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Yayın Mobile applications discovery: a subscriber-centric approach(Wiley Periodicals, 2011-03) Erman, Bilgehan; İnan, Ali; Nagarajan, Ramesh; Uzunalioğlu, HüseyinRapid adoption of smartphones and the business success of the Apple App Store have resulted in the rampant growth of mobile applications. Seeking new revenue opportunities from application development has created a gold rush. However, free or very cheap applications constitute a great bulk of the application downloads putting great pricing pressure on the developers. Furthermore, usage statistics suggest that most of the applications have been either one-trick applications or are downright useless, meriting no attention from the user beyond the first day. This is not surprising since cheap prices will dissuade developers from investing large sums of money to continue to develop more sophisticated, high quality applications. Developers have been complaining about the lack of visibility of their applications in stores that are beginning to resemble a high volume warehouse. It is clear that enhancing application discovery and building better marketing tools will be essential for the continued success of the mobile application marketplace and application stores. This paper proposes and investigates techniques for effective discovery of applications by matching user interests with application characteristics, with a special focus on adapting classical data mining techniques to user ratings of the applications. The user ratings are leveraged to make recommendations on potential applications of interest.Yayın Kural bazlı otomatik haber etiketleme(IEEE, 2017-06-27) Özenç, Berke; Solak, ErcanBu çalışmada , genel ağ kaynaklarından haber toplayan ve topladığı bu haberleri otomatik olarak etiketleyen kural tabanlı bir uygulama yapılmıştır. Çalışmanın alt amacı hangi özelliklerin etiket belirleme işine daha uygun olduğunu ölçmektir. Elle etiketlenmiş 100 haber üzerinde her bir kuralın başarısı oranı ölçülmüştür.Yayın Transforming tourism experience: AI-based smart travel platform(Association for Computing Machinery, 2023) Yöndem, Meltem Turhan; Özçelik, Şuayb Talha; Caetano, Inés; Figueiredo, José; Alves, Patrícia; Marreiros, Goreti; Bahtiyar, Hüseyin; Yüksel, Eda; Perales, FernandoIn this paper, we propose the development of a novel personalized tourism platform incorporating artificial intelligence (AI) and augmented reality (AR) technologies to enhance the smart tourism experience. The platform utilizes various data sources, including travel history, user activity, and personality assessments, combined with machine learning algorithms to generate tailored travel recommendations for individual users. We implemented fundamental requirements for the platform: secure user identification using blockchain technology and provision of personalized services based on user interests and preferences. By addressing these requirements, the platform aims to increase tourist satisfaction and improve the efficiency of the tourism industry. In collaboration with various universities and companies, this multinational project aims to create a versatile platform that can seamlessly integrate new smart tourism units, providing an engaging, educational, and enjoyable experience for users.Yayın Federated hybrid privacy-preserving movie recommendation system for internet-of-vehicles(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-08-02) Şimşek, Musa; Erman Tüysüz, Ayşegül; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Master’s Program in Computer EngineeringIn this research, we introduced a pioneering strategy to address the pressing privacy concerns associated with vehicular movie recommendation systems. As the demand for personalized entertainment options in vehicles increases, so does the need to protect user data. To tackle this challenge, we utilized the PyTorch framework to create a robust foundation from scratch. A key component of our approach was the addition of Laplace noise during the training process, which ensured differential privacy. This technique effectively safeguarded user data while simultaneously optimizing model performance, allowing us to maintain high levels of recommendation accuracy. Furthermore, we employed the Optuna hyperparameter optimization framework, which played a crucial role in enhancing the model's performance. By fine-tuning various parameters, we were able to elevate the overall efficiency of the system beyond the capabilities of the base model. Our extensive experimentation utilized the Movielens-1M benchmark movie dataset, which provided a solid basis for evaluating our approach. The results demonstrated a significant improvement over baseline models, validating the effectiveness of our privacy-preserving vehicular movie recommendation system. In addition to our centralised model, we conducted a comprehensive comparison with practical federated frameworks, including FedAvg, FedProx, and FedMedian. Our findings revealed that all federated models outperformed the centralised models by at least 2%, while also exhibiting shorter runtimes.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 Adaptive incident escalation in SOCs via AI-driven skill-aware assignment and tier optimization(Institute of Electrical and Electronics Engineers Inc., 2026-04-15) Abuaziz, Ahmed; Çeliktaş, BarışModern Security Operations Centers (SOCs) face significant operational bottlenecks driven by escalating alert volumes, increasingly sophisticated cyberattack vectors, and chronic imbalances in analyst workloads. Conventional rule-based escalation models frequently fail to account for the multi-dimensional nature of incident characteristics, the nuances of analyst expertise, and fluctuating operational demands. This study proposes a comprehensive AI-driven framework for intelligent incident assignment and workload optimization. The framework introduces five primary contributions: 1) a multi-factor scoring model that integrates severity and complexity metrics with dynamic workload balancing; 2) two novel optimization algorithms, Quantile-Targeted Normality-Regularized Optimization (QT-NRO) and Joint Optimization of Weights and Thresholds (JOWT), to calibrate scoring coefficients against target analyst utilization; 3) a Large Language Model (LLM) engine leveraging Retrieval-Augmented Generation (RAG) for semantic alignment between incident requirements and analyst expertise; 4) an Adaptive Capacity Zoning mechanism for dynamic workload management; and 5) a novel RAG Relevance Score metric—a pre-resolution, semantic alignment indicator that quantifies analyst-incident assignment quality independently of resolution time, addressing a fundamental limitation of traditional temporal metrics such as Mean Time to Resolution (MTTR) and providing a reusable benchmark applicable to any skill-aware assignment system. In addition, the framework incorporates a feedback-based continuous learning mechanism that utilizes historical resolution data to inform future assignments. An experimental evaluation using 10,021 real-world incidents from Microsoft Defender demonstrates that the JOWT algorithm achieves a tier distribution alignment within 0.8% of targets. LLM-enhanced semantic matching yields improvements between 26.7% and 126.8% in skill alignment across both normal-load and high-load evaluations, while simulations indicate a 31.8% reduction in MTTR. These results substantiate the efficacy of AI-driven methodologies in enhancing SOC operational efficiency and response precision.
