Modern sky surveys generate large data volumes, increasing the need for automated analyses. In this work a stellar spectral dataset from five libraries was assembled and relabelled via SIMBAD. A three-branch 1-D CNN was developed to jointly predict MK temperature and luminosity classes in a single pass. Errors were weighted in a custom loss by their distance on the MK grid. Performance remained stable under circular shifts up to 3 pixels. An ensemble with a custom ResNet50 achieved macro-F1 67.6% and kappa 98.3/88.4, and statistical gains confirmed via bootstrapping. Grad-CAM indicated features of focus overlapped with known molecular bands.

Date: 28.08.2025 / 13:30 Place: B-223

This thesis introduces a Large Language Model (LLM)-driven framework for automated augmentation policy optimization in image classification. The approach leverages LLM reasoning to generate and adapt augmentation strategies without human intervention. Two modes are explore: a before-training method that iteratively refines static LLM-generated policies, and an in-training adaptive method where policies evolve based on real-time performance feedback. By integrating semantic understanding with optimization loops, the framework tailors augmentation to dataset characteristics, enhancing model robustness and generalization compared to conventional augmentation techniques.

Date: 28.08.2025 / 14:45 Place: B-223

This study addresses critical challenges in disease progression modeling, particularly longitudinal data scarcity and class imbalance in medical imaging. The thesis proposes a novel conditional diffusion framework for synthesizing realistic disease progression sequences from cross-sectional data, utilizing ulcerative colitis endoscopic images. A diffusion model is developed with two distinct ordinal class embedding strategies that enable interpolation between discrete disease severity classes. This approach transforms static medical datasets into dynamic progression models, offering solutions for enhanced clinical training and disease understanding in medical applications.

Date: 29.08.2025 / 13:30 Place: B-223

This thesis presents a framework to enhance the quality of LLM-assisted academic peer review by addressing the lack of domain-specific evaluation criteria. While general review guidelines cover broad concerns, they often miss methodological nuances. The proposed system extracts review questions from past OpenReview evaluations and aligns them with new submissions using structured metadata such as methodology, datasets, and evaluation metrics. Experiments show that the framework improves review specificity, reduces hallucinated content, and enhances interpretability—providing more explainable and relevant automated reviews compared to baseline models.

Date: 21.08.2025 / 14:00 Place: A-212

This thesis proposes a cost-driven predictive maintenance strategy for turbofan engines using the C-MAPSS dataset. The method predicts failures within multiple horizons using LSTM models, interprets predictions via SHAP analysis, and applies maintenance decisions based on dominant sensor contributions. Various maintenance and failure cost ratios are evaluated, and strategies are compared to identify the most cost-effective approach. Results provide decision-making insights for companies with different cost priorities and operational constraints.

Date: 25.08.2025 / 15:30 Place: A-212

This study explores university students’ behavioral intention to adopt generative AI tools for learning, using an extended Technology Acceptance Model (TAM). Constructs such as trust, social influence, hedonic motivation, and task-technology fit are integrated to better understand the factors affecting adoption. Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze survey data collected from students. The findings aim to provide insights into students’ perceptions and guide effective integration of generative AI in educational contexts.

Date: 27.08.2025 / 10:30 Place: A-212

This study presents a mobile authentication method based on modeling the distribution of touch behavior features. Instead of using summary statistics, the approach represents user interactions as probability distributions and compares them using KL divergence. A Siamese neural network is used to learn differences between users. The method is evaluated on a public dataset, showing improvements over baseline models in terms of error rates. Results suggest that distribution-based modeling can provide useful information for continuous authentication without requiring active user input.

Date: 21.08.2025 / 10:00 Place: A-212

This thesis investigates adaptive window sampling strategies for continuous mobile behavioral biometric authentication using accelerometer data. A dual-branch Siamese model architecture leveraging time-series and frequency-domain features is proposed to generate discriminative embeddings. Multiple triplet selection techniques—including session-balanced, session-weighted, entropy-filtered, and position-aware sampling—are explored to enhance model generalization. A window-ensembled inference strategy is employed to improve verification robustness. Experimental results demonstrate the effectiveness of targeted sampling and highlight the importance of temporally informed triplet construction in mobile biometric systems.

Date: 21.08.2025 / 11:00 Place: A-212

This study investigates how sequence variations in microexon and intronic regions influence splicing outcomes under differential SRRM4 expression. A custom CNN model was developed to predict PSI values from sequence data, followed by interpretation via DeepLIFT and motif discovery using TF-MoDISco-lite. The approach uncovered cis-regulatory patterns predictive of SRRM4-sensitive splicing, providing insights into the regulatory logic governing microexon inclusion.

Date: 01.09.2025 / 11:00 Place: A-212

This thesis proposes a new analysis method aimed at identifying the root causes of problems encountered in the business world. Combining the qualitative management tool Relationship Diagram with the quantitative modeling tool Bayesian Networks, this method offers a probabilistic analysis process that takes into account both expert opinions and data containing uncertainty. This approach, which incorporates the human factor into the process, This approach, which incorporates the human factor into the process, is adaptable not only to a specific sector but also to different fields. The developed method has been tested with a real case study in the field of electronic component production and has contributed to a deeper understanding of business processes.

Date: 29.07.2025 / 10:30 Place: A-212