Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/1917
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dc.contributor.advisorBajic, Vladimir B.-
dc.contributor.advisorDuffy, Kevin Jan-
dc.contributor.authorHuman, Sepen_US
dc.date.accessioned2017-01-31T06:46:36Z
dc.date.available2017-01-31T06:46:36Z
dc.date.issued2002-
dc.identifier.urihttp://hdl.handle.net/10321/1917-
dc.descriptionDissertation submitted in compliance with the requirements for Master's Degree in Technology: Electrical Engineering (Light Current), Technikon Natal, Durban, South Africa, 2002.en_US
dc.format.extent82 pen_US
dc.language.isoenen_US
dc.subject.lcshUltraviolet radiation--South Africaen_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.titleAssessment of UV index using artificial neural networksen_US
dc.typeThesisen_US
dc.description.levelMen_US
dc.identifier.doihttps://doi.org/10.51415/10321/1917-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis-
item.languageiso639-1en-
Appears in Collections:Theses and dissertations (Engineering and Built Environment)
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