Graduate School of Informatics

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

This thesis investigates multimodal data fusion and multicompartment image analysis in acute and chronic lung diseases. In a COVID-19 cohort, we integrated imaging, clinical, and viral genomic data, using sparse canonical correlation analysis and cooperative learning to explore inter-modality associations and predict intensive care unit admission. We leveraged Word2Vec to encode the viral genome. In an interstitial lung diseases cohort, we extracted lung and pulmonary artery radiomics features from chest computed tomography scans, demonstrating predictive value for pulmonary hypertension and transplant-free survival. We illustrated that multimodal data fusion and multicompartment image analysis mirror clinical decision-making processes and improve personalized prognostication.

Date: 16.07.2025 / 15:30 Place: A-108

This thesis investigates the potential of EEG-based features to distinguish between Attention Deficit Hyperactivity Disorder (ADHD) and Major Depressive Disorder (MDD), two conditions that often share overlapping symptoms and neurophysiological characteristics. Focusing on power spectral density (PSD) across frequency bands and mean curve length (MCL) as a complexity measure, the study applies multilayer perceptron (MLP) classifiers to a clinically diverse sample under both eyes open and eyes closed resting state conditions.

Date: 16.07.2025 / 14:00 Place: A-212

In energy and latency-constrained embedded systems, binary neural networks offer dramatic reductions in compute and memory by binarizing weights and activations. This thesis focuses binarizing a lightweight neural network model for classification. Using MNIST, CIFAR-10, and ImageNet, it compares full-precision models with binarized models according to accuracy, resource use, and performance trade-offs. Moreover, this thesis surveys AI hardware frameworks for FPGA-based reconfigurable systems in terms of compatibility with trending technologies and optimization strategies as a comprehensive guide.

Date: 08.07.2025 / 10:00 Place: B-116