Running Theses
Fine-tuning pre-trained large language models (LLMs) enhances biomedical text mining. This thesis introduces a tool capable of performing tasks such as Named Entity Recognition (NER), Normalization (NEN), and Knowledge Graph Construction (KGC). A key research question explores how LLMs can address the challenges of named entity recognition, normalization, and relation extraction in biomedical contexts.
This thesis aims to explore the realm of Natural Language Processing (NLP) in the context of automated grading systems, focusing specifically on identifying and analyzing frequent learner mistakes. With the increasing integration of technology in education, automated grading systems have gained prominence, but they often lack nuanced understanding and feedback provision. This research endeavors to ...
Developing a Flexible Interface for Agent-Behaviors inMulti-Agent-System Simulations of Electrical Power Systems
Synopsis operators for a distributed on-the-edge streaming architecture
Detect Flames in industrial Video data using edge devices.
Validating the Reference Architecture Model of International Data Spaces Regarding Vocabulary Sharing Requirements From Life-Science Industry in the Context of Persistent Identifier Systems
