The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.
Software defect prediction (SDP) has emerged as a crucial task in ensuring software quality and reliability. The early and accurate identification of defect-prone modules significantly reduces maintenance costs and improves system performance. In this study, we introduce a novel…
This study presents a comprehensive comparative analysis of machine learning, deep learning, and optimization-based hybrid methods for malicious URL detection on the Malicious Phish dataset. For feature selection and model hyperparameter tuning, the Genetic Algorithm (GA), Partic…
This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. A systematic review of the…
Human–Machine Interfaces (HMIs) in autonomous driving technology have recently gained significant research interest in public transportation. However, most of the studies are biased towards qualitative methods, while combining quantitative and qualitative approaches has yet to re…
Large language models (LLMs) are playing an increasingly important role in data science applications. In this study, the performance of LLMs in generating code and designing solutions for data science tasks is systematically evaluated based on different real-world tasks from the…
As autonomous vehicles (AVs) become part of urban environments, pedestrian safety and interactions with these vehicles are critical to creating sustainable, walkable cities. Intuitive pedestrian-vehicle communication is essential not only for reducing crash risk but also for supp…