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Artigo de periodico
Machine learning in marine ecology: an overview of techniques and applications (2023)
Rubbens, Peter ; Brodie, Stephanie ; Cordier, Tristan ; Barcellos, Diogo Destro ; Devos, Paul ; Fernandes-Salvador, Jose A. ; Fincham, Jennifer I. ; Gomes, Alessandra C. S.; Handegard, Nils Olav ; Howell, Kerry ; Jamet, Cédric ; Kartveit, Kyrre Heldal ; Moustahfid, Hassan ; Parcerisas, Clea ; Politikos, Dimitris ; Sauzède, Raphaëlle ; Sokolova, Maria ; Uusitalo, Laura ; Bulcke, Laure Van den ; Helmond, Aloysius T M van ; Watson, Jordan T. ; Welch, Heather ; Beltran-Perez, Oscar ; Chaffron, Samuel ; Greenberg, David S. ; Kühn, Bernhard; Kiko, Rainer ; Lo, Madiop; Lopes, Rubens Mendes ; Möller, Klas Ove ; Michaels, William ; Pala, Ahmet ; Romagnan, Jean-Baptiste ; Schuchert, Pia ; Seydi, Vahid ; Villasante, Sebastian ; Malde, Ketil ; Irisson, Jean-Olivier
Source: ICES Journal of Marine Science (print). Unidade: IO
Subjects: ACÚSTICA, ECOLOGIA, APRENDIZADO COMPUTACIONAL, SENSORIAMENTO REMOTO
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ABNT
RUBBENS, Peter et al. Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science (print), v. 80, n. 7, p. 1829-1853, 2023Tradução . . Disponível em: https://doi.org/10.1093/icesjms/fsad100. Acesso em: 05 nov. 2024.
APA
Rubbens, P., Brodie, S., Cordier, T., Barcellos, D. D., Devos, P., Fernandes-Salvador, J. A., et al. (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science (print), 80( 7), 1829-1853. doi:10.1093/icesjms/fsad100
NLM
Rubbens P, Brodie S, Cordier T, Barcellos DD, Devos P, Fernandes-Salvador JA, Fincham JI, Gomes ACS, Handegard NO, Howell K, Jamet C, Kartveit KH, Moustahfid H, Parcerisas C, Politikos D, Sauzède R, Sokolova M, Uusitalo L, Bulcke LV den, Helmond ATM van, Watson JT, Welch H, Beltran-Perez O, Chaffron S, Greenberg DS, Kühn B, Kiko R, Lo M, Lopes RM, Möller KO, Michaels W, Pala A, Romagnan J-B, Schuchert P, Seydi V, Villasante S, Malde K, Irisson J-O. Machine learning in marine ecology: an overview of techniques and applications [Internet]. ICES Journal of Marine Science (print). 2023 ; 80( 7): 1829-1853.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1093/icesjms/fsad100
Vancouver
Rubbens P, Brodie S, Cordier T, Barcellos DD, Devos P, Fernandes-Salvador JA, Fincham JI, Gomes ACS, Handegard NO, Howell K, Jamet C, Kartveit KH, Moustahfid H, Parcerisas C, Politikos D, Sauzède R, Sokolova M, Uusitalo L, Bulcke LV den, Helmond ATM van, Watson JT, Welch H, Beltran-Perez O, Chaffron S, Greenberg DS, Kühn B, Kiko R, Lo M, Lopes RM, Möller KO, Michaels W, Pala A, Romagnan J-B, Schuchert P, Seydi V, Villasante S, Malde K, Irisson J-O. Machine learning in marine ecology: an overview of techniques and applications [Internet]. ICES Journal of Marine Science (print). 2023 ; 80( 7): 1829-1853.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1093/icesjms/fsad100
Artigo de periodico
A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition (2023)
Ribeiro, Kandice Suane Barros ; Nuñez, Henrique Hiram Libutti; Venter, Giuliana Sardi ; Doude, Haley R ; Coelho, Reginaldo Teixeira
Source: International Journal of Advanced Manufacturing Technology. Unidades: EESC, ICMC
Subjects: MANUFATURA ADITIVA, PROCESSAMENTO DE IMAGENS, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
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ABNT
RIBEIRO, Kandice Suane Barros et al. A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition. International Journal of Advanced Manufacturing Technology, v. 127, n. 7-8, p. 3183-3194, 2023Tradução . . Disponível em: https://doi.org/10.1007/s00170-023-11582-z. Acesso em: 05 nov. 2024.
APA
Ribeiro, K. S. B., Nuñez, H. H. L., Venter, G. S., Doude, H. R., & Coelho, R. T. (2023). A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition. International Journal of Advanced Manufacturing Technology, 127( 7-8), 3183-3194. doi:10.1007/s00170-023-11582-z
NLM
Ribeiro KSB, Nuñez HHL, Venter GS, Doude HR, Coelho RT. A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition [Internet]. International Journal of Advanced Manufacturing Technology. 2023 ; 127( 7-8): 3183-3194.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/s00170-023-11582-z
Vancouver
Ribeiro KSB, Nuñez HHL, Venter GS, Doude HR, Coelho RT. A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition [Internet]. International Journal of Advanced Manufacturing Technology. 2023 ; 127( 7-8): 3183-3194.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/s00170-023-11582-z
Artigo de periodico
Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning (2023)
Mochi, Victor H; Nuñez, Henrique Hiram Libutti; Ribeiro, Kandice Suane Barros ; Venter, Giuliana Sardi
Source: International Journal of Advanced Manufacturing Technology. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PROCESSAMENTO DE IMAGENS, MANUFATURA ADITIVA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MOCHI, Victor H et al. Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning. International Journal of Advanced Manufacturing Technology, v. 129, n. 11-12, p. 5643-5654, 2023Tradução . . Disponível em: https://doi.org/10.1007/s00170-023-12658-6. Acesso em: 05 nov. 2024.
APA
Mochi, V. H., Nuñez, H. H. L., Ribeiro, K. S. B., & Venter, G. S. (2023). Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning. International Journal of Advanced Manufacturing Technology, 129( 11-12), 5643-5654. doi:10.1007/s00170-023-12658-6
NLM
Mochi VH, Nuñez HHL, Ribeiro KSB, Venter GS. Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning [Internet]. International Journal of Advanced Manufacturing Technology. 2023 ; 129( 11-12): 5643-5654.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/s00170-023-12658-6
Vancouver
Mochi VH, Nuñez HHL, Ribeiro KSB, Venter GS. Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning [Internet]. International Journal of Advanced Manufacturing Technology. 2023 ; 129( 11-12): 5643-5654.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1007/s00170-023-12658-6
Artigo de periodico
Hierarchical classification of pollinating flying insects under changing environments (2022)
Parmezan, Antonio Rafael Sabino ; Souza, Vinícius Mourão Alves de ; Seth, Arpita; Zliobaite, Indre ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Ecological Informatics. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, HYMENOPTERA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PARMEZAN, Antonio Rafael Sabino et al. Hierarchical classification of pollinating flying insects under changing environments. Ecological Informatics, v. 70, p. Se 2022, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.ecoinf.2022.101751. Acesso em: 05 nov. 2024.
APA
Parmezan, A. R. S., Souza, V. M. A. de, Seth, A., Zliobaite, I., & Batista, G. E. de A. P. A. (2022). Hierarchical classification of pollinating flying insects under changing environments. Ecological Informatics, 70, Se 2022. doi:10.1016/j.ecoinf.2022.101751
NLM
Parmezan ARS, Souza VMA de, Seth A, Zliobaite I, Batista GE de APA. Hierarchical classification of pollinating flying insects under changing environments [Internet]. Ecological Informatics. 2022 ; 70 Se 2022.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1016/j.ecoinf.2022.101751
Vancouver
Parmezan ARS, Souza VMA de, Seth A, Zliobaite I, Batista GE de APA. Hierarchical classification of pollinating flying insects under changing environments [Internet]. Ecological Informatics. 2022 ; 70 Se 2022.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1016/j.ecoinf.2022.101751
Artigo de periodico
Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer (2018)
Carnielli, Carolina Moretto; Macedo, Carolina Carneiro Soares; Rossi, Tatiane de; Granato, Daniela Campos; Rivera, César; Domingues, Romênia Ramos; Pauletti, Bianca Alves; Yokoo, Sami; Heberle, Henry; Busso-Lopes, Ariane Fidelis; Cervigne, Nilva Karla; Sawazaki-Caloni, Iris; Meirelles, Gabriela Vaz; Marchi, Fabio Albuquerque; Telles, Guilherme Pimentel; Minghim, Rosane ; Ribeiro, Ana Carolina Prado; Brandão, Thaís Bianca; Castro Jr, Gilberto de; González-Arriagada, Wilfredo Alejandro; Gomes, Alexandre; Penteado, Fabio; Santos-Silva, Alan Roger; Lopes, Márcio Ajudarte; Rodrigues, Priscila Campioni; Sundquist, Elias; Salo, Tuula; Silva, Sabrina Daniela da; Alaoui-Jamali, Moulay A; Graner, Edgard; Fox, Jay W; Coletta, Ricardo Della; Leme, Adriana Franco Paes
Source: Nature Communications. Unidade: ICMC
Subjects: NEOPLASIAS BUCAIS, PROTEÔMICA, APRENDIZADO COMPUTACIONAL, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARNIELLI, Carolina Moretto et al. Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer. Nature Communications, v. 9, p. 1-17, 2018Tradução . . Disponível em: https://doi.org/10.1038/s41467-018-05696-2. Acesso em: 05 nov. 2024.
APA
Carnielli, C. M., Macedo, C. C. S., Rossi, T. de, Granato, D. C., Rivera, C., Domingues, R. R., et al. (2018). Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer. Nature Communications, 9, 1-17. doi:10.1038/s41467-018-05696-2
NLM
Carnielli CM, Macedo CCS, Rossi T de, Granato DC, Rivera C, Domingues RR, Pauletti BA, Yokoo S, Heberle H, Busso-Lopes AF, Cervigne NK, Sawazaki-Caloni I, Meirelles GV, Marchi FA, Telles GP, Minghim R, Ribeiro ACP, Brandão TB, Castro Jr G de, González-Arriagada WA, Gomes A, Penteado F, Santos-Silva AR, Lopes MA, Rodrigues PC, Sundquist E, Salo T, Silva SD da, Alaoui-Jamali MA, Graner E, Fox JW, Coletta RD, Leme AFP. Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer [Internet]. Nature Communications. 2018 ; 9 1-17.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1038/s41467-018-05696-2
Vancouver
Carnielli CM, Macedo CCS, Rossi T de, Granato DC, Rivera C, Domingues RR, Pauletti BA, Yokoo S, Heberle H, Busso-Lopes AF, Cervigne NK, Sawazaki-Caloni I, Meirelles GV, Marchi FA, Telles GP, Minghim R, Ribeiro ACP, Brandão TB, Castro Jr G de, González-Arriagada WA, Gomes A, Penteado F, Santos-Silva AR, Lopes MA, Rodrigues PC, Sundquist E, Salo T, Silva SD da, Alaoui-Jamali MA, Graner E, Fox JW, Coletta RD, Leme AFP. Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer [Internet]. Nature Communications. 2018 ; 9 1-17.[citado 2024 nov. 05 ] Available from: https://doi.org/10.1038/s41467-018-05696-2

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