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Yayın Electroencephalography signatures associated with developmental dyslexia identified using principal component analysis(Multidisciplinary Digital Publishing Institute (MDPI), 2025-08-27) Eroğlu, Günet; Harb, Mhd Raja AbouBackground/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensionality reduction and clustering techniques to high-density electroencephalographic (EEG) recordings. We further examined the functional relevance of these features to reading performance under standardised test conditions. Methods: EEG data were collected from 200 children (100 with dyslexia and 100 age- and IQ-matched typically developing controls). Principal Component Analysis (PCA) was applied to high-dimensional EEG spectral power datasets to extract latent neurophysiological components. Twelve principal components, collectively accounting for 84.2% of the variance, were retained. K-means clustering was performed on the PCA-derived components to classify participants. Group differences in spectral power were evaluated, and correlations between principal component scores and reading fluency, measured by the TILLS Reading Fluency Subtest, were computed. Results: K-means clustering trained on PCA-derived features achieved a classification accuracy of 89.5% (silhouette coefficient = 0.67). Dyslexic participants exhibited significantly higher right parietal–occipital alpha (P8) power compared to controls (mean = 3.77 ± 0.61 vs. 2.74 ± 0.56; p < 0.001). Within the dyslexic group, PC1 scores were strongly negatively correlated with reading fluency (r = −0.61, p < 0.001), underscoring the functional relevance of EEG-derived components to behavioural reading performance. Conclusions: PCA-derived EEG patterns can distinguish between dyslexic and typically developing children with high accuracy, revealing spectral power differences consistent with atypical hemispheric specialisation. These results suggest that EEG-derived neurophysiological features hold promise for early dyslexia screening. However, before EEG can be firmly established as a reliable molecular biomarker, further multimodal research integrating EEG with immunological, neurochemical, and genetic measures is warranted.Yayın Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Harb, Mhd Raja Abou; Çeliktaş, BarışThis paper presents a novel approach to estimating Sigmoid and Tanh activation functions using Artificial Neural Networks (ANN) optimized for homomorphic encryption. The proposed method is compared against second-degree polynomial and Piecewise Linear approximations, demonstrating a minor loss in accuracy while maintaining computational efficiency. Our results suggest that the ANN-based estimator is a viable alternative for secure machine learning models requiring privacypreserving computation.Yayın A context-aware, AI-driven load balancing framework for incident escalation in SOCs(Institute of Electrical and Electronics Engineers Inc., 2025-08-12) Abuaziz, Ahmed; Çeliktaş, BarışSOCs face growing challenges in incident management due to increasing alert volumes and the complexity of cyberattacks. Traditional rule-based escalation models often fail to account for the workload of the analyst, the severity of the incident, and the organizational context. This paper proposes a context-aware, AI-driven load balancing framework for intelligent analyst assignment and incident escalation. Our framework leverages large language models (LLMs) with retrievalaugmented generation (RAG) to evaluate incident relevance and historical assignments. A reinforcement learning (RL)-based scheduler continuously optimizes incident-to-analyst assignments based on operational outcomes, enabling the system to adapt to evolving threat landscapes and organizational structures. Planned simulations in realistic SOC environments will compare the model with traditional rule-based models using metrics such as Mean Time to Resolution (MTTR), workload distribution, and escalation accuracy. This work highlights the potential of AIdriven approaches to improve SOC performance and enhance incident response effectiveness.Yayın ANN activation function estimators for homomorphic encrypted inference(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, BarışHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.Yayın Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, FarshadRecent 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.Yayın Assessing ChatGPT's accuracy in dyslexia inquiry(Institute of Electrical and Electronics Engineers Inc., 2024) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia poses challenges in accessing reliable information, crucial for affected individuals and their families. Leveraging chatbot technology offers promise in this regard. This study evaluates the OpenAI Assistant's precision in addressing dyslexia-related inquiries. Three hundred questions commonly posed by parents were categorized and presented to the Assistant. Expert evaluation of responses, graded on accuracy and completeness, yielded consistently high scores (median=5). Descriptive questions scored higher (average=4.9568) than yes/no questions (average=4.8957), indicating potential response challenges. Statistical analysis highlighted the significance of question specificity in response quality. Despite occasional difficulties, the Assistant demonstrated adaptability and reliability in providing accurate dyslexia-related information.Yayın “Can we use a biomarker detection algorithm to measure the effectiveness of 14-channel neurofeedback in dyslexia?”(Routledge, 2025-10-01) Eroğlu, Günet; Harb, Raja AbouDyslexia, one of children’s most common neurological diversities, primarily manifests as a reduced reading ability. Genetic factors contribute to dyslexia, with contemporary theories attributing it to a delay in left hemispheric lateralization that reduces effective reading and writing skills. To assist dyslexic children, smartphone application, Auto Train Brain, has been developed to enhance reading comprehension and speed. Previously, the efficacy of the mobile application’s training program was assessed using psychometric tests; however, our study employed a biomarker detection software to evaluate the neurofeedback’s impact. Machine learning (ML) techniques have recently gained traction in differentiating between dyslexia and typically developing children (TDC). The dataset of this study consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and 100 TDC. Therefore, the dyslexia biomarker detection software assessed the efficacy of the 14-channel neurofeedback administered via Auto Train Brain. Results showed significant improvement in electrophysiological normalization, increasing from 30% in the first 20 sessions to 61% by the end of the training. A two-proportion Z-test confirmed this improvement was statistically significant (Z = −3.96, p = 0.00007), particularly between the 1–20 and 1–60 session intervals (Z = −2.66, p = 0.0079).Yayın A metric-driven IT risk scoring framework: incorporating contextual and organizational factors(Institute of Electrical and Electronics Engineers Inc., 2025-09-24) Ünal, Nezih Mahmut; Çeliktaş, BarışRisk analysis is a critical process for organizations seeking to manage their cybersecurity posture effectively. However, traditional risk analysis frameworks, such as the Common Vulnerability Scoring System (CVSS), primarily evaluate technical impacts without incorporating organizational context and dynamic risk factors. This paper presents a metric-based risk analysis framework designed to provide a more adaptable and context-aware risk-scoring framework. The proposed model enables risk owners to define customized threat scenarios and dynamically adjust metric weights based on organizational needs. Unlike traditional approaches, our method integrates contextual parameters to improve the accuracy and relevance of risk calculations. Experimental evaluations demonstrate that the proposed framework enhances risk prioritization and provides more actionable insights for decision-makers. This study contributes to the field by addressing the limitations of existing risk analysis models and offering a systematic approach for cybersecurity risk management.Yayın Mahremiyeti koruyan, merkezi, hibrit film öneri sistemi: araçlar arası internet için bir yaklaşım(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Şimşek, Musa; Tüysüz Erman, AyşegülBu çalışmada, kullanıcı verilerinin gizliliğini korurken öneri doğrulu günü artırmayı hedefleyen, diferansiyel mahremiyet destekli hibrit bir öneri modeli sunulmuştur. Model mimarisi, Matris Çarpanlaması (MF), Çok Katmanlı Algılayıcı (MLP) ve Uzun Kısa Süreli Bellek (LSTM) ağlarını birleştirmektedir. Laplace mekanizmasına dayalı gürültü enjeksiyonu ile eğitim sürecinde diferansiyel mahremiyet sağlanmış ve ayrıca hiperparametre optimizasyonu uygulanmıştır. Model, kullanıcı film etkileşimlerini içeren MovieLens 100K veri kümesi üzerinde değerlendirilmiştir. Performans değerlendirmesi MSE, MAE ve NDCG metrikleriyle yapılmış; hiperparametre optimizasyonu ile MSE bazında yaklaşık %4 iyileşme sağlandığı, yüksek gizlilik düzeyinde ise doğrulukta yaklaşık %39 oranında bozulma yaşandığı gözlemlenmiştir.Yayın Çok ölçekli görsel benzerlik analizi ile oltalama saldırısı tespiti(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Kılıç, Bartu; Çeliktaş, BarışOltalama saldırıları teknolojinin gelişmesiyle günümüzün en yaygın siber güvenlik tehditlerinden biri haline gelmiştir. Bu çalışma, web sitelerinin ekran görüntülerini gelişmiş bir görsel benzerlik analizi yöntemiyle inceleyerek oltalama saldırılarını yüksek doğrulukla tespit eden bir yaklaşım sunmaktadır. Oltalama tespiti için önerilen yöntemde, algısal özütleme tabanlı çoklu çözünürlük analizi, akıllı ilgi bölgesi (ROI) tespiti ve çoklu metrik füzyonu gibi teknikler birleştirilerek yüksek doğrulukta tespit yapılabilmektedir. Veri seti, popüler bankacılık, e-posta ve sosyal medya platformlarının gerçek ve oltalama sayfalarından oluşan 23 gerçek ve 3 oltalama sayfası ekran görüntülerinden derlenmiştir. Yapılan testler, yöntemin %85 doğruluk oranı ile tekil metrik tabanlı yaklaşımlardan daha iyi performans gösterdiğini ortaya koymuştur. Dil bağımsız çalışan bu yöntem, URL ve HTML manipülasyonlarına karşı dayanıklıdır ve gerçek zamanlı oltalama tespiti için güçlü bir çözüm sunmaktadır.
