Journal Focus Issue

   

A Focus Issue based on the Symposium topic will be published by the journal Machine Learning: Engineering (IOP Publishing).

This issue is now accepting articles for peer review!

Here’s the link to the FOCUS ISSUE website.

The open-access journal will fully waive article processing fees (APCs) for submissions made to this Focus Issue. 

We encourage our delegates to consider submitting their research to the special issue. 

SCOPE OF FOCUS ISSUE

Machine learning (ML) has revolutionized decision-making across multiple fields, including engineering, by enabling unconventional strategies that have driven significant improvements in key performance outcomes. As more powerful hardware and cutting-edge algorithms continue to emerge, ML’s role in decision sciences is expanding, offering new ways to address complex, real-world challenges.

The application of ML-based decision-making in various sectors, such as manufacturing, flow chemistry, water and energy management, and robotics, holds great potential for driving higher productivity, improved quality, faster progress toward net-zero emissions, reduced waste, and the more sustainable use of resources.

We invite researchers and practitioners to submit high-quality papers on innovative uses of machine learning for real-time or near real-time control decisions in various engineering domains. The collection focuses on how ML can enhance the adaptability, autonomy, and efficiency of control systems, processes, and devices. Submissions should emphasize the practical application of ML techniques, their benefits over traditional control strategies, and the potential for improving outcomes such as productivity, quality, and sustainability.

The collection covers (but it is not limited to):

1. ML-Based Control Systems including reinforcement learning and Bayesian ML

2. Industrial Applications of ML

3. Sustainability and Efficiency

4. Data-Driven Decision-Making including adaptive control, predictive analytics, anomaly detection and resource management

5. Cross-Disciplinary and Novel Approaches including digital twins, real-time feedback, multi-agent systems, and multi-objective optimizations

6. Case Studies and Practical Implementations including industrial case studies, the comparison of ML-based control strategies, and practical challenges and solutions for the integration of ML in real-time systems.

JOURNAL SCOPE: