Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results …

Artificial Intelligence (AI) offers promising opportunities for innovating the field of medical imaging, yet its practical implementation remains limited. Two important reasons for this are strict legislation and the unavailability of data, partly due to patient privacy. Synthetic data (SD) could offer …

Designing lead-free piezoelectric ceramics with tailored electrical properties remains a critical challenge for various applications. In this paper we present a novel methodology integrating Machine Learning (ML) and optimization procedures to fine-tune electrical properties in lead-free (1-x) Na0.5 …

The recent advancements in machine learning (ML) techniques have significantly improved the performance in various natural language processing (NLP) tasks, particularly in deep learning with the transformer architecture. Despite their promising applications in different domains, these innovations have …

Industry 4.0 has placed an emphasis on real-time decision making in the execution of systems, such as semiconductor manufacturing. This article will evaluate a scheduling methodology called Evolutionary Learning Based Simulation Optimization (ELBSO) using data generated by a Manufacturing Execution System …

A lot of industries in the world use water and produce waste, and with limited water resources and an increasing population, industrial development has grown, putting even more stress on resources. This issue of concern has been recognized and efforts into conserving these precious resources has been …