Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/1248

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Citation Count: 0
Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
(St. Petersburg Federal Research Center of the Russian Academy of Sciences, 2025) Imamverdiyev, Y.; Baghirov, E.; Chukwu, I.J.
In the internet and smart devices era, malware detection has become crucial for system security. Obfuscated malware poses significant risks to various platforms, including computers, mobile devices, and IoT devices, by evading advanced security solutions. Traditional heuristic-based and signature-based methods often fail against these threats. Therefore, a cost-effective detection system was proposed using memory dump analysis and ensemble learning techniques. Utilizing the CIC-MalMem-2022 dataset, the effectiveness of decision trees, gradient-boosted trees, logistic Regression, random forest, and LightGBM in identifying obfuscated malware was evaluated. The study demonstrated the superiority of ensemble learning techniques in enhancing detection accuracy and robustness. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to elucidate model predictions, improving transparency and trustworthiness. The analysis revealed vital features significantly impacting malware detection, such as process services, active services, file handles, registry keys, and callback functions. These insights are crucial for refining detection strategies and enhancing model performance. The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. This paper offers valuable insights for future research and advancements in malware detection, paving the way for more robust and effective cybersecurity solutions in the face of evolving and sophisticated malware threats. © 2025 St. Petersburg Federal Research Center of the Russian Academy of Sciences. All rights reserved.
Citation Count: 0
War Disability: Complications and Possibilities for Peacebuilding Processes
(Taylor and Francis, 2025) Sünbüloğlu, N.Y.
[No abstract available]
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Intangible Heritage – Bridging Tangible and Intangible Heritage Through Placemaking: Senses of Belonging and Identification With Place
(Brill, 2025) Erek, A.; Sepe, M.; Székely, J.
During recent years, there have been several scholarly works that – instead of viewing tangible and intangible heritage as entirely separate entities – hint at an approach that not only acknowledges the intimate ties between the two, but also stresses their unambiguous embeddedness in social, political, cultural, and even psychological contexts. Corresponding to the interpretation of heritage as a “verb”, this chapter will also frame heritage as a complex and dynamic process connected to practices of placemaking that – as we will argue – further stresses the interrelatedness of tangible and intangible heritage. Starting from these premises, this chapter aims to illustrate different approaches in three different cities, which mutually enhance in/tangible heritage and placemaking: through our case studies of (1) the Bosphorus in Istanbul, Turkey, (2) the Machine of Santa Rosa in Viterbo, Italy and (3) the Bloomsday Festival in Szombathely, Hungary, we will investigate (1) narratives and stories, (2) traditions and rituals as well as (3) performances. While our cases showcase different stages in the processes of heritagization significantly differing through the dominance of top-down or bottom-up strategies, they will also underline our interpretation of heritage as a living system. Our cases not only illustrate how heritage can be a resource that connects people and places and how it can contribute to local identity and the sense of belonging, but they also shed light on the potential conflicts embedded in the processes that the linkages between placemaking and heritage can reveal in specific sociocultural contexts. The interrelatedness of tangible and intangible heritage is explored, highlighting the role of placemaking in shaping heritage and its socio-spatial practices. © 2025 by Kadir Has Üniversitesi.
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Integrating Computational and Experimental Insights Into Osmolyte-Driven Activation of Geobacillus Kaustophilus L-Asparaginase for Acrylamide Mitigation
(Elsevier B.V., 2025) Özdemir, F.İ.; Servili, B.; Demirtaş, Ö.; Şükür, G.; Tülek, A.; Yildirim, D.
Osmolytes play a critical role in enhancing the stability and activity of enzymes for industrial applications. This study systematically investigated the effects of various osmolytes on the activity, optimal pH, temperature, stability, metal ion effects, storage, and acrylamide mitigation performance of L-asparaginase from the thermophilic Geobacillus kaustophilus (GkASNase). The experimental findings were further supported by computationally integrated tools such as homology modeling, docking, and molecular dynamics (MD) simulations. Among the selected osmolytes (maltose, sorbitol, trehalose, glycine, and sucrose), GkASNase showed the highest stability during 30 days of storage in the presence of maltose and arginine. Maltose increased GkASNase activity approximately 2-fold at 37 °C and 55 °C. In the presence of osmolytes, the Km values of GkASNase decreased and the Vmax values increased compared to controls at 37 °C and 55 °C. In the presence of osmolytes, the acrylamide mitigation performance of GkASNase increased by 1.7-fold in a 15 min reaction. The computational analysis indicates that L-asparagine as substrate enhances protein compactness and stability, while arginine as osmolyte increases flexibility and optimizes water distribution around the enzyme. These findings provide novel insights into enzyme stabilization that have implications for therapeutic and biotechnological applications. © 2025 Elsevier B.V.
Citation Count: 0
Comparison of Feature Selection Methods for Mechanical Properties of Cold Rolled Products in Flat Steel Manufacturing
(Institute of Electrical and Electronics Engineers Inc., 2024) Ilme, D.B.; Öper, M.; Yetkin, E.F.
The mechanical properties of steel are critical for ensuring its quality and are traditionally tested using destructive methods, which involve cutting test samples after the skin-rolling process. This procedure necessitates the scrapping of the last 8 meters of the coil and extracting a 500 mm wide sample, consuming approximately 1 to 1.5 minutes. To eliminate these additional process steps and minimize material waste, this study aims to predict steel coils' yield strength and tensile strength in the flat steel industry using six machine learning models. The models incorporate 24 distinct production parameters as inputs. The models examined include Linear Regression, Support Vector Regressor (SVR), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and eXtreme Gradient Boosting (XGBoost). To enhance the predictive performance of these models, seven different feature selection methods are employed. These methods systematically rank the production parameters based on their influence and are iteratively utilized within the models to refine their accuracy. The application of these feature selection techniques significantly improves the models' efficiency, leading to substantial operational benefits. The study demonstrates that machine learning models, when optimized with advanced feature selection methods, can accurately predict the mechanical properties of steel, thereby reducing the need for destructive testing. This approach not only conserves material and time but also enhances the overall efficiency of the production process in the flat steel industry. © 2024 IEEE.
Citation Count: 0
Introduction To the Handbook of Tourism and Consumer Behavior
(Edward Elgar Publishing Ltd., 2024) Zheng, D.; Kozak, M.; Wen, J.
Tourism plays a crucial role in human activity and significantly impacts the economy. This introduction explores the historical development of tourism and the evolution of consumer behavior, emphasizing the close connection between tourism and consumer choices. Unlike traditional consumption, tourist behaviors are active and location-focused, influenced by various internal and external factors, prioritizing unique experiences and brand loyalty while reducing post-consumption waste. Through an examination of current global challenges and technological advancements, the chapter anticipates future shifts in tourist behavior, including post-pandemic therapeutic tourism, niche exploration for memorable travel experiences, the transition to tech-savvy ‘smart’ travelers, the impact of social media on travel decision-making, and the rise of responsible travel as a social norm. The chapter concludes by discussing the book’s scope and expressing gratitude for contributions. © The Editors and Contributors Severally 2024.
Citation Count: 0
Resource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based Attacks
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, M.I.; Çukur, Z.; Izgün, M.A.; Ui Ain, N.; Daǧ, H.
The rise of Edge IIoT networks has transformed industries by enabling real-time data processing, but these networks face significant c ybersecurity risks, particularly from OSINT-based attacks. This paper presents a resource-efficient ensemble learning framework designed to detect such attacks in Edge IIoT environments. The framework integrates machine learning models, including RandomForest, K-Nearest Neighbors, and Logistic Regression, optimized with Principal Component Analysis (PCA) to reduce data dimensionality and computational overhead. GridSearchCV and StratifiedKFold cross-validation were employed to fine-tune the models, resulting in high detection accuracy. This approach ensures robust and efficient security for resource-constrained Edge IIoT networks. © 2024 IEEE.
Citation Count: 0
Relationship Between Bilingual Experience And Cognitive Control Of Bilingual Children;
(Dilbilim Dernegi, 2024) Gong, X.; Aktan-Erciyes, A.; Karadöller, D.Z.
There has been growing interest in how different dimensions of bilingual experience relate to cognitive abilities within the bilingual group. However, this interest rarely targeted data from young bilingual children who lack sufficient language production. The current study includes a variety of bilingual experience-related factors, such as language proficiency, language use, and code-switching frequency, to investigate 30-to 48-month-old bilingual children as well as their parents. Results showed that children’s age, rather than any of the bilingual-experience-related variables from neither children nor parents, predicted children’s cognitive control abilities. This study is one of the few that looked at the bilingual effects by including three bilingual-experience-related dimensions as well as parental factors. The implications of applying the bilingual experience dimension-based approach and including environmental factors while studying young bilingual age groups with limited language production were discussed. © 2024, Dilbilim Dernegi. All rights reserved.
Citation Count: 0
Mare Clausum: War and Diplomacy on the Black Sea, 1939 - 91
(De Gruyter, 2024) Işçi, O.
[No abstract available]
Citation Count: 0
Transfer Learning for Phishing Detection: Screenshot-Based Website Classification
(Institute of Electrical and Electronics Engineers Inc., 2024) Çolhak, F.; Ecevit, M.I.; Daǧ, H.
Phishing remains a significant threat in the evolving cybersecurity landscape as phishing websites become increasingly similar to legitimate websites, complicating detection using traditional methods. This study explores AI-based solutions for screenshot-based phishing detection, utilizing the MTLP dataset and applying transfer learning with pretrained models (DenseNet, ResNet, EfficientNet, Inception, MobileNet, VGG) using the timm library. The study also discusses challenges related to phishing datasets and compares publicly available datasets, highlighting MTLP Dataset's strengths. DenseNetBlur121D was identified as the top-performing model, achieving an accuracy of 95.28%, a recall of 95.38%, a precision of 93.42%, and an F1 score of 94.39% when applied to the entire MTLP dataset. Both the model code and dataset are publicly available, providing a valuable resource for further research and development in this domain. © 2024 IEEE.
Citation Count: 0
On Symbolic Prediction of Time Series for Predictive Maintenance Based on Sax-Lstm
(Institute of Electrical and Electronics Engineers Inc., 2024) Güler, A.; Balli, T.; Yetkin, E.F.
This work proposed a new forecasting approach for predictive maintenance in industrial settings, combining standard segmentation approaches like Symbolic Aggregate Approximation (SAX) and Piecewise Aggregate Approximation (PAA) with LSTM (Long-Short Time Memory). The work aims to construct a robust forecasting mechanism to estimate maintenance requirements in advance properly. We first demonstrated the results of the proposed approach for synthetically generated data and extended the results with real industrial vibration data. The algorithm's performance is assessed using real-world industry data from steel production furnaces, where timely maintenance is critical for increasing operating efficiency and reducing downtime. Experimental results show that using SAX and LSTM for forecasting industrial time series data achieves high accuracy rates (90.2 %) in a reasonable computational time. © 2024 IEEE.
Citation Count: 0
Handbook of Tourism and Consumer Behavior
(Edward Elgar Publishing Ltd., 2024) Zheng, D.; Kozak, M.; Wen, J.
This Handbook evaluates cutting-edge research on consumer behavior in the modern day, discussing key areas such as emerging tourism experiences and technology-enabled services. © The Editors and Contributors Severally 2024.
Citation Count: 0
A Robust Microservices Framework for Indoor Tracking System Development
(Institute of Electrical and Electronics Engineers Inc., 2024) Hayytbayev, G.; Küçük, K.; Çavur, M.
The demand for indoor tracking systems is steadily increasing across various applications. While GPS is effective for outdoor localization, indoor localization presents distinct challenges related to hardware, algorithms, architecture, and infrastructure. Many researchers have focused on developing algorithms or hardware solutions to address these challenges. In response, we designed and implemented a robust, innovative framework utilizing microservices to achieve a scalable, fault-tolerant, flexible, and multi-platform indoor localization system. Our system employs RFID hardware for tracking, with data storage managed by a PostgreSQL database. The architecture incorporates RabbitMQ and the Spring framework, utilizing the Java programming language. The proposed framework was tested using a graphical user interface (GUI) within a metallic underground mine, demonstrating scalability by successfully deploying 7 and 22 RFID readers. The system supports development across various platforms, including web, desktop, and mobile, and is compatible with Mac, Linux, and Windows operating systems. The tracking accuracy was measured at 5.12 meters within a 300-meter metallic mining gallery. Overall, the microservices-based framework proved highly suitable for indoor tracking systems. © 2024 IEEE.
Citation Count: 0
A Machine Learning Approach To Steel Sheet Production Surface Quality
(Institute of Electrical and Electronics Engineers Inc., 2024) Öztürk, A.; Aydin, M.N.
This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE.
Citation Count: 0
Automatic Segmentation of Time Series Data With Pelt Algorithm for Predictive Maintenance in the Flat Steel Industry
(Institute of Electrical and Electronics Engineers Inc., 2024) Kaçar, S.; Balli, T.; Yetkin, E.F.
In this study, we aim to test the usability of Change Point Detection (CPD) algorithms (specifically the Pruned Exact Linear Time-PELT) to facilitate the utilization of large volumes of data within predictive mechanisms in the industry. We proposed an efficient CPD parameter selection mechanism for defect diagnosis using time-series vibration data from critical assets. We emphasized the practical algorithm PELT to ensure broad industrial applicability. Our experimental analysis, using synthetic and actual vibration data, demonstrated the practical applicability and effectiveness of PELT algorithm for automatic segmentation. The numerical results show the potential of CPD methodologies for improving predictive maintenance operations by providing an automatic segmentation mechanism. This pipeline proposes a way to increase the operational efficiency and scalability of predictive maintenance approaches, enhancing maintenance procedures and ensuring the long-term reliability of industrial systems. © 2024 IEEE.
Citation Count: 0
Critically Queer Yet Politically Affirmative Engagements With Human Rights
(Istanbul University Press, 2024) Güner, R.O.
Considering recent queer engagements with international human rights, this article argues that emerging attempts at queering rights have often resulted in framing queer critique into the normativity of human rights. This article critiques this tendency, suggesting that queer engagement with rights can be critical yet (potentially) affirmative. It shows that queer critique, understood as non-essentialist politics, can contribute to contemporary critical human rights studies and their analyses of identity-producing functions of rights. In this way, the paper engages not only with the subject paradox of the rights discourse but also with queer responses to identity-based rights claims. I argue that queer critiques, shifting the focus from ontology to politics, encourage an affirmative engagement with framings of rights by considering identities as political claims, understanding rights not in ontological terms but as instruments for shifting temporary strategies in practice. The arena of rights, a site where debates about the definitions of human are contested, is a crucial space for deploying non-essentialist politics. In this context, the article refers to queer as a critical method in deploying rights to reduce the disciplinary effects of identities, helping us to free ourselves, our engagements with others, and politics from the eyes of the Normative. © 2024 Istanbul University Press. All rights reserved.
Citation Count: 0
Barriers To Gender-Based Pro-Environmental Travel Behavior
(Edward Elgar Publishing Ltd., 2024) Chalermchaikit, V.; Kozak, M.
This chapter aims to rationally analyze responsible travel behavior from the sustainability and development perspective, indicating barriers and implications toward tourists’ pro-environmental behavior. Based on sustainability, the triple bottom line shows possible ways to move from the previous travel behavior via sustainable behavior, highlighting the ‘Go Green’ concept influencing marketing, communication, and policies. Gender implications become important keys to sustainable behavior patterns via marketing, communication, and policies. Also, the chapter integrates the current practice of the United Nations via sustainable development goals with implementation as a part of travel behavior. Thus, the viewpoints analyze the different marketing, communication, and policy approaches via different dimensions; values, social norms, and travel constraints through sustainable travel behavior. Furthermore, the scope of different gender perceptions is from the lens of tourists via attitudes, behavior, and characteristics. Hence, the chapter conceptualizes gender-based pro-environment and concludes with coherent predictions of pro-environment behavior. © The Editors and Contributors Severally 2024.
Citation Count: 0
Electricity System Resilience: an Integrated Bibliometric and Systematic Literature Review
(Elsevier Sci Ltd, 2025) Bektas, Zeynep; Yilmaz, Dilek
This study presents a pioneering review of electricity system resilience literature through bibliometric analysis and systematic literature review, addressing four original research questions. It examines whether the literature aligns with advancements in electricity systems and identifies gaps in the reviewed research field. Suggestions include improving methodologies and developing specific resilience metrics. By analyzing current literature and offering future directions, this study provides valuable insights for researchers, policymakers, and practitioners to enhance electricity system resilience.
Citation Count: 0
An Innovative Performance Assessment Method for Increasing the Efficiency of Aodv Routing Protocol in Vanets Through Colored Timed Petri Nets
(Wiley, 2025) Heidari, Arash; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari
Routing protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On-Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real-life situations. It is interesting to see how the results of the CTPN-based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T-tests indicate that the modeling results are highly reliable.
Citation Count: 0
Synthetic Data for Non-Intrusive Load Monitoring: a Markov Chain Based Approach
(Institute of Electrical and Electronics Engineers Inc., 2024) Sayilar, B.C.; Mihci, G.; Ceylan, O.
This paper deals with the generation of synthetic data, which plays an important role in the Non-Intrusive Load Monitoring (NILM) problem. We introduce the NILM problem and then explain its crucial role in improving energy efficiency and supporting smart grid functions. The paper explains the stages of the NILM problem, including data acquisition, feature extraction, event detection, and appliance classification. We also explain two methods for generating synthetic data: AMBAL (Appliance Model Based Algorithm for Load monitoring) and SmartSim. Then, we propose a synthetic data generation method based on Markov chains, which is designed to generate labeled data useful for training supervised machine learning models. The proposed method utilizes the probabilistic transitions between different operational states of appliances, and captures the stochastic nature of real-world appliance usage. Thus, the generated synthetic data not only reflects realistic usage patterns, but also contains labels indicating the state of each appliance at a given time. The simulations are then run by generating synthetic data for typical office equipment such as laptops and televisions. The generated data sets provide detailed and accurate usage profiles, which are important for the effective training and validation of NILM algorithms. Since the generated data also includes the labeled data, this method will improve the ability of NILM systems to accurately identify and monitor individual appliances in a complex load environment. © 2024 IEEE.

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