Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development

  • Daniel Leal Souza CESUPA-ARGO / NPCA
  • Isadora Mendes dos Santos NPCA / UFRA
  • Caio Johnston Soares CESUPA-ARGO / NPCA / IBM
  • José Pires de Oliveira Neto NPCA / UFRA
  • Lucas Cassiano NPCA / UFRA
  • Marco Aurélio Proença Neto CESUPA-ARGO / NPCA
  • Aline Maria Pereira Cruz Ramos UFPA
  • Liliane Afonso de Oliveira UFPA
  • Flávia Luciana Guimaraes Marçal Pantoja de Araújo UFPA
  • Fabrício Almeida Araújo NPCA / UFRA
  • Gilberto Nerino de Souza Junior NPCA / UFRA
  • Marcus de Barros Braga NPCA / UFRA

Resumo


Child development Assessment is a multifaceted process that incorporates variables of diverse origins in order to identify developmental delays. The present study proposes a hybrid artificial intelligence model, combining first-order logic and fuzzy logic to identify delays in child development. The usage of first-order logic facilitates the integration of large volumes of data, promoting a holistic view. The usage of fuzzy logic enables the treatment of uncertainties and a detailed analysis of variables. The results indicate that the proposed model is effective in mapping delays in child development, as well as in using the data obtained to map the child’s evolution trend.
Publicado
17/11/2024
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SOUZA, Daniel Leal et al. Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 13. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 196-208. ISSN 2643-6264.