Işık University Institutional Repository
Digitally stores academic resources such as books, articles, dissertations, bulletins, reports, research data published directly or indirectly by İbn Haldun University at international standards, helps track the academic performance of the university, provides long term preservation for resources and makes publications available to Open Access in accordance with their copyright to increase the effect of publications.
Recent Submissions
Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23)
Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.
Sustainable soil stabilization using colemanite: experimental and numerical analysis of sandy soils for improved geotechnical properties
(Springer Nature, 2025-06-12)
This paper discusses the use of colemanite, a boron compound, which is a natural additive to geotechnically improved sandy soils, thus providing an eco-friendly alternative to conventional soil stabilization. Clean angular sand was the base material with the addition of colemanite in amounts of 0%, 5%, 10%, and 15% by dry mass. Various laboratory tests, such as Atterberg limits, void ratio, specific gravity, compaction, permeability, and unconsolidated undrained triaxial tests, were carried out to determine the physical and mechanical characteristics of the produced mixtures. Numerical modeling, adopted by the PLAXIS finite element program, was used to carry out simulations under various conditions for soil profiles to determine and compare soil behavior. The findings revealed that the addition of colemanite significantly reduced permeability and void ratios while enhancing stiffness and strength, with 15% colemanite yielding the best performance. This study is one of those that focuses on the introduction of colemanite, which can also act as an effective stabilizer and is a much greener and more environmentally friendly option. Apart from this, it has other advantages both economically and ecologically by reducing the amount of cement, which is a high carbon source required for building based on this. The discoveries bring in the further development of green geotechnical engineering, which also includes the construction of sustainable infrastructures.
Retinal disease diagnosis in OCT scans using a foundational model
(Springer Science and Business Media Deutschland GmbH, 2025)
This study examines the feasibility and performance of using single OCT slices from the OCTA-500 dataset to classify DR (Diabetic Retinopathy) and AMD (Age-Related Macular Degeneration) with a pre-trained transformer-based model (RETFound). The experiments revealed the effective adaptation capability of the pretrained model to the retinal disease classification problem. We further explored the impact of using different slices from the OCT volume, assessing the sensitivity of the results to the choice of a single slice (e.g., “middle slice”) and whether analyzing both horizontal and vertical cross-sectional slices could improve outcomes. However, deep neural networks are complex systems that do not indicate directly whether they have learned and generalized the disease appearance as human experts do. The original dataset lacked disease localization annotations. Therefore, we collected new disease classification and localization annotations from independent experts for a subset of OCTA-500 images. We compared RETFound’s explainability-based localization outputs with these newly collected annotations and found that the region attributions aligned well with the expert annotations. Additionally, we assessed the agreement and variability between experts and RETFound in classifying disease conditions. The Kappa values, ranging from 0.35 to 0.69, indicated moderate agreement among experts and between the experts and the model. The transformer-based RETFound model using single or multiple OCT slices, is an efficient approach to diagnosing AMD and DR.
Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis
(Institute of Electrical and Electronics Engineers Inc., 2025-05-19)
The rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.
Goal-Oriented Random Access (GORA)
(Institute of Electrical and Electronics Engineers Inc., 2025-08)
We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function of the Age of Information. Our findings reveal that, under certain conditions, it may be desirable for transmitters to delay channel access intentionally and, when accessing the channel, transmit aged samples to reach a specific goal at the receiver.
