论文标题
无监督的机器学习和学校的EMF辐射：对希腊205所学校的研究
Unsupervised Machine Learning and EMF radiation in schools: a study of 205 schools in Greece
论文作者
论文摘要
希腊网络基础设施的扩展引起了人们对敏感群体可能产生的负面健康影响（例如儿童）的负面影响，从暴露于长期射频电磁场（RF-EMF）。这项研究的目的是采用无监督的机器学习方法，例如分层聚类，以建立学校的EMF辐射模式。为此，我们在色萨利地区的多数学校单位进行了测量，以计算27 MHz -3 GHz范围内RF -EMF暴露的平均值，该范围涵盖了RF -EMF源的整个光谱。分层聚类树状图分析表明，沙质城市地区的人口密度与学校的EMF暴露水平无关。此外，在$ 97.5 \％$ $ $ $ $ $中，在特萨利地区发现的学校水平至少低于希腊风险范围的限制至少3500倍，而$ 2.5 \％$的曝光水平低于限制少于500倍。
The expansion of network infrastructure in Greece has raised concerns about the possible negative health effects on sensitive groups, such as children, from exposure to long-term radiofrequency electromagnetic fields (RF-EMFs). The objective of this study is to apply Unsupervised Machine Learning methods such as hierarchical clustering, in order to establish patterns of EMF radiation in schools. To this end we performed measurements in the majority schools units in the region of Thessaly in order to calculate the mean value for RF - EMF exposure in the 27 MHz - 3 GHz range, which covers the whole spectrum of RF - EMF sources. Hierarchical clustering dendrogram analysis shows that population density in urban areas of Thessaly bears no relation to the level of EMF exposure in schools. Furthermore, in $97.5\%$ of schools found in the Thessaly region, the exposure level is at least 3500 times below the Greek exposure limits while in $2.5\%$ it is a little less than 500 times below the limit.
