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Enhancing environmental safety: Innovations in radiation monitoring and security with machine learning

Durlik Irmina, Miller Tymoteusz, Kostecka Ewelina, Sokołowska Sylwia, Kostecki Tomasz, Zwolak Rafał

Zeszyty Naukowe Politechniki Morskiej w Szczecinie

2025

nr 84 (156)

34--45

In this article, we delve into the fusion of machine learning (ML) and Internet of Things (IoT) technologies to redefine environmental radiation monitoring and security. By harnessing these advanced technologies, this work presents a novel approach to radiation safety, emphasizing enhanced real-time monitoring, precision in detection, and improved regulatory compliance. Through an in-depth analysis of various case studies and methodologies, it uncovers the potential of ML and IoT in overcoming traditional challenges, such as data accuracy and privacy concerns. The discussion extends to the implications of these technologies on environmental safety, offering a forward-looking perspective on the evolution of radiation monitoring systems. This article not only addresses the technical and ethical challenges but also highlights the transformative impact of ML and IoT integration on public health and environmental protection, paving the way for innovative solutions in the domain of environmental safety and security.