-Damanhoury, Hatem M.; Hossain, Kazi M. Zakir; Alawadhi, Hussain; AlMisned, Ghada; Tekin, Hüseyin OzanAn assessment of microstructure, dentinal tubule occlusion and X-ray attenuation properties of Nd: YAG laser-enhanced titanium-doped phosphate glass and nano-hydroxyapatite pastes (vol 130, 313, 2024)Öğe VM consolidation steps in cloud computing: A perspective review(Elsevier b.v., 2024) Rozehkhani, Seyyed Meysam; Mahan, Farnaz; Pedrycz, WitoldThe proliferation of cloud-based applications, data, and services has significantly transformed digital interactions, driven by the widespread use of powerful smart devices and the expansion of cloud ecosystems. These ecosystems rely on data centers composed of Physical Machines (PMs) and Virtual Machines (VMs). The increasing demand for cloud services has led to extensive use of physical servers, resulting in high energy consumption and inefficient resource utilization. Consequently, optimizing resource allocation and reducing power consumption have become pivotal challenges in data center management. A key strategy to address these challenges is Virtual Machine Consolidation (VMC), which optimizes computing resources by consolidating multiple VMs into fewer PMs. This paper comprehensively reviews the three critical phases involved in VMC: PM detection, VM selection, and VM placement. Through an extensive analysis of literature spanning from 2015 to 2024, this review seeks to provide valuable insights into the current landscape of VMC and its potential ramifications on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics when the emphasis should have been on the three primary steps in VMC. The importance of this categorization lies in its ability to provide clarity, organization, and a structured framework for comprehending the intricate landscape of VMC. VMC is a multifaceted undertaking encompassing numerous subtasks. Categorization simplifies this complexity by breaking it down into manageable components. Researchers can address each category individually, potentially leading to more focused and effective solutions.Öğe Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: a review(Elsevier b.v., 2025) Du, Sheng; Ma, Xian; Fan, Haipeng; Hu, Jie; Cao, Weihua; Wu, Min; Pedrycz, WitoldIron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.Öğe Sparsity in transformers: A systematic literature review(Elsevier B.V., 2024) Farina, M.; Ahmad, U.; Taha, A.; Younes, H.; Mesbah, Y.; Yu, X.; Pedrycz W.Transformers have become the state-of-the-art architectures for various tasks in Natural Language Processing (NLP) and Computer Vision (CV); however, their space and computational complexity present significant challenges for real-world applications. A promising approach to address these issues is the introduction of sparsity, which involves the deliberate removal of certain parameters or activations from the neural network. In this systematic literature review, we aimed to provide a comprehensive overview of current research on sparsity in transformers. We analyzed the different sparsity techniques applied to transformers, their impact on model performance, and their efficiency in terms of time and space complexity. Moreover, we identified the major gaps and challenges in the existing literature. Our study also highlighted the importance of investigating sparsity in transformers for computational efficiency, reduced resource requirements, scalability, environmental impact, and hardware-algorithm co-design. By synthesizing the current state of research on sparsity in transformer-based models, we also provided valuable insights into their efficiency, impact on model performance, and potential trade-offs, contributing to advancing the field further. © 2024 Elsevier B.V.Öğe Broad-deep network-based fuzzy emotional inference model with personal information for intention understanding in human–robot interaction(Elsevier Ltd, 2024) Li, M.; Chen, L.; Wu, M.; Hirota, K.; Pedrycz, W.A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human–robot interaction. It aims to understand students’ intentions in the university teaching scene. Initially, we employ convolution and maximum pooling for feature extraction. Subsequently, we apply the ridge regression algorithm for emotional behavior recognition, which effectively mitigates the impact of complex network structures and slow network updates often associated with deep learning. Moreover, we utilize multivariate analysis of variance to identify the key personal information factors influencing intentions and calculate their influence coefficients. Finally, a fuzzy inference method is employed to gain a comprehensive understanding of intentions. Our experimental results demonstrate the effectiveness of the BDFEI model. When compared to existing models, namely FDNNSA, ResNet-101+GFK, and HCFS, the BDFEI model achieved superior accuracy on the FABO database, surpassing them by 12.21%, 1.89%, and 0.78%, respectively. Furthermore, our self-built database experiments yielded an impressive 82.00% accuracy in intention understanding, confirming the efficacy of our emotional intention inference model. © 2024 Elsevier LtdÖğe Artificial intelligence for production, operations and logistics management in modular construction industry: A systematic literature review(Elsevier B.V., 2024) Liu, Q.; Ma, Y.; Chen, L.; Pedrycz, W.; Skibniewski, M.J.; Chen, Z.-S.Artificial intelligence (AI) has garnered significant attention within the modular construction industry, emerging as a prominent frontier development trend. A comprehensive and systematic analysis is required to gain a thorough understanding of the existing literature on the use of AI in the management of production, operations, and logistics within the modular construction industry. This review delves into the various aspects of AI implementation in this sector, adopting a critical perspective. The objective of this paper is to analyze the progress, suitability, and research patterns in the field of AI for the management of productions, operations, and logistics within the modular construction industry. First, a concise overview of AI technologies pertaining to the contemporary research on the production, operations and logistics management of the modular construction industry is provided. Second, a bibliometric analysis is performed to provide a comprehensive overview of the existing publications pertaining to this subject matter. Subsequently, this paper presents literature reviews and outlines future directions for each component, specifically AI in the context of production management, operations management, and logistics management within the modular construction industry. The review provides a valuable knowledge base and roadmap to guide future research and development efforts in AI-enhanced modular construction management. © 2024 Elsevier B.V.Öğe Editorial: Innovation and trends in the global food systems, dietary patterns and healthy sustainable lifestyle in the digital age(Frontiers Media, 2023) Hoteit, Maha; Qasrawi, Radwan; Al Sabbah, Haleama; Tayyem, ReemaThe global food systems are undergoing significant changes due to evolving dietary habits and the digital era's influence, impacting health and overall global stability. As processed foods and sedentary lifestyles become more prevalent, there's a marked increase in non-communicable diseases like obesity and diabetes. Despite advancements in food security in developed regions, low-to-middle-income countries still grapple with substantial challenges, exacerbated by the COVID-19 pandemic's disruptions. Technology offers promising solutions. Developments in artificial intelligence, data science, and ICT are reshaping our understanding and approaches to global food systems, dietary choices, and sustainable health behaviors. This Research Topic compiles studies examining the intersection of food security, nutrition, and technological innovation. Comprising 15 papers, the collection emphasizes global dietary trends, especially in the Eastern Mediterranean Region, both pre and post-COVID-19. Highlights include the growing prevalence of nutrition-related diseases in the region, the efficacy of long-term dietary interventions for obesity, the links between dietary patterns and childhood anemia, and the ripple effect of parental dietary habits on families. The importance of maintaining practices like the Mediterranean Diet is also underscored, given its health benefits.Öğe Boron nitride nanosheet-reinforced WNiCoFeCr high-entropy alloys: the role of B4C on the structural, physical, mechanical, and radiological shielding properties (vol 128, 694, 2022)(SPRINGER HEIDELBERG, 2022) Kavaz, Esra; Gül, Ali Oktay; Başgöz, Öyküm; Güler, Ömer; Almisned, Ghada; Bahçeci, Ersin; Güler, Seval Hale; Tekin, Hüseyin OzanNo Abstract Available.
