Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos

dc.contributor.advisorNotargiacomo, Pollyana Coelho da Silva
dc.contributor.advisor-co1Silva, Leandro Augusto da
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/1396385111251741 / https://orcid.org/0000-0002-8671-3102por
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/5131975026612008 / https://orcid.org/0000-0001-8292-1644por
dc.contributor.authorCasara, Mary Adriana
dc.creator.Latteshttp://lattes.cnpq.br/0782937908073737por
dc.date.accessioned2021-12-18T21:44:29Z
dc.date.available2021-12-18T21:44:29Z
dc.date.issued2020-03-24
dc.description.abstractScientific production is one of the components considered by CAPES in the four-year evaluation of Stricto Sensu Graduate Programs (Masters and Doctoral Programs) in Brazil and directly influences the grade awarded to these Programs. Higher grades imply greater visibility and, consequently, the attraction of financial resources in the form of scholarships and funding for research. Thus, this paper aims to analyze the scientific production represented by the theses, dissertations, projects and book chapters generated in the period from 2013 to 2016, that is, the period coinciding with the 2017 evaluation for the four-year preceding period, in order to understand its relationship with the performance of the original Programs. This work not only consists of the quantitative analysis of the production of Graduate Programs, but also seeks, through techniques of artificial neural networks and text mining, to generate groups of Programs based on the similarity of their productions. The results obtained allow the identification of predominant patterns and characteristics of the Programs considered to be of excellence, which can be used as a reference by other Programs that wish to achieve the same performance.eng
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superiorpor
dc.description.sponsorshipInstituto Presbiteriano Mackenziepor
dc.formatapplication/pdf*
dc.identifier.citationCASARA, Mary Adriana. Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos. 2020. 70 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2020por
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/28617
dc.keywordsbibliometric indicatorseng
dc.keywordstext miningeng
dc.keywordsdata miningeng
dc.keywordsself-organizing mapseng
dc.keywordsartificial neural networkseng
dc.keywordstopic modelingeng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectindicadores bibliométricospor
dc.subjectmineração de textospor
dc.subjectmineração de dadospor
dc.subjectmapas auto-organizáveispor
dc.subjectredes neurais artificiaispor
dc.subjectmodelagem de tópicospor
dc.subject.cnpqCNPQ::ENGENHARIASpor
dc.titleAnálise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicospor
dc.typeDissertaçãopor
local.contributor.board1Lopes, Paulo Batista
local.contributor.board1Latteshttp://lattes.cnpq.br/1678715490240349 / https://orcid.org/0000-0002-8070-1688por
local.contributor.board2Colugnati, Fernando Antonio Basile
local.contributor.board2Latteshttp://lattes.cnpq.br/1622643885752324 / https://orcid.org/0000-0002-8288-203Xpor
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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