Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed

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Citation - WoS: 1
Citation - Scopus: 1
3-State Protein Secondary Structure Prediction Based on Scope Classes
(Inst Tecnologia Parana, 2021) Atasever, Sema; Azginoglu, Nuh; Erbay, Hasan; Aydin, Zafer
Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q(3) accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSI PRED on the SCOPe test datasets and found that our method outperformed PSI PRED.
3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
3Mont: A Multi-Omics Integrative Tool for Breast Cancer Subtype Stratification
(Public Library Science, 2025) Unlu Yazici, Miray; Marron, J. S.; Bakir-Gungor, Burcu; Zou, Fei; Yousef, Malik; Yazici, Miray Unlu
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.
Citation - WoS: 12
Citation - Scopus: 14
4D-QSAR Investigation and Pharmacophore Identification of Pyrrolo[2,1-C][1,4]Benzodiazepines Using Electron Conformational-Genetic Algorithm Method
(Taylor & Francis Ltd, 2016) Ozalp, A.; Yavuz, S. C.; Sabanci, N.; Copur, F.; Kokbudak, Z.; Saripinar, E.
In this paper, we present the results of pharmacophore identification and bioactivity prediction for pyrrolo[2,1-c][1,4]benzodiazepine derivatives using the electron conformational-genetic algorithm (EC-GA) method as 4D-QSAR analysis. Using the data obtained from quantum chemical calculations at PM3/HF level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. The ECMC of the lowest energy conformer of the compound with the highest activity was chosen as the template and compared with the ECMCs of the lowest energy conformer of the other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA, i.e. pharmacophore) by ECSP software. A descriptor pool was generated taking into account the obtained pharmacophore. To predict the theoretical activity and select the best subset of variables affecting bioactivities, the nonlinear least square regression method and genetic algorithm were performed. For four types of activity including the GI(50), TGI, LC50 and IC50 of the pyrrolo[2,1-c][1,4] benzodiazepine series, the r(train)(2), r(test)(2) and q(2) values were 0.858, 0.810, 0.771; 0.853, 0.848, 0.787; 0.703, 0.787, 0.600; and 0.776, 0.722, 0.687, respectively.
A Potential Hemostatic Chitosan/Gelatin Cryogel Impregnated with Verbascum Thapsus Leaf Extract for Noncompressible Hemorrhage Management
(IOP Publishing Ltd, 2025) Uzuner, Hacernur; Yuruk, Adile; Isoglu, Ismail Alper
In this study, we prepared a series of chitosan/gelatin (CS/GEL) cryogels containing Verbascum thapsus (V. thapsus) leaf extract and identified a lead formulation for noncompressible hemorrhage (NCH). Cryogels with average pore diameters ranging from 225 to 478 mu m were fabricated through cryogelation at various CS/GEL ratios. C15 was chosen as the base scaffold due to its homogeneous pore distribution, with a pore size coefficient of variation (CV) of approximately 0.22. Extract loading was 1%, 5%, 10%, and 20% w/v. Functional porosity was reported by the relative accessible void index (RAVI). In PBS, the values relative to neat C15 were 1.00, 0.27, 0.20, 0.13, and 0.09 for concentrations of 0%, 1%, 5%, 10%, and 20% w/v, respectively. In citrated blood, the series was 1.00, 0.29, 0.12, 0.14, and 0.09. After loading, equilibrium swelling decreased and the compressive modulus increased, consistent with partial pore filling in a fixed network. The cryogels maintained an interconnected macroporous network and showed swelling from 300% to 3600% in blood and PBS. Antibacterial activity reached 89% inhibition, and cell viability remained above 80%. Hemolysis was low and within acceptance limits. Clotting improved in whole blood as the blood clotting index decreased from 11.9 to 6.5, and the clotting time was approximately 6 min. The 5% w/v group provided the optimal balance of clotting, antibacterial effects, and biocompatibility. This study presents a novel hemostatic CS/GEL cryogel containing V. thapsus leaf extract that holds strong potential for future applications in NCH management.
Citation - WoS: 2
Citation - Scopus: 2
Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems
(Wiley, 2025) Hacilar, Hilal; Dedeturk, Bilge Kagan; Ozmen, Mihrimah; Celik, Mehlika Eraslan; Gungor, Vehbi Cagri
Metaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.
Accurate Prediction of Residual Stresses in Machining of Inconel 718 Alloy through Crystal Plasticity Modelling
(2023) Bal, Burak; Cetın, Barıs; Yılmaz, Okan Deniz; Kesriklioglu, Sinan; Kapçı, Mehmet Fazıl; Buyukcapar, Ridvan
Artık gerilmelerin belirlenmesi ve değerlendirilmesi, savunma, havacılık ve otomotiv endüstrilerinde kullanılan bileşenlerin arızalanmasını önlemede çok önemlidir. Bu çalışmanın amacı, Inconel 718'in işlenmesi sırasında oluşan artık gerilmeleri doğru bir şekilde tahmin etmek için bir malzeme modeli sunmaktır. Ortogonal talaşlı imalat testleri, çeşitli kesme ve ilerleme hızlarında gerçekleştirilerek, Inconel 718'in işlenmesinden sonraki artık gerilmeler, X-Ray ışın kırınımı ile karakterize edildi. Bu süper alaşımın mikroyapısal girdilerini ticari olarak temin edilebilen bir sonlu eleman yazılımına (Deform 2D) aktarmak için bir viskoplastik kendi içinde tutarlı kristal plastisite modeli geliştirildi. Ayrıca simülasyonlar klasik Johnson - Cook malzeme modeli ile aynı işleme parametrelerinde yapıldı. Bu çalışmada elde edilen simülasyon ve deneysel sonuçlar, kristal plastisite tabanlı çok ölçekli ve çok ölçekli malzeme modelinin, mevcut modele kıyasla Inconel 718'in işleme kaynaklı kalıntı gerilmelerinin tahmin doğruluğunu önemli ölçüde geliştirdiğini ve yüzey kusurlarını en aza indirmek için kullanılabileceğini göstermiştir. Geliştirilen bu model, kesilmesi zor malzemelerin işlenmesinde yüzey kusurlarını ve üretim denemelerinin maliyetini en aza indirmek için kullanılabilir.
Achieving Extreme Solubility and Green Solvent-Processed Organic Field-Effect Transistors: A Viable Asymmetric Functionalization of [1]Benzothieno[3,2-B][1]Benzothiophenes
(American Chemical Society, 2025) Yıldız, T.A.; Deneme, İ.; Usta, H.
Novel structural engineering strategies for solubilizing high-mobility semiconductors are critical, which enables green solvent processing for eco-friendly, sustainable device fabrication, and unique molecular properties. Here, we introduce a viable asymmetric functionalization approach, synthesizing monocarbonyl [1]benzothieno[3,2-b][1]benzothiophene molecules on a gram scale in two transition-metal-free steps. An unprecedented solubility of up to 176.0 mg·mL–1(at room temperature) is achieved, which is the highest reported to date for a high-performance organic semiconductor. The single-crystal structural analysis reveals a herringbone motif with multiple edge-to-face interactions and nonclassical hydrogen bonds involving the carbonyl unit. The asymmetric backbones adopt an antiparallel arrangement, enabling face-to-face π-π interactions. The mono(alkyl-aryl)carbonyl-BTBT compound, m-C6PhCO-BTBT enables formulations in varied green solvents, including acetone and ethanol, all achieving p-channel top-contact/bottom-gate OFETs in ambient conditions. Charge carrier mobilities of up to 1.87 cm2/V·s (μeff≈ 0.4 cm2/V·s; Ion/Ioff≈ 107–108) were achieved. To the best of our knowledge, this is one of the highest OFET performances achieved using a green solvent. Hansen solubility parameters (HSP) analysis, combined with Scatchard–Hildebrand regular solution theory and single-crystal packing analysis, elucidates this exceptional solubility and reveals unique relationships between molecular structure, interaction energy densities, cohesive energetics, and solute–solvent distances (Ra). An optimal solute–green solvent interaction distance in HSP space proves critical for green solvent-processed thin-film properties. This asymmetric functionalization approach, with demonstrated unique solubility insights, provides a foundation for designing green solvent-processable π-conjugated systems, potentially advancing innovation in sustainable (opto)electronics and bioelectronics. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
Achieving High Optical Absorption in Thin Film Photovoltaic Devices via Nanopillar Arrays and Metal Nanoparticles
(Wiley-VCH Verlag GmbH, 2025) Tut, Turgut
In this study, crystalline silicon nanopillars has been employed as a hexagonal array photonic crystal structure with low optical reflection, augmented by silver metallic nanoparticles ranging from 10 to 50 nm in diameter in order to achieve high absorption in thin silicon films, a critical factor for applications in photovoltaic devices. Initially, it has been begun with an optimized structure in terms of pillar filling ratio, pillar height, and diameter, as established in the previous study. This allows to obtain a hexagonal array of nanopillars with a surface characterized by low optical reflection. To enhance the optical absorption within the bulk of the silicon thin film, the optical scattering properties of silver (Ag) metallic nanoparticles (MNPs) has been harnessed. The integration of silver metal nanoparticles into the photonic crystal hexagonal nanopillar array involved introducing a cavity into the silicon pillar. Placing Ag MNPs near the bottom of the cavity prevented the degradation of the photonic crystal's ability to maintain low reflection within the desired optical spectrum (between 400-1100 nm). Comparison between the nanopillar hexagonal array structure with Ag MNPs and the bare silicon substrate revealed a remarkable 104.76 percent increase in optical absorption for a 1-micron thick silicon bulk material. This triple hybrid structure exhibits tremendous potential in photovoltaic device applications, including solar cells and photodetectors, with the capacity to significantly enhance conversion efficiency.
Adaptation of Guar Bean (Cyamopsis Tetragonoloba L.Taub.) to Different Regions of Turkey for Grain Yield and Gum Traits
(2020) Erol, Elif; Akan, Kadir; Kökten, Kağan; Akçura, Mevlüt; Kahraman, Kevser; Kara, Burhan; Kara, Rukiye
Proje ile ülkemizin farklı çevre koşullarında tane verimi ve sakız içeriği yönünden uygun olan sakız fasulyesi genotiplerinin geliştirilmesi, sakız fasulyesi için en uygun çevrelerin tespit edilmesi, elde edilen sakızın teknolojik özelliklerinin belirlenmesi ve sakız alındıktan sonra kalan posanın yem özelliklerinin incelenmesi hedeflenmiştir. Materyal olarak Hindistan ve Pakistan?dan temin edilen popülasyonlar içerisinden Çanakkale sulu koşullarında 2011?2015 yılları arasında teksel seleksiyon ile seçilen saf hatlardan olumlu özellikleri (Çanakkale koşullarına tane verimi, olgunlaşma süresi, hastalıklara dayanıklılık vb. yönünden uyum sağlayan) taşıyan 86 adet hat ile Hindistan?da tescil ettirilmiş 4 çeşit kullanılmıştır. Projenin birinci yılında, Çanakkale (2 set), Bandırma (2 set), Burhaniye (2 set), İzmir (Bayındır), Kahramanmaraş (Merkez), ve Bingöl (Merkez) lokasyonlarında standart çeşitler ile hatları toplam 9 çevrede 41 özellik ( tane verimi, tohum, fenolojik, sakız ve yem) yönünden karşılaştırmak amacıyla dikdörtgen latis (9 x 10) deneme desenine göre 3 tekerrürlü denemeler kurulmuştur. Bu faaliyetlerin sonucuna göre tane ve sakız verimi ile sakız özellikleri yönünden en yüksek değerlere sahip olan 25 adet hat belirlenmiştir İkinci ve üçüncü yıllarda ise belirlenen 25 adet hat ve 4 standart çeşit ile aynı lokasyonlara ilave olarak Isparta (Merkez) lokasyonunda tesadüf blokları deneme desenine göre üç tekerrürlü olarak toplam 14 adet deneme (2 yıl 7 lokasyon) kurulmuştur. Denemelerde tane verimi, tohum, fenolojik, sakız ve yem özelliklerinden oluşan toplam 41 adet özellik incelenmiştir. Elde edilen sonuçların değerlendirilmesinde farklı stabilite parametreleri ile GGE-biplot yöntemi kullanılmıştır. Bu yöntemler ile deneme kurulan çevreler arasında yıllara değişmekle birlikte en uygun çevrelerin sırasıyla, Kahramanmaraş, İzmir, Çanakkale ve Burhaniye çevrelerinin olduğu, Bingöl ve Isparta çevrelerinin ise uygun olmadığı, hatlar arasında ise en iyilerin 23, 12, 13 ve 16 nolu hatların olduğu belirlenmiştir. En iyi olan hatlardan bir tanesinin ülkemizin ilk sakız fasulyesi çeşitleri olarak tescillenmesi için Tohumluk Tescil ve Sertifikasyon Merkezi?ne müracaatı için hazırlıklar devam etmektedir.
Citation - WoS: 15
Citation - Scopus: 20
Adaptive Fault Detection Scheme Using an Optimized Self-Healing Ensemble Machine Learning Algorithm
(China Electric Power Research inst, 2022) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Li, Xiangjun; Muyeen, S. M.
This paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.
Citation - Scopus: 9
Adjustment Speed of Debt Maturity: Evidence From Financial Crises in East Asia
(Bank Indonesia Institute, 2021) Tekin, Hasan; Polat, Ali Yavuz
We investigate the change in adjustment speed of debt maturity for East Asian firms between 1990 and 2017 by including two exogenous shocks: the Asian Financial Crisis 1997-1998 (AFC) and the Global Financial Crisis 2007-2009 (GFC). We employ the least square dummy variable correction and find that East Asian firms have a slower adjustment of long-term debt over time. Besides, the decrease in adjustment speed of long-term debt after the GFC is more compared to the decrease after the AFC. Further analysis shows the optimal debt maturity differs across countries and industries. Another important implication of our results is that firms in high governance countries are more likely to close the gap between the actual and target debt maturity in time. Overall, debt holders and investors should consider financial uncertainties. © 2025 Elsevier B.V., All rights reserved.
Advances in Natural Building and Construction Materials
(MDPI, 2025) Strzalkowski, Pawel; Sousa, Luis; Koken, Ekin; Strzałkowski, Paweł
Aerodynamic Optimization of the Cessna 172 Propeller via Computational Fluid Dynamics
(2025) Temirel, Mikail; Gördebil, Mehmet Ali
The term “Light Aircraft” refers to aircraft weighing less than 5.5 tons. The term light aircraft refers to aircraft weighing less than 5.5 tons. These aircraft formed the backbone of air forces during both world wars, primarily powered by propellers, and continue to be widely used today for flight training, travel, recreation, and personal use. Cessna, a leading light aircraft manufacturer, introduced the Cessna 172 in 1956—a model that remains in production. However, its limited speed and altitude performance fall short of modern aviation requirements. To address these limitations, a computational fluid dynamics (CFD) study was conducted using Ansys Fluent and CFX to enhance the aircraft’s aerodynamic efficiency and speed. Design modifications were applied to the propeller, with a focus on improving thrust generation. The modified configuration produced a significant improvement: CFD results indicated an approximately 50% increase in thrust, corresponding to a 21% increase in the maximum velocity of the aircraft. This study highlights the potential of CFD tools to optimize classic aircraft designs such as the Cessna 172, providing practical insights for the modernization of light aircraft in both civilian and military applications.
Citation - WoS: 23
Citation - Scopus: 23
The Age Structure, Stringency Policy, Income, and Spread of Coronavirus Disease 2019: Evidence From 209 Countries
(Frontiers Media S.A., 2021) Bilgili, Faik; Dundar, Munis; Kuskaya, Sevda; Lorente, Daniel Balsalobre; Unlu, Fatma; Gencoglu, Pelin; Mugaloglu, Erhan
This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.
Agency Theory: A Review in Finance
(2020) Polat, Ali Yavuz; Tekin, Hasan
Temsil ve risk paylaşımı problemleri büyük firmalarda sahiplik ve kontrolün ayrılmış olmasından dolayı, müvekkil (sahipler) ve vekil (yöneticiler) arasında çıkar çatışması olduğunda ortaya çıkmaktadır. Bu problemler temel olarak bilgi asimetrisinden kaynaklanmaktadır. Bu da müvekkil için vekalet maliyeti ortaya çıkarmaktadır. Halihazırdaki önemli teorilerden biri olan Vekil Teorisi vekalet ilişkilerindeki maliyetleri minimize etmeye odaklanmaktadır. Bu çalışma müvekkil-vekil ilişkilerini daha iyi anlamak için, kurumsal finans alanındaki hissedar-yönetici ve tahvil sahibi-hissedar ilişkilerine odaklanarak, müvekkil-vekil ilişkilerini kritik bir şekilde değerlendirmektedir.
Citation - Scopus: 4
Aguhyper: a Hyperledger-Based Electronic Health Record Management Framework
(PeerJ Inc., 2024) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu
The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off-chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper’s decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identified in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper’s implementation configurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes significantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the field. © 2024 Elsevier B.V., All rights reserved.
Aguhyper: A Hyperledger-Based Electronic Health Record Management Framework
(PeerJ Inc, 2024) Dedeturk, Beyhan Adanur; Bakir-Gungor, Burcu
The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off -chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper ' s decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identi fi ed in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper ' s implementation con fi gurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes signi fi cantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the fi eld.
Citation - WoS: 116
Citation - Scopus: 201
AI-Based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions
(Elsevier, 2023) Iftikhar, Sundas; Gill, Sukhpal Singh; Song, Chenghao; Xu, Minxian; Aslanpour, Mohammad Sadegh; Toosi, Adel N.; Uhlig, Steve
Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.
AI-Enhanced PV Power Forecasting Using Cloud Thickness and Motion in Kayseri, Türkiye
(Wiley, 2025) Yavuz, Levent; Onen, Ahmet; Awad, Ahmed; Ahshan, Razzaqul; Al-Badi, Abdullah
The incorporation of renewable energy in photovoltaic (PV) systems has made significant progress. The inherent intermittency nature of PV generation, nevertheless poses an obstacle to accurate energy forecasting. Historical PV production plus meteorological data such as temperature, humidity, and atmospheric pressure are largely utilized in present methods of forecasting. However, cloud thickness and dynamics-integrated system, has not been investigated and tested in real-world examples yet.This research seeks to fill this gap in research through the development of a new AI-based PV forecasting model that incorporates cloud thickness, cloud motion, and solar position into the forecasting model. Cloud properties and their impact on solar radiation are computed through a deep learning-based panel-shadowing model. For cloud movement forecasting, a gated recurrent unit (GRU) is used, while multiple convolutional neural networks (CNNs) are used for estimating cloud thickness. These outcomes are then integrated with measurements from environmental sensors to improve the accuracy of the predictions.The system was implemented and tested at Abdullah G & uuml;l University and exhibited a remarkable improvement in forecasting accuracy compared to current models. The results prove that cloud motion and thickness improve the accuracy of PV predictions, which is important for energy market stability and power grid operations.

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