Predicting surgical case durations accurately is essential for operating room scheduling and resource management of healthcare facilities. While everything relies on technology recently, enabling a system that predicts surgical durations using machine learning enhances accuracy, decreases workload of the healthcare personnel and increases patient satisfaction eventually. Using RFID-derived data to develop this machine learning model is more precise because the data source is not manually recorded. By dividing the available surgical data into three as short, medium and long, predictions are made by giving an estimation interval and the results are coherent with the literature according to the error rates.

Date: 13.01.2026 / 10:00 Place: A-212

This thesis proposes a unified framework for few-shot vision-language adaptation. Addressing the limitations of holistic image matching in models like CLIP, we introduce a dual-branch architecture combining global prompts with a locality-aware pathway. This local branch utilizes Value-Value (V-V) attention and Optimal Transport (OT) to enforce balanced, discriminative alignments between fine-grained image patches and class-specific prompts. Extensive evaluation on 11 benchmarks demonstrates state-of-the-art average accuracy. Furthermore, the framework exhibits superior Out-of-Distribution (OOD) robustness, offering a configurable trade-off between task specialization and generalized robustness.

Date: 05.01.2026 / 13:00 Place: A-212

This thesis study investigates the adoption of Generative Artificial Intelligence (GenAI) technologies among small and medium-sized IT enterprises in Türkiye. Using the Delphi method and Interpretive Structural Modeling (ISM), the research identifies key barriers and examines their interrelationships to reveal hierarchical influences. The study explores technological, organizational, and environmental determinants shaping GenAI adoption. Findings provide an empirically grounded framework that supports strategic decision-making for SMEs aiming to integrate generative AI effectively. The proposed model offers both theoretical insight and practical guidance for advancing AI-driven digital transformation.

Date: 22.12.2025 / 10:00 Place: B-116

Somatic mutations drive the transformation of normal cells into cancer. However,distinguishing driver mutations, which confer a selective growth advantage, from the vast background of neutral passenger mutations remains a critical challenge. To address this, we introduce a novel graph-based framework that constructs mutation-centric networks by leveraging longrange genomic interaction data. Our method models genomic intervals as nodes and their long-range interactions or overlaps as edges. Starting from a ’seed’ mutation, the graph expands iteratively, finding overlaps and interacting intervals to capture both local and distal genomic context. This architecture allows us to quantify a mutation’s topological influence, identify complex structural patterns (such as graph cycles), and assess proximity to known driver genes across variable ranges. Furthermore, this approach naturally generates embeddings for individual mutations, enabling the clustering of samples based on mutation profile similarity. Ultimately, by providing a comprehensive, interaction-aware view of the genomic landscape, our framework facilitates more accurate driver identification and improved patient stratification for personalized treatment.

Date: 13.01.2026 / 11:00 Place: A-212

This study presents a data-driven approach for modeling strong motion data and soil characteristics using generative AI. A Conditional Convolutional Variational Autoencoder was trained on amplitude and phase spectrograms of earthquake waveforms to generate realistic strong-motion data. The model, fine-tuned with limited site-specific data, effectively captures physical patterns and soil-dependent features without theoretical assumptions or heavy computation. Validation through fundamental site frequency analysis achieved an alignment score of 0.84, demonstrating that generated waveforms accurately reproduce site-specific frequency characteristics and distinguish between different locations.

Date: 03.11.2025 / 10:00 Place: A-212

This thesis examines technical debt (TD) in AI-integrated software projects by mapping debt cases to lifecycle processes defined in ISO/IEC 5338, 12207, and 15288. A dataset of 8 main and 24 subcategories of TD was systematically linked to 25 processes, with validation by a large language model, a domain expert, and two academic reviewers. Findings reveal uneven TD distribution, particularly in AI Data Engineering, Quality Assurance, and System Requirements Definition. The study highlights the interconnected nature of processes and proposes a process-based standard-driven approach to support sustainable reliability in AI-integrated systems.

Date: 01.09.2025 / 10:00 Place: B-116

This thesis examines the adoption of cloud computing in small and medium-sized enterprises (SMEs), highlighting both its transformative potential and the challenges it presents. While cloud solutions offer scalability, flexibility, and cost efficiency, SMEs face barriers such as limited resources, security concerns, and regulatory compliance. Through a meta-synthesis of existing studies, the research identifies key success factors and proposes a tailored framework that integrates technological, organizational, and strategic considerations. By bridging benefits with practical challenges, the study provides SMEs with a systematic approach to cloud migration, enhancing competitiveness in the digital economy.

Date: 28.08.2025 Place: A-212

This thesis tackles radiographic flaw detection in aluminium castings by pairing a new high-resolution X-ray dataset, annotated according to the American Society for Testing and Materials (ASTM) standards, with self-supervised pretraining on synthetic radiographs. Standard detectors are benchmarked to establish baselines and reveal domain challenges. To mitigate data scarcity and class imbalance, class-specific synthetic samples are generated from a few publicly available references using Stable Diffusion and are employed for multi-positive contrastive pretraining. The resulting domain-aligned backbone, surpasses ImageNet-pretrained baseline on downstream tasks on the proposed dataset.

Date: 01.09.2025 / 15:00 Place: B-116

Green Renovator is a serious game that makes city-scale energy management tangible using a real neighborhood dataset. Players act as a local authority balancing retrofit investments, energy supply, technologies, and policy under uncertainty via a random-event system. An in-game encyclopedia and optional annual quiz support reflection. Evaluated with N=55 via SUS and custom awareness/learning and engagement instruments, results show consistent good usability and positive educational effects. Thematic insights reveal an understanding of interdependencies and call for more events/policy depth. The study offers a practical, replayable design with demonstrated promise.

Date: 28.08.2025 / 10:00 Place: A-212

This thesis explores how human values shape ethical decision-making in responsible AI (RAI) development. Using Schwartz's value theory framework, the research surveyed 80 AI professionals across various stakeholder roles, including developers, designers, managers, and data scientists, to analyze their value profiles and ethical priorities. Key findings indicate that designers valued Universalism significantly more than developers, with developers scoring lower on Self-Transcendence values. The study shows that stakeholders' gender and experience influence ethical decisions, while also highlighting different ethical priorities among roles. This research underscores the important connection between stakeholder values and ethical AI development, encouraging better collaboration in RAI development.

Date: 27.08.2025 / 15:30 Place: A-212