DSpace Arşivi :: Ana Sayfa

İstanbul Gelişim Üniversitesi Kurumsal Açık Erişim Arşivi

DSpace@Gelişim, İstanbul Gelişim Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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From Code to Security: Machine Learning Approaches in Android Vulnerability Detection
(Springer Science and Business Media Deutschland GmbH, 2026) Arıkan, Kaya Emre; Doğan, Sait Melih; Yılmaz, Ercan Nurcan; Gönen, Serkan
In today’s technology-driven society, an increasing number of individuals rely on mobile devices, leading to a surge in the availability of applications. Smartphone users constantly search for apps that meet their needs, resulting in a flood of options in the marketplace. However, there is a growing concern regarding the security of Android applications, as many have shortcomings in addressing critical security aspects. One reason behind this issue often lies in the lack of automated mechanisms during the design and development stages to identify, test, and rectify vulnerabilities in the source code. It is crucial to address these issues proactively rather than relying solely on updates and patches for already published apps. In response to this challenge, researchers have proposed machine learning techniques to enhance application security by detecting vulnerabilities and malicious code within source code. This systematic literature review delves into this domain by examining 85 carefully selected technical studies published between 2017 and 2024. It aims to shed light on the strengths, weaknesses, and practical applicability of these techniques, while also identifying areas for further improvement. Moreover, the growing focus on advanced approaches—such as Large Language Models (LLMs) and Explainable AI (XAI)—indicates a trend toward more transparent and context-aware vulnerability detection. By synthesizing key insights from the current literature, this review enhances our understanding of Android security approaches, identifies promising directions for future research, and ultimately contributes to the advancement of more secure mobile applications through machine learning-based vulnerability detection.
Chirality-Dependent Cutoff Frequency and I–V Characteristics in Graphene Nanoribbon-Based FETs
(Springer, 2026) Alizadeh Arashloo, Banafsheh
Graphene-based transistors are suitable candidates for overcoming the scaling problems of Si-based devices in radio frequency (RF) applications and nanoscale devices. The graphene nanoribbon (GNR) is a one-dimensional member of graphene-based materials which possesses the superior properties of graphene. The crucial demands in device technology, particularly the need for high-speed performance, have led to the selection of GNR field-effect transistor (FETs) as a solution for addressing and overcoming scaling issues. In the present work, the cutoff frequency, time delay, and I–V characteristics of GNR-based FETs are investigated as indispensable parameters for transistor speed, and their impact on the design and implementation of GNR-based FETs is explored. GNRs are employed in the channel region of metal–oxide–semiconductor FETs to numerically and analytically investigate the cutoff frequency and time delay, which are critical for high-speed switching performance. The Y-parameter in unity current gain magnitude (0 dB) within the quasi-static approximation is used in the model. Results show that increased delay time is associated with reduced channel conductance and corresponding decrease in cutoff frequency. Conversely, high-frequency operation is achieved at low drain–source voltage with small delay times and enhanced channel conductance. In addition, the small output conductance enables Early voltage reduction, leading to a significantly improved voltage gain as confirmed by the I–V characteristics. The proposed model demonstrates good agreement with conventional device behavior, validating its accuracy and applicability. Additionally, a comparison of the armchair GNR (AGNR) and zigzag GNR (ZGNR) channels shows that ZGNRs maintain stable current and cutoff frequency with minimal chirality and length effects, while AGNRs exhibit chirality-dependent reductions in current and increased cutoff frequency. This highlights ZGNRs’ stability and AGNRs’ sensitivity for future nanoscale device applications.
The Impact of an 8-Week Training Program on the Sportive Character and Moral Approaches of Wheelchair Tennis Players
(SAGAMORE PUBLISHING LLC, 3611 N. Staley Rd, Suite B, Champaign, IL 61822, UNITED STATES, 2026) Şahinler, Yunus; Ulukan, Mahmut; Atasoy, Taner
Wheelchair tennis is an important sport that enables people with physical disabilities to participate actively in competitive sport. However, research focusing on the sporting character and moral attitudes of wheelchair tennis players remains limited. In this context, with the increasing prevalence of wheelchair tennis, assessing key concepts such as sporting character and morality among wheelchair tennis players is a crucial step in understanding and improving their behaviour. The aim of this study is to investigate the effects of an 8-week training programme on the sporting character and moral attitudes of wheelchair tennis players. In line with this aim, the study aims to highlight the importance of sporting character and moral values displayed by wheelchair tennis players during sporting activities. The research was conducted using a single group pretest-posttest experimental design with no control group. The research group consisted of 23 wheelchair tennis players. Data were collected using a personal information form, the Sport Character Scale and the Sport Morale Scale. The athletes received 60-minute training sessions for 8 weeks and data were collected using pre- and post-test methods. The results of the study show a significant improvement in the sporting character and moral attitudes of wheelchair tennis athletes. Significant differences were found between the athletes’ pre- and post-test scores. Furthermore, regression analysis revealed that sportsmanship, disability type, and disability level significantly predicted the moral attitudes of the athletes. The results of the study suggest that training programmes for wheelchair tennis players have positive effects on their sporting character and moral attitudes. Therefore, it is recommended that similar training programmes aimed at improving the sporting character and moral attitudes of wheelchair tennis players should be widely implemented. Furthermore, it would be beneficial to develop policy recommendations that would encourage the participation of athletes with disabilities in general sports activities.
Machine Learning-Based Prediction of Optimum Design Parameters for Axially Symmetric Cylindrical Reinforced Concrete Walls
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Kayabekir, Aylin Ece
This study presents a hybrid approach integrating metaheuristic optimization and machine learning methods to quickly and reliably estimate the optimum design parameters of dome-shaped axially symmetric cylindrical reinforced concrete (RC) walls. A comprehensive dataset was created using the Jaya algorithm to minimize total material cost for hinged and fixed support conditions. For each optimized design case, total wall height (H), dome height (Hd), dome thickness (hd), and fluid unit weight (γ) were considered as input parameters; optimum wall thickness (hw) and total cost were determined as output parameters. Using the obtained dataset, a total of thirteen different regression-based machine learning algorithms, including linear regression-based models, tree-based ensemble methods, and neural network models, were trained and tested. Hyperparameter adjustments for all models were performed using the Optuna framework, and model performances were evaluated using a ten-fold cross-validation method and holdout dataset results. The results showed that machine learning models can learn the optimum design space obtained from metaheuristic optimization outputs with high accuracy. In optimum wall thickness estimation, Gradient Boosting-based models provided the highest accuracy under both hinged and fixed support conditions. In total cost estimation, the Gradient Boosting model stood out under hinged support conditions, while the XGBoost model yielded the most successful results for fixed support conditions. The findings clearly show that no single machine learning model exhibits the best performance for all output parameters and support conditions. The proposed approach offers significantly higher computational efficiency compared to traditional iterative optimization processes and allows for rapid estimation of optimum design parameters without the need for any iterations. In this respect, this study provides an effective decision support tool that can be used especially in the preliminary design phases and contributes to sustainable, cost-effective reinforced concrete structure design.
A Logistic Optimization System Inspired by the Migration Route Planning Abilities of Storks: A New Model
(Yildiz Technical University, 2026) Yazıcı, Ayşe Meriç
Storks’ migration route planning capabilities have long been a focus of attention as a natural phenomenon. This study aims to examine these capabilities and provide a new perspective to the logistics sector. Inspired by the migration behavior of storks, the study presents a new logistics optimization model. The model is designed based on stork migration route planning strategies by combining nature-inspired approaches. By simulating the natural behavior of storks, the model aims to achieve basic logistics goals such as energy saving, cost reduction and effective time management. The stork-inspired logistics model is expected to provide significant advantages in logistics operations. It has the potential to increase energy efficiency, reduce costs, and improve operational efficiency in route planning. In addition, the model takes into account other important factors such as environmental sustainability and social impact. Although further research is needed to evaluate the real-world applicability and effectiveness of this model in logistics operations, it is clear that this approach provides several potential benefits to the sector. Future studies should focus on adapting the model to large-scale enterprises.