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Artigo de periodico
On the transient and equilibrium features of growing fractal complex networks (2024)
Benatti, Alexandre ; Costa, Luciano da Fontoura
Source: Chaos, Solitons and Fractals. Unidade: IFSC
Subjects: RECONHECIMENTO DE PADRÕES, FRACTAIS, FÍSICA COMPUTACIONAL, REDES COMPLEXAS
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ABNT
BENATTI, Alexandre e COSTA, Luciano da Fontoura. On the transient and equilibrium features of growing fractal complex networks. Chaos, Solitons and Fractals, v. 183, p. 114904-1-114904-7, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.chaos.2024.114904. Acesso em: 10 out. 2024.
APA
Benatti, A., & Costa, L. da F. (2024). On the transient and equilibrium features of growing fractal complex networks. Chaos, Solitons and Fractals, 183, 114904-1-114904-7. doi:10.1016/j.chaos.2024.114904
NLM
Benatti A, Costa L da F. On the transient and equilibrium features of growing fractal complex networks [Internet]. Chaos, Solitons and Fractals. 2024 ; 183 114904-1-114904-7.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.chaos.2024.114904
Vancouver
Benatti A, Costa L da F. On the transient and equilibrium features of growing fractal complex networks [Internet]. Chaos, Solitons and Fractals. 2024 ; 183 114904-1-114904-7.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.chaos.2024.114904
Artigo de periodico
Color-texture classification based on spatio-spectral complex network representations (2024)
Ribas, Lucas Correia ; Scabini, Leonardo Felipe dos Santos ; Condori, Rayner Harold Montes ; Bruno, Odemir Martinez
Source: Physica A. Unidades: IFSC, ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE PADRÕES, APRENDIZAGEM PROFUNDA, REDES COMPLEXAS, TEXTURA
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ABNT
RIBAS, Lucas Correia et al. Color-texture classification based on spatio-spectral complex network representations. Physica A, v. 635, p. 129518-1-129518-15, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2024.129518. Acesso em: 10 out. 2024.
APA
Ribas, L. C., Scabini, L. F. dos S., Condori, R. H. M., & Bruno, O. M. (2024). Color-texture classification based on spatio-spectral complex network representations. Physica A, 635, 129518-1-129518-15. doi:10.1016/j.physa.2024.129518
NLM
Ribas LC, Scabini LF dos S, Condori RHM, Bruno OM. Color-texture classification based on spatio-spectral complex network representations [Internet]. Physica A. 2024 ; 635 129518-1-129518-15.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2024.129518
Vancouver
Ribas LC, Scabini LF dos S, Condori RHM, Bruno OM. Color-texture classification based on spatio-spectral complex network representations [Internet]. Physica A. 2024 ; 635 129518-1-129518-15.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2024.129518
Artigo de periodico
A network classification method based on density time evolution patterns extracted from network automata (2024)
Zielinski, Kallil Miguel Caparroz; Ribas, Lucas Correia ; Machicao, Jeaneth ; Bruno, Odemir Martinez
Source: Pattern Recognition. Unidades: IFSC, EP
Subjects: REDES COMPLEXAS, REDES NEURAIS, VISÃO COMPUTACIONAL, TEXTURA
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ABNT
ZIELINSKI, Kallil Miguel Caparroz et al. A network classification method based on density time evolution patterns extracted from network automata. Pattern Recognition, v. 146, p. 109802-1-109802-13 + supplementary materials, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.patcog.2023.109946. Acesso em: 10 out. 2024.
APA
Zielinski, K. M. C., Ribas, L. C., Machicao, J., & Bruno, O. M. (2024). A network classification method based on density time evolution patterns extracted from network automata. Pattern Recognition, 146, 109802-1-109802-13 + supplementary materials. doi:10.1016/j.patcog.2023.109946
NLM
Zielinski KMC, Ribas LC, Machicao J, Bruno OM. A network classification method based on density time evolution patterns extracted from network automata [Internet]. Pattern Recognition. 2024 ; 146 109802-1-109802-13 + supplementary materials.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2023.109946
Vancouver
Zielinski KMC, Ribas LC, Machicao J, Bruno OM. A network classification method based on density time evolution patterns extracted from network automata [Internet]. Pattern Recognition. 2024 ; 146 109802-1-109802-13 + supplementary materials.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2023.109946
Artigo de periodico
Learning a complex network representation for shape classification (2024)
Ribas, Lucas Correia ; Bruno, Odemir Martinez
Source: Pattern Recognition. Unidade: IFSC
Subjects: REDES COMPLEXAS, REDES NEURAIS, VISÃO COMPUTACIONAL, TEXTURA, RECONHECIMENTO DE PADRÕES
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ABNT
RIBAS, Lucas Correia e BRUNO, Odemir Martinez. Learning a complex network representation for shape classification. Pattern Recognition, v. 154, p. 110566-1-110566-10 + supplementary data, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.patcog.2024.110566. Acesso em: 10 out. 2024.
APA
Ribas, L. C., & Bruno, O. M. (2024). Learning a complex network representation for shape classification. Pattern Recognition, 154, 110566-1-110566-10 + supplementary data. doi:10.1016/j.patcog.2024.110566
NLM
Ribas LC, Bruno OM. Learning a complex network representation for shape classification [Internet]. Pattern Recognition. 2024 ; 154 110566-1-110566-10 + supplementary data.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2024.110566
Vancouver
Ribas LC, Bruno OM. Learning a complex network representation for shape classification [Internet]. Pattern Recognition. 2024 ; 154 110566-1-110566-10 + supplementary data.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2024.110566
Artigo de periodico
RADAM: texture recognition through randomized aggregated encoding of deep activation maps (2023)
Scabini, Leonardo Felipe dos Santos ; Zielinski, Kallil Miguel Caparroz; Ribas, Lucas Correia ; Gonçalves, Wesley Nunes ; Baets, Bernard De ; Bruno, Odemir Martinez
Source: Pattern Recognition. Unidade: IFSC
Subjects: REDES COMPLEXAS, REDES NEURAIS, VISÃO COMPUTACIONAL, TEXTURA
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ABNT
SCABINI, Leonardo Felipe dos Santos et al. RADAM: texture recognition through randomized aggregated encoding of deep activation maps. Pattern Recognition, v. No 2023, p. 109802-1-109802-13 + supplementary materials, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.patcog.2023.109802. Acesso em: 10 out. 2024.
APA
Scabini, L. F. dos S., Zielinski, K. M. C., Ribas, L. C., Gonçalves, W. N., Baets, B. D., & Bruno, O. M. (2023). RADAM: texture recognition through randomized aggregated encoding of deep activation maps. Pattern Recognition, No 2023, 109802-1-109802-13 + supplementary materials. doi:10.1016/j.patcog.2023.109802
NLM
Scabini LF dos S, Zielinski KMC, Ribas LC, Gonçalves WN, Baets BD, Bruno OM. RADAM: texture recognition through randomized aggregated encoding of deep activation maps [Internet]. Pattern Recognition. 2023 ; No 2023 109802-1-109802-13 + supplementary materials.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2023.109802
Vancouver
Scabini LF dos S, Zielinski KMC, Ribas LC, Gonçalves WN, Baets BD, Bruno OM. RADAM: texture recognition through randomized aggregated encoding of deep activation maps [Internet]. Pattern Recognition. 2023 ; No 2023 109802-1-109802-13 + supplementary materials.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.patcog.2023.109802
Artigo de periodico
Text characterization based on recurrence networks (2023)
Souza, Bárbara Côrtes e ; Silva, Filipi Nascimento ; Arruda, Henrique Ferraz de ; Silva, Giovana Daniele da ; Costa, Luciano da Fontoura ; Amancio, Diego Raphael
Source: Information Sciences. Unidades: IFSC, ICMC
Subjects: REDES COMPLEXAS, GEOMETRIA E MODELAGEM COMPUTACIONAL
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ABNT
SOUZA, Bárbara Côrtes e et al. Text characterization based on recurrence networks. Information Sciences, v. 641, p. 119124-1-119124-15, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2023.119124. Acesso em: 10 out. 2024.
APA
Souza, B. C. e, Silva, F. N., Arruda, H. F. de, Silva, G. D. da, Costa, L. da F., & Amancio, D. R. (2023). Text characterization based on recurrence networks. Information Sciences, 641, 119124-1-119124-15. doi:10.1016/j.ins.2023.119124
NLM
Souza BC e, Silva FN, Arruda HF de, Silva GD da, Costa L da F, Amancio DR. Text characterization based on recurrence networks [Internet]. Information Sciences. 2023 ; 641 119124-1-119124-15.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.ins.2023.119124
Vancouver
Souza BC e, Silva FN, Arruda HF de, Silva GD da, Costa L da F, Amancio DR. Text characterization based on recurrence networks [Internet]. Information Sciences. 2023 ; 641 119124-1-119124-15.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.ins.2023.119124
Artigo de periodico
Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks (2023)
Neiva, Mariane Barros ; Bruno, Odemir Martinez
Source: Physica A. Unidades: IFSC, ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE PADRÕES, APRENDIZAGEM PROFUNDA, REDES COMPLEXAS
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ABNT
NEIVA, Mariane Barros e BRUNO, Odemir Martinez. Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks. Physica A, v. 626, p. 129086-1-129086-11, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2023.129086. Acesso em: 10 out. 2024.
APA
Neiva, M. B., & Bruno, O. M. (2023). Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks. Physica A, 626, 129086-1-129086-11. doi:10.1016/j.physa.2023.129086
NLM
Neiva MB, Bruno OM. Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks [Internet]. Physica A. 2023 ; 626 129086-1-129086-11.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2023.129086
Vancouver
Neiva MB, Bruno OM. Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks [Internet]. Physica A. 2023 ; 626 129086-1-129086-11.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2023.129086
Artigo de periodico
Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning (2023)
Pineda, Aruane Mello ; Rodrigues, Francisco Aparecido ; Alves, Caroline Lourenço ; Möckel, Michael; Oliveira, Thaise Graziele Lima de ; Porto, Joel Augusto Moura
Source: Journal of Complex Networks. Unidades: ICMC, IFSC
Subjects: TECNOLOGIAS DA SAÚDE, APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, DOENÇAS VASCULARES
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ABNT
PINEDA, Aruane Mello et al. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning. Journal of Complex Networks, v. 11, n. 5, p. cnad030-1-cnad030-21, 2023Tradução . . Disponível em: https://doi.org/10.1093/comnet/cnad030. Acesso em: 10 out. 2024.
APA
Pineda, A. M., Rodrigues, F. A., Alves, C. L., Möckel, M., Oliveira, T. G. L. de, & Porto, J. A. M. (2023). Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning. Journal of Complex Networks, 11( 5), cnad030-1-cnad030-21. doi:10.1093/comnet/cnad030
NLM
Pineda AM, Rodrigues FA, Alves CL, Möckel M, Oliveira TGL de, Porto JAM. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning [Internet]. Journal of Complex Networks. 2023 ; 11( 5): cnad030-1-cnad030-21.[citado 2024 out. 10 ] Available from: https://doi.org/10.1093/comnet/cnad030
Vancouver
Pineda AM, Rodrigues FA, Alves CL, Möckel M, Oliveira TGL de, Porto JAM. Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning [Internet]. Journal of Complex Networks. 2023 ; 11( 5): cnad030-1-cnad030-21.[citado 2024 out. 10 ] Available from: https://doi.org/10.1093/comnet/cnad030
Artigo de periodico
Quantifying the hierarchical adherence of modular documents (2023)
Benatti, Alexandre ; Brito, Ana Caroline Medeiros ; Amancio, Diego Raphael ; Costa, Luciano da Fontoura
Source: Journal of Physics : Complexity. Unidades: ICMC, IFSC, IME
Subjects: REDES COMPLEXAS, PROCESSAMENTO DE LINGUAGEM NATURAL
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ABNT
BENATTI, Alexandre et al. Quantifying the hierarchical adherence of modular documents. Journal of Physics : Complexity, v. 4, n. 4, p. 045008-01-045008-18, 2023Tradução . . Disponível em: https://doi.org/10.1088/2632-072X/ad0a9b. Acesso em: 10 out. 2024.
APA
Benatti, A., Brito, A. C. M., Amancio, D. R., & Costa, L. da F. (2023). Quantifying the hierarchical adherence of modular documents. Journal of Physics : Complexity, 4( 4), 045008-01-045008-18. doi:10.1088/2632-072X/ad0a9b
NLM
Benatti A, Brito ACM, Amancio DR, Costa L da F. Quantifying the hierarchical adherence of modular documents [Internet]. Journal of Physics : Complexity. 2023 ; 4( 4): 045008-01-045008-18.[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/2632-072X/ad0a9b
Vancouver
Benatti A, Brito ACM, Amancio DR, Costa L da F. Quantifying the hierarchical adherence of modular documents [Internet]. Journal of Physics : Complexity. 2023 ; 4( 4): 045008-01-045008-18.[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/2632-072X/ad0a9b
Artigo de periodico
Cross-relation characterization of knowledge networks (2023)
Tokuda, Eric Keiji ; Lambiotte, Renaud; Costa, Luciano da Fontoura
Source: European Physical Journal B. Unidades: IFSC, IEA
Subjects: REDES COMPLEXAS, CONHECIMENTO, GEOMETRIA E MODELAGEM COMPUTACIONAL
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ABNT
TOKUDA, Eric Keiji e LAMBIOTTE, Renaud e COSTA, Luciano da Fontoura. Cross-relation characterization of knowledge networks. European Physical Journal B, v. No 2023, n. 11, p. 144-1-144-19, 2023Tradução . . Disponível em: https://doi.org/10.1140/epjb/s10051-023-00608-w. Acesso em: 10 out. 2024.
APA
Tokuda, E. K., Lambiotte, R., & Costa, L. da F. (2023). Cross-relation characterization of knowledge networks. European Physical Journal B, No 2023( 11), 144-1-144-19. doi:10.1140/epjb/s10051-023-00608-w
NLM
Tokuda EK, Lambiotte R, Costa L da F. Cross-relation characterization of knowledge networks [Internet]. European Physical Journal B. 2023 ; No 2023( 11): 144-1-144-19.[citado 2024 out. 10 ] Available from: https://doi.org/10.1140/epjb/s10051-023-00608-w
Vancouver
Tokuda EK, Lambiotte R, Costa L da F. Cross-relation characterization of knowledge networks [Internet]. European Physical Journal B. 2023 ; No 2023( 11): 144-1-144-19.[citado 2024 out. 10 ] Available from: https://doi.org/10.1140/epjb/s10051-023-00608-w
Artigo de periodico
Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia (2023)
Alves, Caroline Lourenço ; Oliveira, Thaise Graziele Lima de ; Porto, Joel Augusto Moura ; Aguiar, Patricia Maria de Carvalho ; Sena, Eduardo Pondé de ; Rodrigues, Francisco Aparecido ; Pineda, Aruane Mello ; Thielemann, Christiane
Source: Journal of Neural Engineering. Unidades: ICMC, IFSC
Subjects: TECNOLOGIAS DA SAÚDE, APRENDIZADO COMPUTACIONAL, ESQUIZOFRENIA, REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, CÉREBRO
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ABNT
ALVES, Caroline Lourenço et al. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. Journal of Neural Engineering, v. 20, n. 5, p. 056025-1-056025-28, 2023Tradução . . Disponível em: https://doi.org/10.1088/1741-2552/acf734. Acesso em: 10 out. 2024.
APA
Alves, C. L., Oliveira, T. G. L. de, Porto, J. A. M., Aguiar, P. M. de C., Sena, E. P. de, Rodrigues, F. A., et al. (2023). Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. Journal of Neural Engineering, 20( 5), 056025-1-056025-28. doi:10.1088/1741-2552/acf734
NLM
Alves CL, Oliveira TGL de, Porto JAM, Aguiar PM de C, Sena EP de, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia [Internet]. Journal of Neural Engineering. 2023 ; 20( 5): 056025-1-056025-28.[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/1741-2552/acf734
Vancouver
Alves CL, Oliveira TGL de, Porto JAM, Aguiar PM de C, Sena EP de, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia [Internet]. Journal of Neural Engineering. 2023 ; 20( 5): 056025-1-056025-28.[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/1741-2552/acf734
Artigo de periodico
Revisiting agglomerative clustering (2022)
Tokuda, Eric Keiji ; Comin, Cesar Henrique ; Costa, Luciano da Fontoura
Source: Physica A. Unidade: IFSC
Subjects: REDES COMPLEXAS, CLUSTERS, MODELAGEM DE DADOS
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ABNT
TOKUDA, Eric Keiji e COMIN, Cesar Henrique e COSTA, Luciano da Fontoura. Revisiting agglomerative clustering. Physica A, v. 585, n. Ja 2022, p. 126433-1-126433-17, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2021.126433. Acesso em: 10 out. 2024.
APA
Tokuda, E. K., Comin, C. H., & Costa, L. da F. (2022). Revisiting agglomerative clustering. Physica A, 585( Ja 2022), 126433-1-126433-17. doi:10.1016/j.physa.2021.126433
NLM
Tokuda EK, Comin CH, Costa L da F. Revisiting agglomerative clustering [Internet]. Physica A. 2022 ; 585( Ja 2022): 126433-1-126433-17.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2021.126433
Vancouver
Tokuda EK, Comin CH, Costa L da F. Revisiting agglomerative clustering [Internet]. Physica A. 2022 ; 585( Ja 2022): 126433-1-126433-17.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2021.126433
Artigo de periodico
Modeling the effects of social distancing on the large-scale spreading of diseases (2022)
Silva, Paulo Cesar Ventura da ; Aleta, Alberto ; Rodrigues, Francisco Aparecido ; Moreno, Yamir
Source: Epidemics. Unidades: ICMC, IFSC
Subjects: MOBILIDADE URBANA, MODELAGEM DE EPIDEMIA, COMPORTAMENTO, REDES COMPLEXAS, SURTOS DE DOENÇAS, SAÚDE PÚBLICA
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ABNT
SILVA, Paulo Cesar Ventura da et al. Modeling the effects of social distancing on the large-scale spreading of diseases. Epidemics, v. 38, p. 100544-1-100544-13, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.epidem.2022.100544. Acesso em: 10 out. 2024.
APA
Silva, P. C. V. da, Aleta, A., Rodrigues, F. A., & Moreno, Y. (2022). Modeling the effects of social distancing on the large-scale spreading of diseases. Epidemics, 38, 100544-1-100544-13. doi:10.1016/j.epidem.2022.100544
NLM
Silva PCV da, Aleta A, Rodrigues FA, Moreno Y. Modeling the effects of social distancing on the large-scale spreading of diseases [Internet]. Epidemics. 2022 ; 38 100544-1-100544-13.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.epidem.2022.100544
Vancouver
Silva PCV da, Aleta A, Rodrigues FA, Moreno Y. Modeling the effects of social distancing on the large-scale spreading of diseases [Internet]. Epidemics. 2022 ; 38 100544-1-100544-13.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.epidem.2022.100544
Artigo de periodico
Learning graph representation with randomized neural network for dynamic texture classification (2022)
Ribas, Lucas Correia ; Sá Júnior, Jarbas Joaci de Mesquita; Manzanera, Antoine ; Bruno, Odemir Martinez
Source: Applied Soft Computing. Unidades: IFSC, ICMC
Subjects: REDES COMPLEXAS, VISÃO COMPUTACIONAL, REDES NEURAIS
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ABNT
RIBAS, Lucas Correia et al. Learning graph representation with randomized neural network for dynamic texture classification. Applied Soft Computing, v. 114, n. Ja 2022, p. 108035-1-108035-14, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2021.108035. Acesso em: 10 out. 2024.
APA
Ribas, L. C., Sá Júnior, J. J. de M., Manzanera, A., & Bruno, O. M. (2022). Learning graph representation with randomized neural network for dynamic texture classification. Applied Soft Computing, 114( Ja 2022), 108035-1-108035-14. doi:10.1016/j.asoc.2021.108035
NLM
Ribas LC, Sá Júnior JJ de M, Manzanera A, Bruno OM. Learning graph representation with randomized neural network for dynamic texture classification [Internet]. Applied Soft Computing. 2022 ; 114( Ja 2022): 108035-1-108035-14.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.asoc.2021.108035
Vancouver
Ribas LC, Sá Júnior JJ de M, Manzanera A, Bruno OM. Learning graph representation with randomized neural network for dynamic texture classification [Internet]. Applied Soft Computing. 2022 ; 114( Ja 2022): 108035-1-108035-14.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.asoc.2021.108035
Artigo de periodico
A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative (2022)
Ribas, Lucas Correia ; Riad, Rabia ; Jennane, Rachid ; Bruno, Odemir Martinez
Source: Biomedical Signal Processing and Control. Unidades: IFSC, ICMC
Subjects: REDES COMPLEXAS, RECONHECIMENTO DE IMAGEM, TECNOLOGIAS DA SAÚDE, OSTEOARTRITE DO JOELHO
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ABNT
RIBAS, Lucas Correia et al. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative. Biomedical Signal Processing and Control, v. 222, n. Ja 2022, p. 103133-1-103133-10, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.bspc.2021.103133. Acesso em: 10 out. 2024.
APA
Ribas, L. C., Riad, R., Jennane, R., & Bruno, O. M. (2022). A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative. Biomedical Signal Processing and Control, 222( Ja 2022), 103133-1-103133-10. doi:10.1016/j.bspc.2021.103133
NLM
Ribas LC, Riad R, Jennane R, Bruno OM. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative [Internet]. Biomedical Signal Processing and Control. 2022 ; 222( Ja 2022): 103133-1-103133-10.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.bspc.2021.103133
Vancouver
Ribas LC, Riad R, Jennane R, Bruno OM. A complex network based approach for knee osteoarthritis detection: data from the Osteoarthritis initiative [Internet]. Biomedical Signal Processing and Control. 2022 ; 222( Ja 2022): 103133-1-103133-10.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.bspc.2021.103133
Artigo de periodico
Modelling how social network algorithms can influence opinion polarization (2022)
Arruda, Henrique Ferraz de ; Cardoso, Felipe Maciel; Arruda, Guilherme Ferraz de; Hernández, Alexis R.; Costa, Luciano da Fontoura ; Moreno, Yamir
Source: Information Sciences. Unidade: IFSC
Subjects: REDES COMPLEXAS, GEOMETRIA E MODELAGEM COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ARRUDA, Henrique Ferraz de et al. Modelling how social network algorithms can influence opinion polarization. Information Sciences, v. 588, p. 265-278 , 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.12.069. Acesso em: 10 out. 2024.
APA
Arruda, H. F. de, Cardoso, F. M., Arruda, G. F. de, Hernández, A. R., Costa, L. da F., & Moreno, Y. (2022). Modelling how social network algorithms can influence opinion polarization. Information Sciences, 588, 265-278 . doi:10.1016/j.ins.2021.12.069
NLM
Arruda HF de, Cardoso FM, Arruda GF de, Hernández AR, Costa L da F, Moreno Y. Modelling how social network algorithms can influence opinion polarization [Internet]. Information Sciences. 2022 ; 588 265-278 .[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.ins.2021.12.069
Vancouver
Arruda HF de, Cardoso FM, Arruda GF de, Hernández AR, Costa L da F, Moreno Y. Modelling how social network algorithms can influence opinion polarization [Internet]. Information Sciences. 2022 ; 588 265-278 .[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.ins.2021.12.069
Artigo de periodico
On hypercomplex networks (2022)
Cunha, Éverton Fernandes da; Costa, Luciano da Fontoura
Source: Physica A. Unidade: IFSC
Subjects: REDES COMPLEXAS, INTELIGÊNCIA ARTIFICIAL, MODELAGEM DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CUNHA, Éverton Fernandes da e COSTA, Luciano da Fontoura. On hypercomplex networks. Physica A, v. 591, p. 126714-1-126714-9, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2021.126714. Acesso em: 10 out. 2024.
APA
Cunha, É. F. da, & Costa, L. da F. (2022). On hypercomplex networks. Physica A, 591, 126714-1-126714-9. doi:10.1016/j.physa.2021.126714
NLM
Cunha ÉF da, Costa L da F. On hypercomplex networks [Internet]. Physica A. 2022 ; 591 126714-1-126714-9.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2021.126714
Vancouver
Cunha ÉF da, Costa L da F. On hypercomplex networks [Internet]. Physica A. 2022 ; 591 126714-1-126714-9.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2021.126714
Artigo de periodico
On similarity (2022)
Costa, Luciano da Fontoura
Source: Physica A. Unidade: IFSC
Subjects: REDES COMPLEXAS, GEOMETRIA E MODELAGEM COMPUTACIONAL, FÍSICA MATEMÁTICA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA, Luciano da Fontoura. On similarity. Physica A, v. 599, p. 127456-1- 127456-20, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.physa.2022.127456. Acesso em: 10 out. 2024.
APA
Costa, L. da F. (2022). On similarity. Physica A, 599, 127456-1- 127456-20. doi:10.1016/j.physa.2022.127456
NLM
Costa L da F. On similarity [Internet]. Physica A. 2022 ; 599 127456-1- 127456-20.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2022.127456
Vancouver
Costa L da F. On similarity [Internet]. Physica A. 2022 ; 599 127456-1- 127456-20.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.physa.2022.127456
Artigo de periodico
A similarity approach to cities and features (2022)
Costa, Luciano da Fontoura ; Tokuda, Eric Keiji
Source: European Physical Journal B. Unidade: IFSC
Subjects: TOPOLOGIA, REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA, Luciano da Fontoura e TOKUDA, Eric Keiji. A similarity approach to cities and features. European Physical Journal B, v. 95, n. 9, p. 155-1-155-14, 2022Tradução . . Disponível em: https://doi.org/10.1140/epjb/s10051-022-00420-y. Acesso em: 10 out. 2024.
APA
Costa, L. da F., & Tokuda, E. K. (2022). A similarity approach to cities and features. European Physical Journal B, 95( 9), 155-1-155-14. doi:10.1140/epjb/s10051-022-00420-y
NLM
Costa L da F, Tokuda EK. A similarity approach to cities and features [Internet]. European Physical Journal B. 2022 ; 95( 9): 155-1-155-14.[citado 2024 out. 10 ] Available from: https://doi.org/10.1140/epjb/s10051-022-00420-y
Vancouver
Costa L da F, Tokuda EK. A similarity approach to cities and features [Internet]. European Physical Journal B. 2022 ; 95( 9): 155-1-155-14.[citado 2024 out. 10 ] Available from: https://doi.org/10.1140/epjb/s10051-022-00420-y
Artigo de periodico
The impact of chaotic saddles on the synchronization of complex networks of discrete-time units (2021)
Medeiros, Everton ; Medrano-T, Rene O.; Caldas, Iberê Luiz ; Feudel, Ulrike
Source: Journal of Physics: Complexity. Unidade: IF
Subjects: CAOS (SISTEMAS DINÂMICOS), SISTEMAS DINÂMICOS (FÍSICA MATEMÁTICA), REDES COMPLEXAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MEDEIROS, Everton et al. The impact of chaotic saddles on the synchronization of complex networks of discrete-time units. Journal of Physics: Complexity, v. 2, n. 3, 2021Tradução . . Disponível em: https://doi.org/10.1088/2632-072X/abedc2. Acesso em: 10 out. 2024.
APA
Medeiros, E., Medrano-T, R. O., Caldas, I. L., & Feudel, U. (2021). The impact of chaotic saddles on the synchronization of complex networks of discrete-time units. Journal of Physics: Complexity, 2( 3). doi:10.1088/2632-072X/abedc2
NLM
Medeiros E, Medrano-T RO, Caldas IL, Feudel U. The impact of chaotic saddles on the synchronization of complex networks of discrete-time units [Internet]. Journal of Physics: Complexity. 2021 ; 2( 3):[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/2632-072X/abedc2
Vancouver
Medeiros E, Medrano-T RO, Caldas IL, Feudel U. The impact of chaotic saddles on the synchronization of complex networks of discrete-time units [Internet]. Journal of Physics: Complexity. 2021 ; 2( 3):[citado 2024 out. 10 ] Available from: https://doi.org/10.1088/2632-072X/abedc2

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