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

Authors must submit an electronic copy (in word or pdf) of their complete manuscript directly to the Session Organizer (lien.nguyen@sydneyinstitute.edu.au) before April 15, 2026

Associate Professor Lien Nguyen
Sydney Institute of Higher Education, Australia
lien.nguyen@sydneyinstitute.edu.au

This special session explores cutting-edge research in Explainable AI (XAI), and AI interpretability focusing on techniques, applications, and challenges of making AI transparent and interpretable.

• Novel XAI methods for complex models
• XAI in critical applications (healthcare, finance, etc.)
• Mechanistic Interpretability & Representation Engineering
• Open challenges in XAI and Interpretability adoption

Authors must submit an electronic copy (in word or pdf) of their complete manuscript directly to the Session Organizer (jonathan.pan.jy@gmail.com) before April 15, 2026

Professor Jonathan Pan
Nanyang Technological University
jonathan.pan.jy@gmail.com

Evolutionary algorithms have demonstrated superior global search capabilities, and have been applied to diverse real-life single, multi, and many-objective optimization problems. Examples include the use of evolutionary algorithms for optimal parameter identification and discriminative feature selection pertaining to diverse classification and regression models as well as hybrid evolutionary and clustering algorithms for image segmentation and visual saliency detection.
In parallel, deep learning models have demonstrated great success in dealing with complex computer vision tasks. Examples include the use of deep convolutional neural networks combined with recurrent models for image caption generation and visual question generation. Deep learning combined with transfer learning has also been employed to deal with various computer vision tasks. Nevertheless, the design of new and effective deep learning models and identification of the optimal hyper-parameters of the resulting models require profound domain knowledge, which may not always be available to researchers. In this regard, superior search capabilities of evolutionary algorithms can be exploited to tackle such optimization problems, e.g. to formulate evolving deep neural networks that fit the tasks at hand.
This special session aims to stimulate research pertaining to not only feature selection, optimal topology and hyper-parameter identification for clustering and classification systems but also evolving deep architecture generation through evolutionary algorithm and related paradigms.
It also aims to stimulate new developments to address research gaps such as deep network generation with residual and dense connectivity as well as hybrid cascaded architectures to tackle vanishing gradients and complex computer vision tasks such as object detection, image description and visual question generation.

• Image segmentation
• Data stream clustering
• Feature selection
• Object detection and recognition
• Image description generation
• Visual question generation
• Visual saliency detection
• Image classification
• Image retrieval
• Human or object attribute prediction
• Facial expression recognition and age estimation
• Human action recognition
• Bioinformatics (e.g. skin cancer, heart disease, and brain tumour classification etc.)
• Machine translation, language generation and speech recognition
• Evolving deep neural network generation for diverse computer vision, image processing and signal processing problems
• Hybrid clustering techniques (e.g. clustering models combined with evolutionary algorithms)
• Optimal topology and hyper-parameter identification for classification/regression and ensemble learning models

Authors must submit an electronic copy (in word or pdf) of their complete manuscript directly to the Session Organizer (li.zhang@rhul.ac.uk) before April 15, 2026

Professor Li Zhang
Royal Holloway, University of London, UK
li.zhang@rhul.ac.uk