Guest Editor(s)
Assoc. Prof. Ir. Dr. Kim Seng Chia
Email: kschia@uthm.edu.my
Affiliation: Department of Electronic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Homepage:
Research Interests: soft sensor, machine learning, transfer learning, real-time embedded system
Dr. Sim Hiew Moi
Email: hiewmoi@utm.my
Affiliation: Department of Software Engineering, Universiti Teknologi Malaysia, Skudai Johor
Homepage:
Research Interests: machine learning, biometrics, image processing, artificial intelligence, software engineering
Assoc. Prof. Dr. Wan Nurshazwani Wan Zakaria
Email: wshazwani@uitm.edu.my
Affiliation: Faculty of Mechanical Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia
Homepage:
Research Interests: robot manipulation, mechatronics, robot navigation, artificial intelligence, vision system
Summary
The rapid advancement of artificial intelligence (AI), machine learning (ML), and industrial digitalization is accelerating the development of smart and sustainable industrial systems. Data-driven technologies have significantly improved process monitoring, prediction, optimization, and decision-making by extracting valuable insights from complex industrial processes and heterogeneous sensor data. In particular, soft sensing has become an essential approach for estimating critical process variables that are difficult or costly to measure directly, enabling more efficient, reliable, and sustainable industrial operations. Despite these advances, practical deployment remains challenging due to limited labelled data, varying operating conditions, sensor uncertainties, nonlinear process dynamics, and the need for robust, interpretable, and transferable AI models. Addressing these challenges requires innovative machine learning methodologies and intelligent data-driven solutions for reliable industrial applications.
This Special Issue provides a platform for researchers and practitioners to present original research on the latest advances in machine learning and soft sensing for smart and sustainable industrial systems.
Topics of interest include, but are not limited to:
· Machine learning and deep learning for industrial applications
· Intelligent soft sensing and virtual sensing
· Data-driven modelling and predictive analytics
· Transfer learning and domain adaptation
· Explainable and trustworthy machine learning
· Sensor fusion and multimodal data analytics
· Edge AI and Industrial Internet of Things (IIoT)
· Predictive maintenance and condition monitoring
· Intelligent process monitoring and control
· Sustainable manufacturing and industrial optimization
Keywords
machine learning, soft sensing, data-driven modelling, intelligent automation, industrial AI, transfer learning, deep learning, edge AI, predictive analytics
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