首页|University of Florence Reports Findings in Machine Learning (Machine Learning and Deep Learning for Safety Applications: Investigating the Intellectual Structure and the Temporal Evolution)
University of Florence Reports Findings in Machine Learning (Machine Learning and Deep Learning for Safety Applications: Investigating the Intellectual Structure and the Temporal Evolution)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Florence, Italy, by NewsRx correspondents, research stated, “Over the last decades, safety requirements have become of primary concern. In the context of safety, several strategies could be pursued in many engineering fields.” Our news editors obtained a quote from the research from the University of Florence, “Moreover, many techniques have been proposed to deal with safety, risk, and reliability matters, such as Machine Learning (ML) and Deep Learning (DL). ML and DL are characterised by a high variety of algorithms, adaptable for different purposes. This generated wide and fragmented literature on ML and DL for safety purposes, moreover, literature review and bibliometric studies of the past years mainly focus on a single research area or application field. Thus, this paper aims to provide a holistic understanding of the research on this topic through a Systematic Bibliometric Analysis (SBA), along with proposing a viable option to conduct SBAs. The focus is on investigating the main research areas, application fields, relevant authors and studies, and temporal evolution. It emerged that rotating equipment, structural health monitoring, batteries, aeroengines, and turbines are popular fields. Moreover, the results depicted an increase in popularity of DL, along with new approaches such as deep reinforcement learning through the past four years.”
FlorenceItalyEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Florence
NETL
NSTL
Elsevier
