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dc.creatorAndrade Júnior, William Teles de
dc.date.accessioned2024-10-25T15:46:24Z
dc.date.available2024-10-25T15:46:24Z
dc.date.issued2023-12-06
dc.identifier.citationANDRADE JÚNIOR, William Teles de. Avaliando o desempenho de modelos generativos de dados para classificação de notícias falsas mediante modelo matemático auxiliado por algoritmo genético. Orientador: Antônio Correia de Sá Barreto Neto. 2023. Artigo (Tecnólogo em Análise e Desenvolvimento de Sistemas) - Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco - Campus Paulista, Paulista, PE, 2023. 20 p.pt_BR
dc.identifier.urihttps://repositorio.ifpe.edu.br/xmlui/handle/123456789/1411
dc.description.abstractThis paper presents a comparative analysis of the performance of a fake news classification system using a mathematical model improved by a genetic algorithm. The objective of this study is to investigate the potential of models to generate synthetic Instituto Federal de Pernambuco. Campus Paulista. Curso de Análise e Desenvolvimento de Sistemas. 06 de dezembro de 2023. 2 data for this fake news detection approach. The research compares the results obtained from a real dataset, containing news information, with those obtained from four synthetic datasets generated using GAN, VAE, DDPM and SMOTE. The results of the study indicate that classification performance improved when using artificial data, with an accuracy score of approximately 87%. These results suggest that synthetic data can serve as valuable tools for improving fake news classification performance.pt_BR
dc.format.extent20 p.pt_BR
dc.languagept_BRpt_BR
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dc.rightsAcesso Abertopt_BR
dc.rightsAn error occurred on the license name.*
dc.rights.uriAn error occurred getting the license - uri.*
dc.subjectModelos Generativospt_BR
dc.subjectFake Newspt_BR
dc.subjectAlgoritmo Genéticopt_BR
dc.subjectClassificaçãopt_BR
dc.titleAvaliando o desempenho de modelos generativos de dados para classificação de notícias falsas mediante modelo matemático auxiliado por algoritmo Genético.pt_BR
dc.typeArticlept_BR
dc.creator.Latteshttp://lattes.cnpq.br/1956777595001855pt_BR
dc.contributor.advisor1Barreto Neto, Antônio Correia de Sá
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2773609778338983pt_BR
dc.contributor.advisor-co1Silva, Rodrigo Cesar Lira da
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/2442224050349612pt_BR
dc.contributor.referee1Oliveira, Flávio Rosendo da Silva
dc.contributor.referee2Silva, João Gabriel Rocha
dc.contributor.referee1Latteshttp://lattes.cnpq.br/6828380394080049pt_BR
dc.contributor.referee2Latteshttp://lattes.cnpq.br/4555578101519491pt_BR
dc.publisher.departmentPaulistapt_BR
dc.publisher.countryBrasilpt_BR
dc.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAOpt_BR
dc.description.resumoEste artigo apresenta uma análise comparativa do desempenho de um sistema de classificação de notícias falsas utilizando um modelo matemático aprimorado por um algoritmo genético. O objetivo deste estudo é investigar o potencial dos modelos generativos de dados sintéticos para esta abordagem de detecção de notícias falsas. A pesquisa compara os resultados obtidos de um conjunto de dados real, contendo informações das notícias, com aqueles obtidos de quatro conjuntos de dados sintéticos gerados usando redes adversárias generativas, autoencoders variacionais, modelo probabilístico de difusão de redução de ruído e técnica de sobre-amostragem minoritária sintética. Os resultados do estudo indicam que o desempenho da classificação obteve uma melhora quando usado os dados artificiais, com uma pontuação de acurácia de, aproximadamente, 87%. Esses resultados sugerem que dados sintéticos, podem servir como ferramentas valiosas para melhorar o desempenho classificação de notícias falsas.pt_BR


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