Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/435
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dc.contributor.authorAmaratunga, D-
dc.contributor.authorCabrera, J-
dc.date.accessioned2022-01-03T10:06:51Z-
dc.date.available2022-01-03T10:06:51Z-
dc.date.issued2021-09-25-
dc.identifier.issn2783-8862-
dc.identifier.urihttp://localhost:80/handle/123456789/435-
dc.description.abstractMany applications involve looking at and comparing trends in data. We will discuss some statistics that can be used to assess the similarity or dissimilarity between pairs of cumulative trends. These statistics can then be used to study sets of trends – for example, to cluster them or to compare them across different groups We will describe one possible approach and illustrate its use in two case studies. In the first case study, we studied the trend over time of COVID-19 in New Jersey in the USA– it was found that areas close to New York City had significantly different (more rapidly increasing) cumulative trends compared to areas further from New York City during the early days of the pandemic, but this difference dissipated as the pandemic progressed and spread within New Jersey itself. In the second case study, we compared two sets of CT scan images of lungs – a significant difference could be detected between COPD-diseased lungs and normal lungs. Overall, the method performed well and detected insightful differences.en_US
dc.description.sponsorshipFaculty of Humanities & Sciences,SLIITen_US
dc.language.isoenen_US
dc.publisherFaculty of Humanities and Sciences,SLIITen_US
dc.relation.ispartofseriesSICASH 2021;610-616p.-
dc.subjectHotelling’s testen_US
dc.subjectMultidimensional Scalingen_US
dc.subjectManhattan Distanceen_US
dc.titleComparing Trends in Data (with Applications to COVID and Image Data)en_US
dc.typeArticleen_US
Appears in Collections:Proceedings of the SLIIT International Conference on Advancements in Sciences and Humanities2021 [SICASH]
SLIIT Journal of Humanities & Sciences (SJHS)

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