Machine Learning Algorithms for Industrial Applications

Dear Colleagues,

Machine learning (ML) is now central to the digital transformation of industrial processes, enabling data-driven decision-making, predictive maintenance, quality assurance, supply chain optimization, process automation, and the development of autonomous technologies.

The convergence of big data, high-performance computing, and advanced ML algorithms is unlocking new possibilities across diverse sectors such as manufacturing, energy, transportation, aerospace, and healthcare. However, significant challenges remain in developing ML models that are interpretable, scalable, and resilient under real-world conditions.

This Special Issue welcomes original research articles, review papers, and case studies that address these opportunities and challenges. Research areas include (but are not limited to) the following topics:

1. Supervised and unsupervised learning techniques for industrial systems;
2. Reinforcement learning for process control and automation;
3. Real-time machine learning and edge computing in industrial environments;
4. Predictive maintenance and fault diagnosis using ML;
5. Quality control and defect detection using computer vision and ML;
6. Explainable AI (XAI) for industrial decision support systems;
7. ML-based optimization for manufacturing, logistics, and resource planning;
8. Integration of ML with digital twins and Industry 4.0 paradigms;
9. Safety, reliability, and robustness of ML models in critical systems.

Dr. Abiel Aguilar-González
Prof. Dr. Tobias Meisen
Guest Editors

Manuscript Submission Information

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