TT 02 - Artificial intelligence in industrial applications

Track Chairs

Daswin De Silva, La Trobe University, Australia and  Muhammad Khan, Sejong University, Korea and  Evgeny Osipov, Luleå University of Technology, Sweden and  Yudong Zhang, University of Leicester, UK

Focus

This technical track focuses on current and emerging topics of artificial intelligence that are relevant and important for industrial applications and industrial information technologies. It is aligned to the transformation of industrial informatics from model-based solutions to generative, data-driven solutions that address the challenges of dynamic and non-deterministic digital environments.

Topics under this track include (but not limited to)

  • Machine Learning
    • Deep Learning in industrial applications
    • Hyperdimensional computing for energy efficient machine learning on the Edge
    • Automated machine learning
    • Online learning from data stream
    • Unsupervised machine learning
    • Scalable machine learning
    • Machine learning for multimodal information fusion
    • Interpretability and traceable machine learning
    • Text, image, audio, video and social media analysis in industrial applications
  • Semantic Reasoning and Digital Twins
    • Semantic models for industrial applications
    • Probabilistic reasoning on IoT data
    • Combined reasoning and machine learning in digital twins
    • Context and semantic learning for industrial domain expertise
    • Digital thread models
  • AI methods for making Human-Machine Interaction Intelligent
    • Intelligent human behaviour monitoring in industrial scenarios
  • Optimization and Control
    • Reinforced learning in control
    • Fuzzy based control