Scopus İndeksli Yayınlar Koleksiyonu

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

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Citation - WoS: 10
Citation - Scopus: 10
About Lagrangian Formulation of Classical Fields Within Riemann-Liouville Fractional Derivatives
(American Society of Mechanical Engineers, 2005) Baleanu, D.; Muslih, S.I.
Recently, an extension of the simplest fractional problem and the fractional variational problem of Lagrange was obtained by Agrawal. The first part of this study presents the fractional Lagrangian formulation of mechanical systems and introduce the Levy path integral. The second part is an extension to Agrawal's approach to classical fields with fractional derivatives. The classical fields with fractional derivatives are investigated by using the Lagrangian formulation. The case of the fractional Schrödinger equation is presented. Copyright © 2005 by ASME.
Citation - Scopus: 2
About the Fn Approximation To Fractional Neutron Transport Equation in Slab Geometry
(Amer Soc Mechanical Engineers, 2011) Baleanu, D.; Kadem, A.
The neutron transport denotes the study of the motions and interactions of neutrons with materials. In given applications we need to know where neutrons are in an apparatus, what direction they are moving, and how fast they are going. In this manuscript the Legendre polynomial approximation method F N was applied to the one dimensional slab geometry neutron transport equation. © 2011 by ASME.
Citation - Scopus: 2
Analysis of Delay Factors for Voice Over Wimax
(Ieee, 2008) Safak, A.; Preveze, B.
This paper presents the results of our computer simulation for delays experienced by voice packets over WiMAX 802.16e protocol running over an enterprise packet network connected to public internet. In the simulation we analyzed the effect of packet size, core network link speed, internet service provider link speed, wireless network link speed, wireless distance, base station range, and distance in internet parameters that effect the amount of delay in a WiMAX embedded IP network. In this study, all the delay parameters are fixed to their default values and current parameter values are changed so that the delay is varied between minimum and maximum values in uniformly distributed 100 steps and the delay is plotted in each step. Total fixed delays can be considered as minimum delay and variable delay corresponds to the maximum delay that can occur under the given conditions. All other fixed and variable delay parameters such as switching delays, packetization delays, contention delays and the delays caused by buffers etc. are also taken into account. The results indicate that among these delays WiMAX base station range and the number of base stations play the most decisive role. © 2008 IEEE.
Application of Various Modelling Techniques Into Consumer Confidence Index
(CRC Press, 2025) Kalayci, B.; Purutçuoğlu, V.; Weber, G.W.; Uğur, Ö.; Defterli, Ö.
The principles of sentiment are presented by economic conditions such that most of the variance in consumer sentiment is caused by economic situations, either directly or indirectly. There are political and economic repercussions to investors’ economic optimism or pessimism. Customer expenditures and hence, the direction of the economy’s future are predicted by the degree of consumer confidence. Investors, who are in a similar way, are positioned economically, but, have different biased thoughts bringing forward quite different sentiments about the future of economics. © 2025 CRC Press.
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.
Citation - Scopus: 3
Autoregressive Models With Stochastic Design Variables and Nonnormal Innovations
(2011) Bayrak, O.T.; Akkaya, A.D.
In autoregression models the design variable has traditionally been assumed to be non-stochastic and innovations are normal. In most real life situations, however, the design variable is stochastic having a non-normal distribution as the innovations. Modified maximum likelihood method is utilized to estimate unknown parameters in such situations. Closed form estimators are obtained and shown to be efficient and robust.
AviBERT: Transformer Tabanlı Hava Aracı Metni Sınıflandırma
(Institute of Electrical and Electronics Engineers Inc., 2025) Unal, Muhammed Cihat; Yurtalan, Gokhan; Karatas, Yahya Bahadir; Karamanlioglu, Alper; Demirel, Berkan
In recent years, transformer-based models pre-trained on extensive corpora have played a critical role in the advancement of Natural Language Processing methodologies. Particularly, methods based on BERT have demonstrated remarkable performance across various tasks by offering robust capabilities in deeply understanding texts semantically. However, despite these advancements, there is a notable scarcity of studies applying these technologies in the aviation sector. This paper develops a multi-class classification model for aviation-specific texts using variants of BERT. The study encompasses the processes of collecting web content related to aircraft, labeling and model training. The details of the dataset are explained and the outcomes of the study are assessed based on the macro F1-score and accuracy of different models. © 2025 Elsevier B.V., All rights reserved.
Beyond the Bully: Exploring the Surprising Performance-Related Outcomes of Abusive Supervision
(Nova Science Publishers, Inc., 2024) Tokat, T.; Çivit, S.; Göncü-Köse, A.
The common definitions of leadership include positive qualities such as vision, ethics, responsibility, respect, and trustworthiness. However, recent studies revealed an increase in unethical behaviors performed by organizational leaders, prompting academics to investigate the harmful types and impacts of leadership. One area of focus has been abusive supervision, which was found to have both positive and negative effects on organizational outcomes including subordinate and supervisor performance. While the harmful effects on subordinates have been a major focus in studies on abusive supervision, some scholars suggested that supervisors’ abusive behaviors may yield temporary functional results and be used as a strategy for leaders to improve work performance. To illustrate, researchers demonstrated the positive association between abusive supervision and work performance when subordinates attribute abusive behaviors to an effort to increase motivation rather than an intention to harm the employee (Li et al., 2022; Liao et al., 2021; Tepper et al., 2015). However, there is limited knowledge about these destructive behaviors’ impact on supervisors. One of these few studies showed that engaging in abusive supervisory behavior was positively correlated with supervisors’ recovery levels. Moreover, abusive supervisory behavior indirectly contributed to increased work engagement through its positive effect on the recovery level (Qin et al., 2018). The present chapter provides a comprehensive overview of the effects of abusive supervision on work performance, outlining both the negative and positive work-, employee-, and supervisor-related outcomes. Additionally, this chapter unfolds potential directions for future research regarding the relationships of abusive supervision with in-role and extra-role performance. Finally, we present practical recommendations for leaders and organizations to minimize the detrimental effects of abusive supervision. The literature findings will be presented in conjunction with various theoretical explanations, concepts, and propositions. © 2024 by Nova Science Publishers, Inc.
Board Structure and Sustainability Performance: a Study on Turkish Manufacturing Companies
(IGI Global, 2025) Aksu, A.; Şener, İ.; Kadir Varoğlu, A.
The adoption of the innovative perspective of Industry 5.0 at the highest level in organizations is important for ensuring sustainability in every field and the board of directors’ role becomes increasingly important in improving companies' sustainability performance. This chapter first presents the theoretical and conceptual background of Industry 5.0 and sustainability, the role of boards of directors in sustainability, and corporate sustainability reporting. Then, the findings of an empirical study that examine the impact of board structure on companies' sustainability performance are presented. The study aims to contribute by revealing which characteristics of companies’ boards of directors positively impact sustainability performances of the companies. The data from 166 manufacturing firms listed on Borsa Istanbul have been used in the research. Findings suggest that both the size of the board and the presence of women on the board have a positive impact on sustainability performance. © 2025 by IGI Global Scientific Publishing. All rights reserved.
Citation - Scopus: 1
The Causal Relationship Between Esg and Economic Growth: Evidence From the Panel of Commonwealth Independent States
(Springer International Publishing, 2024) Baskaraagac, N.Y.
This paper analyzes the causal relationship between ESG and economic growth for CIS member states over the sample period 1996–2020. Since the results of the varied panel stationarity tests suggest mixed findings on the order of integration, the ARDL model is employed to determine the co-integration relation among the series. The result of the VEC model, which is estimated to explore the long-run and short-run dynamics of this casual relation between the series in a basic way, suggests that there is a causal relationship running from income level to ESG criteria. This implies that the adaptation of macroeconomic policies which will stimulate the economic growth process in CIS countries may also encourage the ESG indicator levels of the economy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Controller Synthesis With a Cone Complementarity Linearization Algorithm for CACC System Under Time-Varying Delay
(IEEE, 2025) Bingol, Hilal
Cooperative Adaptive Cruise Control (CACC) is an intelligent vehicle technology that enables vehicle follow-up in a small inter-vehicle distance. Stability is the main property of the CACC system that prevents any signal fluctuations throughout the vehicle string. In CACC system, time delay has a negative impact on string stability. The reason is that constant and varying time delays are unavoidable in real traffic. Here, controller gains should be synthesized under constant and time-varying communication delays to satisfy L-2-string stability conditions (in the Lyapunov sense). Contrary to previously studied convex method, controller gains are now synthesized with iterative nonlinear minimization algorithm and Cone Complementarity Linearization (CCL) method. The results show that the new CCL approach provides more accurate and practical stability bounds. Hence, this shows the potential of CCL to perform more sensitive analyses. The results obtained are evaluated by simulations with the heterogeneous CACC system.
Crack Detection on Asphalt Runway Using Unmanned Aerial Vehicle Data With Non-Crack Object Removal and Deep Learning Methods
(Pontificia Universidad Catolica de Chile, Escuela de Construccion Civil, 2025) Tapkin, S.; Tercan, E.; Bostan, A.; Şengül, G.
Unmanned aerial vehicles are extensively utilized for image acquisition in a cheap, fast, and effective way. In this study, an automatic crack detection method with non-crack object removal and deep learning-based approaches are developed and tested on images captured by unmanned aerial vehicle. The motivation of this study is to detect either a crack exists or not in the asphalt-runway. The novelty of this study lies in integrating a non-crack artifact removal process with six classical edge detectors and comparing the resulting performance with four lightweight CNN models on the same UAV-acquired runway image dataset, enabling a unified evaluation of classical and learning-based approaches. For deep learning-based approach, four lightweight CNN models, namely GoogleNet, SqueezeNet, MobileNetv2, and ShuffleNet, are trained and the best accuracy of 87.9 is obtained whenever GoogleNet model is used. For the non-crack object removal approach, exclusion of non-crack objects from the images is the first step, where crack-detection which makes use of edge-detection techniques is the latter. In the study, Sobel, Prewitt, Canny, Laplacian of Gaussian, Roberts and Zero Cross edge detection algorithms are examined and their success rates in detecting cracks are comparatively presented. With sensitivity=0.981, specificity=0.744, accuracy=0.917, precision=0.912 and F-score=0.945 values Canny algorithm performs significantly better than others in detecting the cracks. This study provides enough evidence for the practicability of automated crack detection on unprocessed digital photographs by the results of the study conducted on asphalt runway. © (c) 2025 Tapkın, S., Tercan, E., Bostan, A. and Şengül, G. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/
Derivatives of Eigenvalues of Third-Order Problems
(Nova Science Publishers, Inc., 2020) Uğurlu, E.
In this chapter, we aim to introduce some properties of the eigenvalues of some third-order boundary-value-transmission problems generated by the following differential expression where the coefficients are real-valued and Lebesgue measurable functions on a given interval. In particular, we aim to show that the eigenvalues of the problems are differentiable with respect to some elements of data. Moreover, we will share some ordinary and Frechet derivatives of the eigenvalues with respect to some elements of data. Note that the results are new in the literature. However, the readers may find some results for the special case s = 0 in the literature. © 2020 Nova Science Publishers, Inc.
Developing Growing Hierarchical Structures for Decision Making
(Ieee, 2007) Beldek, U.; Leblebicioglu, K.
This study is about developing a hierarchical approach for decision-making problems. The development is done on a representative decision-making problem. A hierarchical decision making approach which enables fusion of decisions of previous and current levels is proposed. The agents that determine the decisions at different hierarchy levels is accomplished by utilizing a genetic algorithm.
A Discrete Element Method for Evaluating the Seismic Performance of Concrete Gravity Dam-Reservoir Systems Under Main Shock-Aftershock Events
(Tulpar Academic Publishing, 2025) Soysal, B.F.
Dams are crucial for water supply, flood prevention, and hydroelectric power generation. Often located in seismically active regions, they are vulnerable to main shock-aftershock (MS-AS) sequences, which can compromise structural integrity and hydraulic safety. Critical aspects of dam response to MS–AS events remain unclear, particularly the required rest time between successive events and threshold AS-to-MS intensity measure ratios that could serve as predictors of additional damage. This study addresses these gaps by analyzing concrete gravity dam–reservoir systems of three heights (50 m, 100 m, and 150 m) using the developed discrete element–based approach coupled with displacement/pressure-based mixed finite elements for the reservoir. Empirical rest time equations were derived from 124 as-recorded ground motions, while seismic performance under varying intensity levels was evaluated using 14 as-recorded MS–AS sequences. Damage was quantified using discrete indices of base crack length, maximum base crack width, and maximum total upstream crack width. Results indicate that AS primarily propagate existing cracks at lower intensities, whereas higher intensities generate new cracks along the upstream face, increasing crack widths by 25–30% on average. The 50 m high dam remained within the mild damage category, while taller dams occasionally reached moderate levels, posing potential seepage risks. Threshold AS-to-MS ratios for four different intensity measures were identified. These findings provide mechanistic insight into crack propagation under MS-AS events, providing practical guidance for post-earthquake dam safety assessment, inspection prioritization, and incorporating sequential seismic effects into design and emergency planning. © 2025 by the Author.
Electro- Acoustic Device for Hip Dysplasia Assessment
(SYSCOM 18 S.R.L., 2016) Oliva, H.P.; Fraga, T.C.; Raygoza, N.P.; Gómez-Aguilar, J.F.; Baleanu, D.; Aquino, M.S.; Cabrera, R.G.; Sosa Aquino, Modesto; Cordova Fraga, Teodoro; Padilla Raygoza, Nicolas; Gomez Aguilar, Jose Francisco; Perez Oliva, Huetzin; Guzman Cabrera, Rafael
A device for making diagnosis of dysplasia at the development fracture in newborns, assessment of osteoporosis and injuries of the skeletal system is presented. Its functioning is based on generation of acoustic resonance by sound transmitted through the bone under study. The device operates with a transmitter and an acoustic receiver coupled to the surface, just above the bone area under study. The measurements at the femoral bone in newborns indicate that the dominant frequency is around 160 Hz, which is consistent with other studies. Data comparisons with ultrasound technique suggest that this device could be an alternative for both dysplasia's studies of the hipbone and estimations of bone density.
Citation - WoS: 1
Citation - Scopus: 1
Empathy Development in Digital Accessibility Through Real-Life Practices in a Programming Course: a Case Study
(Assoc Computing Machinery, 2024) Inal, Yavuz; Cagiltay, Nergiz
This case study adopted a project-based learning approach to a programming course based on real-life practices to help software engineering students develop empathy skills regarding digital accessibility. A project was assigned to first-year students to develop software for people with disabilities. The data were collected from each individual project of thirty-three students over four months. Students' efforts regarding analysis, design and development steps, and project outcomes were analyzed. The study results showed that students' experience level and knowledge about the accessibility domain were quite low initially. Regarding the target disability type in their projects, half of the students selected mental illness, followed by blindness, deafness, and physical illness. The students who gathered requirements from domain experts or target users made their products more accessible, indicating the importance of user involvement in empathy building in the development process. We also measured increased awareness of and knowledge about accessibility at the end of the course, leading us to discuss the effectiveness of real-life practices in teaching digital accessibility in programming courses.
Citation - Scopus: 2
Enhanced Task Scheduling in Iaas Cloud Environments Using Elitism-Based Genetic Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2024) Osama, M.; Sultan Mohiuddin Qadri, S.S.; Shams Malick, R.A.; Shahid, M.F.; Dawood, K.
Cloud computing (CC) is a modern commercial model that enables customers to acquire large amounts of virtual resources on demand. Among the various service models in CC, Infrastructure as a Service (IaaS) provides Virtual Machines (VMs) and data centers. Efficient task scheduling, which maps cloud tasks to VMs, is key to optimizing data center performance and reducing energy consumption. Given the heterogeneous nature and computational intensity of these tasks, meta-heuristic methods are often employed for scheduling. This research proposes an enhanced Genetic Algorithm (GA) that integrates an Elitism-Based strategy with Conditional Parameter Tuning to improve convergence speed and solution quality. The elitism approach preserves top-performing solutions across generations, while conditional parameter tuning dynamically adjusts algorithm parameters based on population diversity and fitness levels. Experimental evaluations on Amazon EC2 show that the proposed method significantly outperforms traditional approaches in task completion time, resource utilization, and convergence efficiency. The results demonstrate the effectiveness of combining elitism with adaptive strategies to create a scalable, robust solution for task scheduling in high-demand cloud environments. © 2024 IEEE.
Citation - Scopus: 1
Enterprise Architecture for Personalization of E-Government Services: Reflections From Turkey
(IGI Global, 2012) Erdem, A.; Medeni, İ.T.; Medeni, T.D.
As there has not yet been enough work on enterprise architectures for fully integrated knowledge-based, highly-sophisticated (citizen-oriented) personalized services, this chapter aims to articulate a perspective to design architectures for the development and provision of sophisticated, personalized services. Doing so, the authors benefit from their knowledge and experience in the Turkish e-Government Gateway (eGG) and general e-Government services development and provision. First providing an introduction and background information, the chapter discusses the development of eGG services in Turkey, and then provides a visionary suggestion for knowledge-based personalized, citizen-centric e-Government. Among the suggested perspectives, an E-Citizen Decision Support System, and Entity-Utility and Information Flow Model could be useful for eGG development in Turkey and elsewhere. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 5
Citation - Scopus: 6
Err@hri 2024 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Interactions
(Assoc Computing Machinery, 2024) Spitale, Micol; Parreira, Maria Teresa; Stiber, Maia; Axelsson, Minja; Kara, Neval; Kankariyat, Garima; Gunes, Hatice; Kankariya, Garima
Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions, encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors, to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.

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