ICMLC & ICWAPR 2026

In recent years, Machine Learning (ML) has been playing an increasingly critical role across various industrial systems, enabling predictive analytics, fault detection and diagnosis, anomaly detection, production forecasting, process optimisation, predictive and preventive maintenance and intelligent decision support. While substantial theoretical advances have been made, the practical application of ML algorithms and models in real-world industrial systems remains challenging due to factors such as data complexity and insufficiency, scalability, robustness, interpretability, reliability, and integration with existing systems.
This special session aims to bring together researchers and industry professionals to present and discuss recent advances in applied ML for industrial systems. The session focuses on practical, deployment-oriented research and real-world case studies that demonstrate how ML techniques are designed, implemented, evaluated, and integrated within operational industrial environments.
The session particularly invites contributions that bridge the gap between theory and practice, highlighting insights gained from the application, implementation, and deployment of ML in diverse industrial contexts. By strengthening the link between research and industry, this session seeks to promote robust, scalable, and trustworthy machine learning solutions that deliver measurable and sustainable impact in industrial systems.

Applied Machine Learning for industrial systems
Predictive analytics and decision support
Fault detection and diagnosis
Anomaly detection in industrial systems
Predictive and preventive maintenance
Production and process forecasting
Process optimisation using machine learning
Robust, scalable, and reliable ML models
Explainable Machine Learning for industrial systems
Real-world deployment and industrial case studies