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Yayın Effective semi-supervised learning strategies for automatic sentence segmentation(Elsevier Science BV, 2018-04-01) Dalva, Doğan; Güz, Ümit; Gürkan, HakanThe primary objective of sentence segmentation process is to determine the sentence boundaries of a stream of words output by the automatic speech recognizers. Statistical methods developed for sentence segmentation requires a significant amount of labeled data which is time-consuming, labor intensive and expensive. In this work, we propose new multi-view semi-supervised learning strategies for sentence boundary classification problem using lexical, prosodic, and morphological information. The aim is to find effective semi-supervised machine learning strategies when only small sets of sentence boundary labeled data are available. We primarily investigate two semi-supervised learning approaches, called self-training and co-training. Different example selection strategies were also used for co-training, namely, agreement, disagreement and self-combined. Furthermore, we propose three-view and committee-based algorithms incorporating with agreement, disagreement and self-combined strategies using three disjoint feature sets. We present comparative results of different learning strategies on the sentence segmentation task. The experimental results show that the sentence segmentation performance can be highly improved using multi-view learning strategies that we proposed since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average baseline F-measure of 67.66% to 75.15% and 64.84% to 66.32% when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively.Yayın An industrial application using blackboard architecture(Işık Üniversitesi, 2006) Tünay, Kerem Burak; Kuru, Selahattin; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıThis thesis implements control architecture for goal-driven blackboard systems. The architecture is based on searching a general goal tree by diminishing into sub-goal trees. The aim is to develop a problem solving architecture in the AI space via blackboard system. The basic elements of the architecture are goals, policies, strategies, facts, methods, and knowledge sources. The basic control loop employs a bidding mechanism to determine the knowledge source to be executed at the current cycle. A policy is a local scheduling criterion which guides to bidding process and it indicates which of the attributes of the knowledge sources are relevant in this process. A strategy is a global scheduling criteria such as depth-first, breadth-first etc. A method is a partially complete general goal tree structure representing high level knowledge on how to solve a problem. The architecture employs a control blackboard, and separate knowledge sources for the control problem and for representing the domain knowledge. A production planning application is developed using this architecture. Both C++ and ABAP languages were used to implement this application.Yayın Tree Ensembles on the induced discrete space(Institute of Electrical and Electronics Engineers Inc., 2016-05) Yıldız, Olcay TanerDecision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and these orderings are used as the new attributes in decision tree induction. Although K-tree performs significantly better than the proper one, their exponential time complexity can prohibit their use. In this brief, we propose K-forest, an extension of random forest, where a subset of features is selected randomly from the induced discrete space. Simulation results on 17 data sets show that the novel ensemble classifier has significantly lower error rate compared with the random forest based on the original feature space.Yayın A review of "The Fourth Industrial Revolution" by Klaus Schwab(Işık Üniversitesi Yayınları, 2024-04-30) Abekah-Brown, Mustapha AkweiKlaus Schwab's "The Fourth Industrial Revolution" illuminates a period marked by remarkable technological advancements that are fundamentally reshaping our society. The book meticulously details breakthroughs in artificial intelligence, robotics, and biotechnology, while also acknowledging the challenges accompanying these innovations. Schwab sets out to achieve objectives centered around increasing awareness, fostering understanding, and promoting cooperation throughout the book. This book is an indispensable reading for anyone seeking a deeper understanding of the ongoing revolution and its implications.Yayın Quadratic programming for class ordering in rule induction(Elsevier Science BV, 2015-03-01) Yıldız, Olcay TanerSeparate-and-conquer type rule induction algorithms such as Ripper, solve a K>2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing prior probabilities. Although the heuristic works well in practice, there is much room for improvement. In this paper, we propose a novel approach to improve this heuristic. The approach transforms the ordering search problem into a quadratic optimization problem and uses the solution of the optimization problem to extract the optimal ordering. We compared new Ripper (guided by the ordering found with our approach) with original Ripper (guided by the heuristic ordering) on 27 datasets. Simulation results show that our approach produces rulesets that are significantly better than those produced by the original Ripper.Yayın Software defect prediction using Bayesian networks and kernel methods(Işık Üniversitesi, 2012-07-01) Okutan, Ahmet; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora ProgramıThere are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian modelling to determine the influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, We define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We wxtract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, We learn both the marginal defect proneness probability of the whole software system and the set of most effective metrics. Our experiments on nine open source Promise data repository data sets show that respense for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most efective metrics whereas coupling between objets (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less efective metris on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are unstustworthy. On tthe other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, We observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness.However, futher investigation involving a greater number of projects, is need to confirm our findings. Furthermore, we propose a novel technique for defect prediction that uses plagiarism detection tools. Although the defect prediction problem haz been researched for a long time, the results achieved are not so bright. We use kernel programming to model the relationship between source code similarity and defectiveness. Each value in the kernel matrix shows how much parallelism exit between the corresponding files ib the kernel matrix shows how much parallelism exist between the corresponding files in the software system chosen. Our experiments on 10 real world datasets indicate that support vector machines (SVM) with a precalculated kernel matrix performs better than the SVM with the usual linear and RBF kernels and generates comparable results with the famous defect prediction methods like linear logistic regression and J48 in terms of the area under the curve (AUC).Furthermore, we observed that when the amount of similarity among the files of a software system is high, then the AUC found by the SVM with precomputed kernel can be used to predict the number of defects in the files or classes of a software system, because we observe a relationship between source code similarity and the number of defects. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities. The experiments on 10 Promise datasets indicate that while predicting the number of defects, SVM with a precomputed kernel performs as good as the SVM with the usual linear and RBF kernels, in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of these experiments suggest that source code similarity is a good means of predicting both defectiveness and the number of defects in software modules.Yayın Application of ChatGPT in the tourism domain: potential structures and challenges(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanThe tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.Yayın LuminaURO: a comprehensive Artificial Intelligence Driven Assistant for enhancing urological diagnostics and patient care(Hayat Sağlık ve Sosyal Hizmetler Vakfı, 2025-05-29) Soylu, Tuncay; Topçu, İbrahim; Karaman, Muhammet İhsan; Tuzcu, Esra Melis; Kınık, Abdullah Harun; Güneren, Mustafa Sacit; Salman, Zeynep; Demir, Perihan; Beyzanur, KaçAim: This study aims to develop and validate LuminaURO, a Retrieval-Augmented Generation (RAG)-based AI Assistant specifically designed for urological healthcare, addressing the limitations of conventional Large Language Models (LLMs) in healthcare applications. Methods: We developed LuminaURO using a specialized repository of urological documents and implemented a novel pooling methodology to search multilingual documents and aggregate information for response generation. The system was evaluated using multiple similarity algorithms (OESM, Spacy, T5, and BERTScore) and expert assessment by urologists (n=3). Results: LuminaURO generates responses within 8-15 seconds from multilingual documents and enhances user interaction by providing two contextually relevant follow-up questions per query. The architecture demonstrates significant improvements in search latency, memory requirements, and similarity metrics compared to state-of-the-art approaches. Validation shows similarity scores of 0.6756, 0.7206, 0.9296, 0.9223, and 0.9183 for English responses, and 0.6686, 0.7166, 0.8119, 0.9220, 0.9315, and 0.9086 for Turkish responses. Expert evaluation by urologists revealed similarity scores of 0.9444 and 0.9408 for English and Turkish responses, respectively. Conclusion: LuminaURO successfully addresses the limitations of conventional LLM implementations in healthcare by utilizing specialized urological documents and our innovative pooling methodology for multilanguage document processing. The high similarity scores across multiple evaluation metrics and strong expert validation confirm the system’s effectiveness in providing accurate and relevant urological information. Future research will focus on expanding this approach to other medical specialties, with the ultimate goal of developing LuminaHealth, a comprehensive healthcare assistant covering all medical domains.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 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.
