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  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: RECONHECIMENTO DE TEXTO, FRAMEWORKS, PLATAFORMA DIGITAL, CORRUPÇÃO DE MENORES

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      MILON-FLORES, Daniela Fernanda e CORDEIRO, Robson Leonardo Ferreira. How to take advantage of behavioral features for the early detection of grooming in online conversations. Knowledge-Based Systems, v. 240, p. 1-29, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2021.108017. Acesso em: 10 nov. 2024.
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      Milon-Flores, D. F., & Cordeiro, R. L. F. (2022). How to take advantage of behavioral features for the early detection of grooming in online conversations. Knowledge-Based Systems, 240, 1-29. doi:10.1016/j.knosys.2021.108017
    • NLM

      Milon-Flores DF, Cordeiro RLF. How to take advantage of behavioral features for the early detection of grooming in online conversations [Internet]. Knowledge-Based Systems. 2022 ; 240 1-29.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2021.108017
    • Vancouver

      Milon-Flores DF, Cordeiro RLF. How to take advantage of behavioral features for the early detection of grooming in online conversations [Internet]. Knowledge-Based Systems. 2022 ; 240 1-29.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2021.108017
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: RECONHECIMENTO DE TEXTO, ALGORITMOS ÚTEIS E ESPECÍFICOS, WEB SEMÂNTICA

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      ZANON, André Levi e ROCHA, Leonardo Chaves Dutra da e MANZATO, Marcelo Garcia. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data. Knowledge-Based Systems, v. 252, p. Se 2022, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2022.109333. Acesso em: 10 nov. 2024.
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      Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2022). Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data. Knowledge-Based Systems, 252, Se 2022. doi:10.1016/j.knosys.2022.109333
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      Zanon AL, Rocha LCD da, Manzato MG. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data [Internet]. Knowledge-Based Systems. 2022 ; 252 Se 2022.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2022.109333
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      Zanon AL, Rocha LCD da, Manzato MG. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data [Internet]. Knowledge-Based Systems. 2022 ; 252 Se 2022.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2022.109333
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), COMPUTAÇÃO RECONFIGURÁVEL, TRÁFEGO RODOVIÁRIO

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      ALVES, Matheus Aparecido do Carmo e CORDEIRO, Robson Leonardo Ferreira. Effective and unburdensome forecast of highway traffic flow with adaptive computing. Knowledge-Based Systems, v. 212, n. Ja 2021, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2020.106603. Acesso em: 10 nov. 2024.
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      Alves, M. A. do C., & Cordeiro, R. L. F. (2021). Effective and unburdensome forecast of highway traffic flow with adaptive computing. Knowledge-Based Systems, 212( Ja 2021), 1-13. doi:10.1016/j.knosys.2020.106603
    • NLM

      Alves MA do C, Cordeiro RLF. Effective and unburdensome forecast of highway traffic flow with adaptive computing [Internet]. Knowledge-Based Systems. 2021 ; 212( Ja 2021): 1-13.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.106603
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      Alves MA do C, Cordeiro RLF. Effective and unburdensome forecast of highway traffic flow with adaptive computing [Internet]. Knowledge-Based Systems. 2021 ; 212( Ja 2021): 1-13.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.106603
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: REDES COMPLEXAS, VISUALIZAÇÃO

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      VALEJO, Alan Demetrius Baria et al. A coarsening method for bipartite networks via weight-constrained label propagation. Knowledge-Based Systems, v. 195, p. 1-17, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2020.105678. Acesso em: 10 nov. 2024.
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      Valejo, A. D. B., Faleiros, T., Oliveira, M. C. F. de, & Lopes, A. de A. (2020). A coarsening method for bipartite networks via weight-constrained label propagation. Knowledge-Based Systems, 195, 1-17. doi:10.1016/j.knosys.2020.105678
    • NLM

      Valejo ADB, Faleiros T, Oliveira MCF de, Lopes A de A. A coarsening method for bipartite networks via weight-constrained label propagation [Internet]. Knowledge-Based Systems. 2020 ; 195 1-17.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.105678
    • Vancouver

      Valejo ADB, Faleiros T, Oliveira MCF de, Lopes A de A. A coarsening method for bipartite networks via weight-constrained label propagation [Internet]. Knowledge-Based Systems. 2020 ; 195 1-17.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.105678
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: BANCO DE DADOS, MINERAÇÃO DE DADOS, FRACTAIS

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      OLIVEIRA, Jadson José Monteiro e CORDEIRO, Robson Leonardo Ferreira. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction?. Knowledge-Based Systems, v. 196, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2020.105777. Acesso em: 10 nov. 2024.
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      Oliveira, J. J. M., & Cordeiro, R. L. F. (2020). Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? Knowledge-Based Systems, 196, 1-14. doi:10.1016/j.knosys.2020.105777
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      Oliveira JJM, Cordeiro RLF. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? [Internet]. Knowledge-Based Systems. 2020 ; 196 1-14.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.105777
    • Vancouver

      Oliveira JJM, Cordeiro RLF. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? [Internet]. Knowledge-Based Systems. 2020 ; 196 1-14.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2020.105777
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: BANCO DE DADOS, RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE TEXTO

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      TEKLI, Joe et al. SemIndex+: a semantic indexing scheme for structured, unstructured, and partly structured data. Knowledge-Based Systems, v. 164, n. Ja 2019, p. 378-403, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2018.11.010. Acesso em: 10 nov. 2024.
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      Tekli, J., Chbeir, R., Traina, A. J. M., & Traina Junior, C. (2019). SemIndex+: a semantic indexing scheme for structured, unstructured, and partly structured data. Knowledge-Based Systems, 164( Ja 2019), 378-403. doi:10.1016/j.knosys.2018.11.010
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      Tekli J, Chbeir R, Traina AJM, Traina Junior C. SemIndex+: a semantic indexing scheme for structured, unstructured, and partly structured data [Internet]. Knowledge-Based Systems. 2019 ; 164( Ja 2019): 378-403.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.11.010
    • Vancouver

      Tekli J, Chbeir R, Traina AJM, Traina Junior C. SemIndex+: a semantic indexing scheme for structured, unstructured, and partly structured data [Internet]. Knowledge-Based Systems. 2019 ; 164( Ja 2019): 378-403.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.11.010
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: MINERAÇÃO DE DADOS, RECONHECIMENTO DE TEXTO

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      SINOARA, Roberta Akemi et al. Knowledge-enhanced document embeddings for text classification. Knowledge-Based Systems, v. 163, n. Ja 2019, p. 955-971, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2018.10.026. Acesso em: 10 nov. 2024.
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      Sinoara, R. A., Camacho-Collados, J., Rossi, R. G., Navigli, R., & Rezende, S. O. (2019). Knowledge-enhanced document embeddings for text classification. Knowledge-Based Systems, 163( Ja 2019), 955-971. doi:10.1016/j.knosys.2018.10.026
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      Sinoara RA, Camacho-Collados J, Rossi RG, Navigli R, Rezende SO. Knowledge-enhanced document embeddings for text classification [Internet]. Knowledge-Based Systems. 2019 ; 163( Ja 2019): 955-971.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.10.026
    • Vancouver

      Sinoara RA, Camacho-Collados J, Rossi RG, Navigli R, Rezende SO. Knowledge-enhanced document embeddings for text classification [Internet]. Knowledge-Based Systems. 2019 ; 163( Ja 2019): 955-971.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.10.026
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE DADOS, RECONHECIMENTO DE PADRÕES

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      GARCIA, Luís P. F et al. New label noise injection methods for the evaluation of noise filters. Knowledge-Based Systems, v. 163, n. Ja 2019, p. 693-704, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2018.09.031. Acesso em: 10 nov. 2024.
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      Garcia, L. P. F., Lehmann, J., Carvalho, A. C. P. de L. F. de, & Lorena, A. C. (2019). New label noise injection methods for the evaluation of noise filters. Knowledge-Based Systems, 163( Ja 2019), 693-704. doi:10.1016/j.knosys.2018.09.031
    • NLM

      Garcia LPF, Lehmann J, Carvalho ACP de LF de, Lorena AC. New label noise injection methods for the evaluation of noise filters [Internet]. Knowledge-Based Systems. 2019 ; 163( Ja 2019): 693-704.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.09.031
    • Vancouver

      Garcia LPF, Lehmann J, Carvalho ACP de LF de, Lorena AC. New label noise injection methods for the evaluation of noise filters [Internet]. Knowledge-Based Systems. 2019 ; 163( Ja 2019): 693-704.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.09.031
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: REDES COMPLEXAS, OTIMIZAÇÃO COMBINATÓRIA, HEURÍSTICA

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      VALEJO, Alan et al. Multilevel approach for combinatorial optimization in bipartite network. Knowledge-Based Systems, v. 151, p. 45-61, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2018.03.021. Acesso em: 10 nov. 2024.
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      Valejo, A., Oliveira, M. C. F. de, Filho, G. P. R., & Lopes, A. de A. (2018). Multilevel approach for combinatorial optimization in bipartite network. Knowledge-Based Systems, 151, 45-61. doi:10.1016/j.knosys.2018.03.021
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      Valejo A, Oliveira MCF de, Filho GPR, Lopes A de A. Multilevel approach for combinatorial optimization in bipartite network [Internet]. Knowledge-Based Systems. 2018 ; 151 45-61.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.03.021
    • Vancouver

      Valejo A, Oliveira MCF de, Filho GPR, Lopes A de A. Multilevel approach for combinatorial optimization in bipartite network [Internet]. Knowledge-Based Systems. 2018 ; 151 45-61.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2018.03.021
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE TEXTO

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      ROSSI, Rafael Geraldeli e LOPES, Alneu de Andrade e REZENDE, Solange Oliveira. Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization. Knowledge-Based Systems, v. 132, p. Se 2017, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2017.06.016. Acesso em: 10 nov. 2024.
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      Rossi, R. G., Lopes, A. de A., & Rezende, S. O. (2017). Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization. Knowledge-Based Systems, 132, Se 2017. doi:10.1016/j.knosys.2017.06.016
    • NLM

      Rossi RG, Lopes A de A, Rezende SO. Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization [Internet]. Knowledge-Based Systems. 2017 ; 132 Se 2017.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2017.06.016
    • Vancouver

      Rossi RG, Lopes A de A, Rezende SO. Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization [Internet]. Knowledge-Based Systems. 2017 ; 132 Se 2017.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2017.06.016
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      FRÍAS-BLANCO, Isvani et al. Online adaptive decision trees based on concentration inequalities. Knowledge-Based Systems, v. 104, p. 179-194, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2016.04.019. Acesso em: 10 nov. 2024.
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      Frías-Blanco, I., Campo-Ávila, J. del, Ramos-Jiménez, G., Carvalho, A. C. P. de L. F. de, Ortiz-Díaz, A., & Morales-Bueno, R. (2016). Online adaptive decision trees based on concentration inequalities. Knowledge-Based Systems, 104, 179-194. doi:10.1016/j.knosys.2016.04.019
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      Frías-Blanco I, Campo-Ávila J del, Ramos-Jiménez G, Carvalho ACP de LF de, Ortiz-Díaz A, Morales-Bueno R. Online adaptive decision trees based on concentration inequalities [Internet]. Knowledge-Based Systems. 2016 ; 104 179-194.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2016.04.019
    • Vancouver

      Frías-Blanco I, Campo-Ávila J del, Ramos-Jiménez G, Carvalho ACP de LF de, Ortiz-Díaz A, Morales-Bueno R. Online adaptive decision trees based on concentration inequalities [Internet]. Knowledge-Based Systems. 2016 ; 104 179-194.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2016.04.019
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS

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      PEREIRA, Andre Luiz Vizine e HRUSCHKA, Eduardo Raul. Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, v. 82, p. 11-19, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2015.02.016. Acesso em: 10 nov. 2024.
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      Pereira, A. L. V., & Hruschka, E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82, 11-19. doi:10.1016/j.knosys.2015.02.016
    • NLM

      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016
    • Vancouver

      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016
  • Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      GARCIA, Luís P. F et al. Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowledge-Based Systems, v. 90, p. 153-164, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2015.09.023. Acesso em: 10 nov. 2024.
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      Garcia, L. P. F., Sáez, J. A., Luengo, J., Lorena, A. C., Carvalho, A. C. P. de L. F. de, & Herrera, F. (2015). Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowledge-Based Systems, 90, 153-164. doi:10.1016/j.knosys.2015.09.023
    • NLM

      Garcia LPF, Sáez JA, Luengo J, Lorena AC, Carvalho ACP de LF de, Herrera F. Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems [Internet]. Knowledge-Based Systems. 2015 ; 90 153-164.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2015.09.023
    • Vancouver

      Garcia LPF, Sáez JA, Luengo J, Lorena AC, Carvalho ACP de LF de, Herrera F. Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems [Internet]. Knowledge-Based Systems. 2015 ; 90 153-164.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1016/j.knosys.2015.09.023

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