Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos
dc.contributor.advisor | Notargiacomo, Pollyana Coelho da Silva | |
dc.contributor.advisor-co1 | Silva, Leandro Augusto da | |
dc.contributor.advisor-co1Lattes | http://lattes.cnpq.br/1396385111251741 / https://orcid.org/0000-0002-8671-3102 | por |
dc.contributor.advisor1Lattes | http://lattes.cnpq.br/5131975026612008 / https://orcid.org/0000-0001-8292-1644 | por |
dc.contributor.author | Casara, Mary Adriana | |
dc.creator.Lattes | http://lattes.cnpq.br/0782937908073737 | por |
dc.date.accessioned | 2021-12-18T21:44:29Z | |
dc.date.available | 2021-12-18T21:44:29Z | |
dc.date.issued | 2020-03-24 | |
dc.description.abstract | Scientific 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.sponsorship | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior | por |
dc.description.sponsorship | Instituto Presbiteriano Mackenzie | por |
dc.format | application/pdf | * |
dc.identifier.citation | CASARA, 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, 2020 | por |
dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/28617 | |
dc.keywords | bibliometric indicators | eng |
dc.keywords | text mining | eng |
dc.keywords | data mining | eng |
dc.keywords | self-organizing maps | eng |
dc.keywords | artificial neural networks | eng |
dc.keywords | topic modeling | eng |
dc.language | por | por |
dc.publisher | Universidade Presbiteriana Mackenzie | por |
dc.rights | Acesso Aberto | por |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | indicadores bibliométricos | por |
dc.subject | mineração de textos | por |
dc.subject | mineração de dados | por |
dc.subject | mapas auto-organizáveis | por |
dc.subject | redes neurais artificiais | por |
dc.subject | modelagem de tópicos | por |
dc.subject.cnpq | CNPQ::ENGENHARIAS | por |
dc.title | Análise da produção científica dos cursos de pós-graduação utilizando redes neurais e modelagem de tópicos | por |
dc.type | Dissertação | por |
local.contributor.board1 | Lopes, Paulo Batista | |
local.contributor.board1Lattes | http://lattes.cnpq.br/1678715490240349 / https://orcid.org/0000-0002-8070-1688 | por |
local.contributor.board2 | Colugnati, Fernando Antonio Basile | |
local.contributor.board2Lattes | http://lattes.cnpq.br/1622643885752324 / https://orcid.org/0000-0002-8288-203X | por |
local.publisher.country | Brasil | por |
local.publisher.department | Escola de Engenharia Mackenzie (EE) | por |
local.publisher.initials | UPM | por |
local.publisher.program | Engenharia Elétrica | por |