https://dspace.jcu.cz/handle/20.500.14390/27 Tue, 21 Apr 2026 15:47:18 GMT 2026-04-21T15:47:18Z https://dspace.jcu.cz/handle/20.500.14390/48686 Text classification of customer inquiries via e-mail Weigold, Armin Pascal Sun, 01 Jan 2023 00:00:00 GMT https://dspace.jcu.cz/handle/20.500.14390/48686 2023-01-01T00:00:00Z https://dspace.jcu.cz/handle/20.500.14390/48687 Integration and analysis of the effects of an unnatural amino acid into transmembrane 4 of the Orai1 protein Gemeinhardt, Helene Sabine In this work intramolecular interactions of the Orai1 protein are investigated by integrating the unnatural amino acid p-Azido-L-phenylalanine (Azi) at different positions (A254, V252 and G247) of the Orai's transmembrane domain 4 (TM4). The compound Azi is unreactive under physiological conditions but can be activated using UV-light. Being reactive it can form covalent bonds with nearby residues. It is suspected that that residue A254 on TM4 is close enough to TM3 for the Azi to cross-link these two transmembrane domains. Positions V252 and G247 are assumed to be too far apart for crosslinking. Sun, 01 Jan 2023 00:00:00 GMT https://dspace.jcu.cz/handle/20.500.14390/48687 2023-01-01T00:00:00Z https://dspace.jcu.cz/handle/20.500.14390/48685 Using ML to Model and Optimize Chip Geometry for Improved Lithography Ahmad, Junaid This thesis delves into the creation and application of a predictive model aimed at optimizing chip production on a wafer, while maintaining the on-resistance (Ron) of a MOSFET within acceptable limits. Through a systematic approach, various regression models were developed, including linear regression, Random Forest, XGBoost, and a Deep Neural Network (DNN), to predict chip quantities considering both wafer and chip geometry. Model performance was rigorously evaluated using mean absolute error, with a focus on comparing machine learning models to a geometry-based predictor. The DNN demonstrated superior accuracy and was integrated into an optimization algorithm that managed the balance between chip quantity and Ron value. This algorithm employed Differential Evolution to identify the optimal chip layout, expanding its scope by considering reticle-based scenarios. This work contributes valuable insights into semiconductor manufacturing and chip layout optimization, offering a method to enhance wafer productivity, efficiency, and cost-effectiveness. Sun, 01 Jan 2023 00:00:00 GMT https://dspace.jcu.cz/handle/20.500.14390/48685 2023-01-01T00:00:00Z https://dspace.jcu.cz/handle/20.500.14390/48683 Enhancing Vehicle Interior Action Recognition using Contrastive Self-Supervised Learning with 3D Human Skeleton Representations El Bachiri, Yasser Over the past few years, a mounting alarm regarding the rising fatalities attributed to driver distraction-related car accidents has been highlighted the urgency of developing advanced action recognition systems within the car interior. This master thesis addresses the pressing issue of the need for advanced action recognition systems in the car interior emphasizing the potential of examining human behavior in the vehicle's interior in light of the increasing adoption of automation for better driver adaptation, human-vehicle communication, and safety. We investigate two self-supervised learning approaches, DINO with STTFormer and PSTL with STGCN, using 3D human skeleton representations on NTU RGB+D and Drive&Act datasets. Extensive experiments and evaluations, including linear and k-NN assessments, demonstrate the competitive performance of PSTL with ST-GCN, while revealing challenges in the Drive&Act dataset and the complexities of self-supervised learning convergence. This research not only contributes to the advancement of action recognition systems for safer driving and dynamic adaptation but also underscores the significance of self-supervised learning in interpreting and improving human activities inside vehicles, facilitating the development of more intuitive and responsive autonomous driving systems. Sun, 01 Jan 2023 00:00:00 GMT https://dspace.jcu.cz/handle/20.500.14390/48683 2023-01-01T00:00:00Z