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Artigo de periodico
Electrofacies clustering and classification from the Ariri Formation in Santos Basin (southeastern offshore Brazil) involving unsupervised learning algorithms. (2023)
Gorla, Felipe Ferreira Luiz; Carneiro, Cleyton de Carvalho
Source: Carbonates and Evaporites. Unidade: EP
Subjects: REDES NEURAIS, ALGORITMOS
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ABNT
GORLA, Felipe Ferreira Luiz e CARNEIRO, Cleyton de Carvalho. Electrofacies clustering and classification from the Ariri Formation in Santos Basin (southeastern offshore Brazil) involving unsupervised learning algorithms. Carbonates and Evaporites, p. 38-63, 2023Tradução . . Disponível em: https://doi.org/10.1007/s13146-023-00889-3. Acesso em: 30 jun. 2024.
APA
Gorla, F. F. L., & Carneiro, C. de C. (2023). Electrofacies clustering and classification from the Ariri Formation in Santos Basin (southeastern offshore Brazil) involving unsupervised learning algorithms. Carbonates and Evaporites, 38-63. doi:10.1007/s13146-023-00889-3
NLM
Gorla FFL, Carneiro C de C. Electrofacies clustering and classification from the Ariri Formation in Santos Basin (southeastern offshore Brazil) involving unsupervised learning algorithms. [Internet]. Carbonates and Evaporites. 2023 ; 38-63.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1007/s13146-023-00889-3
Vancouver
Gorla FFL, Carneiro C de C. Electrofacies clustering and classification from the Ariri Formation in Santos Basin (southeastern offshore Brazil) involving unsupervised learning algorithms. [Internet]. Carbonates and Evaporites. 2023 ; 38-63.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1007/s13146-023-00889-3
Artigo de periodico
Self-organizing maps analysis of chemical-mineralogical gold ore characterization in support of geometallurgy (2023)
Costa, Fabrizzio Rodrigues ; Carneiro, Cleyton de Carvalho ; Ulsen, Carina
Source: Mining. Unidade: EP
Subjects: MINERALOGIA, CARACTERIZAÇÃO TECNOLÓGICA DE MINÉRIOS, OURO, REDES NEURAIS
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ABNT
COSTA, Fabrizzio Rodrigues e CARNEIRO, Cleyton de Carvalho e ULSEN, Carina. Self-organizing maps analysis of chemical-mineralogical gold ore characterization in support of geometallurgy. Mining, v. 3, n. 2, p. 230-240, 2023Tradução . . Disponível em: https://doi.org/10.3390/mining3020014. Acesso em: 30 jun. 2024.
APA
Costa, F. R., Carneiro, C. de C., & Ulsen, C. (2023). Self-organizing maps analysis of chemical-mineralogical gold ore characterization in support of geometallurgy. Mining, 3( 2), 230-240. doi:10.3390/mining3020014
NLM
Costa FR, Carneiro C de C, Ulsen C. Self-organizing maps analysis of chemical-mineralogical gold ore characterization in support of geometallurgy [Internet]. Mining. 2023 ; 3( 2): 230-240.[citado 2024 jun. 30 ] Available from: https://doi.org/10.3390/mining3020014
Vancouver
Costa FR, Carneiro C de C, Ulsen C. Self-organizing maps analysis of chemical-mineralogical gold ore characterization in support of geometallurgy [Internet]. Mining. 2023 ; 3( 2): 230-240.[citado 2024 jun. 30 ] Available from: https://doi.org/10.3390/mining3020014
Dissertação (Mestrado)
A framework for closed domain question answering systems in the low data regime. (2022)
Carmo, Vinicius Cleves de Oliveira; Cozman, Fabio Gagliardi (Orientador)
Unidade: EP
Subjects: SISTEMAS DE QUESTÕES E RESPOSTAS, RECUPERAÇÃO DA INFORMAÇÃO, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
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ABNT
CARMO, Vinicius Cleves de Oliveira. A framework for closed domain question answering systems in the low data regime. 2022. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2022. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-24052023-152815/. Acesso em: 30 jun. 2024.
APA
Carmo, V. C. de O. (2022). A framework for closed domain question answering systems in the low data regime. (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3141/tde-24052023-152815/
NLM
Carmo VC de O. A framework for closed domain question answering systems in the low data regime. [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-24052023-152815/
Vancouver
Carmo VC de O. A framework for closed domain question answering systems in the low data regime. [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-24052023-152815/
Artigo de periodico
A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil (2022)
Machicao, Jeaneth ; Specht, Alison; Vellenich, Danton ; Meneguzzi, Leandro ; David, Romain; Stall, Shelley; Ferraz, Katia ; Mabile, Laurence; O’Brien, Margaret; Corrêa, Pedro
Source: Data Science Journal. Unidades: EP, ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, COLETA DE DADOS, IMAGEM DIGITAL, INDICADORES SOCIOECONÔMICOS, REDES NEURAIS
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ABNT
MACHICAO, Jeaneth et al. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. Data Science Journal, v. 21, n. 6, p. 1–15, 2022Tradução . . Disponível em: https://doi.org/10.5334/dsj-2022-006. Acesso em: 30 jun. 2024.
APA
Machicao, J., Specht, A., Vellenich, D., Meneguzzi, L., David, R., Stall, S., et al. (2022). A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. Data Science Journal, 21( 6), 1–15. doi:10.5334/dsj-2022-006
NLM
Machicao J, Specht A, Vellenich D, Meneguzzi L, David R, Stall S, Ferraz K, Mabile L, O’Brien M, Corrêa P. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil [Internet]. Data Science Journal. 2022 ; 21( 6): 1–15.[citado 2024 jun. 30 ] Available from: https://doi.org/10.5334/dsj-2022-006
Vancouver
Machicao J, Specht A, Vellenich D, Meneguzzi L, David R, Stall S, Ferraz K, Mabile L, O’Brien M, Corrêa P. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil [Internet]. Data Science Journal. 2022 ; 21( 6): 1–15.[citado 2024 jun. 30 ] Available from: https://doi.org/10.5334/dsj-2022-006
Dissertação (Mestrado)
Design of a floating offshore structure by a deep neural network (2022)
Esteves, Fillipe Rocha Leonel; Nishimoto, Kazuo (Orientador)
Unidade: EP
Subjects: REDES NEURAIS, ESTRUTURAS OFFSHORE FLUTUANTES, APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, SUPERFÍCIES DE RESPOSTA
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ABNT
ESTEVES, Fillipe Rocha Leonel. Design of a floating offshore structure by a deep neural network. 2022. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2022. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/. Acesso em: 30 jun. 2024.
APA
Esteves, F. R. L. (2022). Design of a floating offshore structure by a deep neural network (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/
NLM
Esteves FRL. Design of a floating offshore structure by a deep neural network [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/
Vancouver
Esteves FRL. Design of a floating offshore structure by a deep neural network [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-110138/
Dissertação (Mestrado)
Vehicle classification based on binary images of optical barriers using convolutional neural networks (2022)
Barreyro, Joaquim; Yoshioka, Leopoldo Rideki (Orientador)
Unidade: EP
Subjects: SISTEMAS INTELIGENTES DE TRANSPORTES, VEÍCULOS ELÉTRICOS, REDES NEURAIS
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ABNT
BARREYRO, Joaquim. Vehicle classification based on binary images of optical barriers using convolutional neural networks. 2022. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2022. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-27052022-081725/. Acesso em: 30 jun. 2024.
APA
Barreyro, J. (2022). Vehicle classification based on binary images of optical barriers using convolutional neural networks (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3142/tde-27052022-081725/
NLM
Barreyro J. Vehicle classification based on binary images of optical barriers using convolutional neural networks [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-27052022-081725/
Vancouver
Barreyro J. Vehicle classification based on binary images of optical barriers using convolutional neural networks [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-27052022-081725/
Artigo de periodico
Neural network meta-models for FPSO motion prediction from environmental data with different platform loads (2022)
Cotrim, Lucas Pereira; Barreira, Rodrigo Augusto ; Santos, Ismael Humberto Ferreira dos; Gomi, Edson Satoshi ; Costa, Anna Helena Reali ; Tannuri, Eduardo Aoun
Source: IEEE Access. Unidade: EP
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
COTRIM, Lucas Pereira et al. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads. IEEE Access, v. 10, p. 86558-86577, 2022Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2022.3199009. Acesso em: 30 jun. 2024.
APA
Cotrim, L. P., Barreira, R. A., Santos, I. H. F. dos, Gomi, E. S., Costa, A. H. R., & Tannuri, E. A. (2022). Neural network meta-models for FPSO motion prediction from environmental data with different platform loads. IEEE Access, 10, 86558-86577. doi:10.1109/ACCESS.2022.3199009
NLM
Cotrim LP, Barreira RA, Santos IHF dos, Gomi ES, Costa AHR, Tannuri EA. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads [Internet]. IEEE Access. 2022 ;10 86558-86577.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/ACCESS.2022.3199009
Vancouver
Cotrim LP, Barreira RA, Santos IHF dos, Gomi ES, Costa AHR, Tannuri EA. Neural network meta-models for FPSO motion prediction from environmental data with different platform loads [Internet]. IEEE Access. 2022 ;10 86558-86577.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/ACCESS.2022.3199009
Trabalho de evento
A methodology to predict the effective thermal conductivity of a granular assembly using deep learning (2022)
Quintana Ruiz, Osvaldo Dario ; Campello, Eduardo de Morais Barreto
Source: Proceedings. Conference titles: Ibero-Latin-American Congress on Computational Methods in Engineering - CILAMCE. Unidade: EP
Subjects: REDES NEURAIS, INTELIGÊNCIA ARTIFICIAL, MATERIAIS GRANULARES
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ABNT
QUINTANA RUIZ, Osvaldo Dario e CAMPELLO, Eduardo de Morais Barreto. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning. 2022, Anais.. Rio de Janeiro: ABMEC, 2022. Disponível em: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf. Acesso em: 30 jun. 2024.
APA
Quintana Ruiz, O. D., & Campello, E. de M. B. (2022). A methodology to predict the effective thermal conductivity of a granular assembly using deep learning. In Proceedings. Rio de Janeiro: ABMEC. Recuperado de https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf
NLM
Quintana Ruiz OD, Campello E de MB. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning [Internet]. Proceedings. 2022 ;[citado 2024 jun. 30 ] Available from: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf
Vancouver
Quintana Ruiz OD, Campello E de MB. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning [Internet]. Proceedings. 2022 ;[citado 2024 jun. 30 ] Available from: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf
Artigo de periodico
Robust beamforming based on complex-valued convolutional neural networks for sensor arrays (2022)
Mohammadzadeh, Saeed; Nascimento, Vítor Heloiz ; Lamare, Rodrigo C; Hajarolasvadi, Noushin
Source: IEEE Signal Processing Letters. Unidade: EP
Subjects: INTELIGÊNCIA ARTIFICIAL, REDES NEURAIS, PROCESSAMENTO DE SINAIS
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ABNT
MOHAMMADZADEH, Saeed et al. Robust beamforming based on complex-valued convolutional neural networks for sensor arrays. IEEE Signal Processing Letters, v. 29, p. 2108-2112, 2022Tradução . . Disponível em: https://doi.org/10.1109/LSP.2022.3212637. Acesso em: 30 jun. 2024.
APA
Mohammadzadeh, S., Nascimento, V. H., Lamare, R. C., & Hajarolasvadi, N. (2022). Robust beamforming based on complex-valued convolutional neural networks for sensor arrays. IEEE Signal Processing Letters, 29, 2108-2112. doi:10.1109/LSP.2022.3212637
NLM
Mohammadzadeh S, Nascimento VH, Lamare RC, Hajarolasvadi N. Robust beamforming based on complex-valued convolutional neural networks for sensor arrays [Internet]. IEEE Signal Processing Letters. 2022 ;29 2108-2112.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/LSP.2022.3212637
Vancouver
Mohammadzadeh S, Nascimento VH, Lamare RC, Hajarolasvadi N. Robust beamforming based on complex-valued convolutional neural networks for sensor arrays [Internet]. IEEE Signal Processing Letters. 2022 ;29 2108-2112.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/LSP.2022.3212637
Trabalho de evento-anais periodico
Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases (2022)
Aguiar, Erikson Júlio de ; Marcomini, Karem Daiane ; Quirino, Felipe Antunes; Gutierrez, Marco Antonio ; Traina Junior, Caetano ; Traina, Agma Juci Machado
Source: Proceedings of SPIE. Conference titles: SPIE Medical Imaging. Unidades: EP, ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, TECNOLOGIAS DA SAÚDE, RADIOGRAFIA, COVID-19
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ABNT
AGUIAR, Erikson Júlio de et al. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. Disponível em: https://doi.org/10.1117/12.2611199. Acesso em: 30 jun. 2024. , 2022
APA
Aguiar, E. J. de, Marcomini, K. D., Quirino, F. A., Gutierrez, M. A., Traina Junior, C., & Traina, A. J. M. (2022). Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. doi:10.1117/12.2611199
NLM
Aguiar EJ de, Marcomini KD, Quirino FA, Gutierrez MA, Traina Junior C, Traina AJM. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases [Internet]. Proceedings of SPIE. 2022 ; 12033 120332P-1-120332P-7.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1117/12.2611199
Vancouver
Aguiar EJ de, Marcomini KD, Quirino FA, Gutierrez MA, Traina Junior C, Traina AJM. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases [Internet]. Proceedings of SPIE. 2022 ; 12033 120332P-1-120332P-7.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1117/12.2611199
Trabalho de evento-anais periodico
A deep learning approach for COVID-19 screening and localization on chest x-ray images (2022)
Marcomini, Karem Daiane ; Cardenas, Diego Armando Cardona ; Traina, Agma Juci Machado ; Krieger, Jose Eduardo ; Gutierrez, Marco Antonio
Source: Proceedings of SPIE. Conference titles: SPIE Medical Imaging. Unidades: ICMC, FM, EP
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, TECNOLOGIAS DA SAÚDE, RADIOGRAFIA, COVID-19
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARCOMINI, Karem Daiane et al. A deep learning approach for COVID-19 screening and localization on chest x-ray images. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. Disponível em: https://doi.org/10.1117/12.2613177. Acesso em: 30 jun. 2024. , 2022
APA
Marcomini, K. D., Cardenas, D. A. C., Traina, A. J. M., Krieger, J. E., & Gutierrez, M. A. (2022). A deep learning approach for COVID-19 screening and localization on chest x-ray images. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. doi:10.1117/12.2613177
NLM
Marcomini KD, Cardenas DAC, Traina AJM, Krieger JE, Gutierrez MA. A deep learning approach for COVID-19 screening and localization on chest x-ray images [Internet]. Proceedings of SPIE. 2022 ; 12033 1203327-1-1203327-9.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1117/12.2613177
Vancouver
Marcomini KD, Cardenas DAC, Traina AJM, Krieger JE, Gutierrez MA. A deep learning approach for COVID-19 screening and localization on chest x-ray images [Internet]. Proceedings of SPIE. 2022 ; 12033 1203327-1-1203327-9.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1117/12.2613177
Tese (Doutorado)
Frameworks for interpretability of deep learning-based prognostics and health management (2022)
Figueroa Barraza, Joaquín Eduardo ; Martins, Marcelo Ramos (Orientador)
Unidade: EP
Subjects: REDES NEURAIS, SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FIGUEROA BARRAZA, Joaquín Eduardo. Frameworks for interpretability of deep learning-based prognostics and health management. 2022. Tese (Doutorado) – Universidade de São Paulo, São Paulo, 2022. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-151410/. Acesso em: 30 jun. 2024.
APA
Figueroa Barraza, J. E. (2022). Frameworks for interpretability of deep learning-based prognostics and health management (Tese (Doutorado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-151410/
NLM
Figueroa Barraza JE. Frameworks for interpretability of deep learning-based prognostics and health management [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-151410/
Vancouver
Figueroa Barraza JE. Frameworks for interpretability of deep learning-based prognostics and health management [Internet]. 2022 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3135/tde-22052023-151410/
Artigo de periodico
LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks (2021)
Rodrigues Junior, José Fernando ; Gutierrez, Marco Antonio ; Spadon, Gabriel ; Machado, Bruno Brandoli ; Amer-Yahia, Sihem
Source: Information Sciences. Unidades: ICMC, EP
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), PROGNÓSTICO, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando et al. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, v. 545, p. 813-827, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.09.024. Acesso em: 30 jun. 2024.
APA
Rodrigues Junior, J. F., Gutierrez, M. A., Spadon, G., Machado, B. B., & Amer-Yahia, S. (2021). LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, 545, 813-827. doi:10.1016/j.ins.2020.09.024
NLM
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Vancouver
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Dissertação (Mestrado)
Towards FPGA-embedded CNNs:: network quantization and HDL infrastructure for bringing CNNs into FPGAs. (2021)
Ferreira, Vitor Finotti; Albertini, Bruno de Carvalho (Orientador)
Unidade: EP
Subjects: REDES NEURAIS, SISTEMAS EMBUTIDOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA, Vitor Finotti. Towards FPGA-embedded CNNs:: network quantization and HDL infrastructure for bringing CNNs into FPGAs. 2021. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14042021-153731/. Acesso em: 30 jun. 2024.
APA
Ferreira, V. F. (2021). Towards FPGA-embedded CNNs:: network quantization and HDL infrastructure for bringing CNNs into FPGAs. (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14042021-153731/
NLM
Ferreira VF. Towards FPGA-embedded CNNs:: network quantization and HDL infrastructure for bringing CNNs into FPGAs. [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14042021-153731/
Vancouver
Ferreira VF. Towards FPGA-embedded CNNs:: network quantization and HDL infrastructure for bringing CNNs into FPGAs. [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14042021-153731/
Dissertação (Mestrado)
Jurisprudence search based on facts similarity using NLP and ML techniques (2021)
Ruiz, Rodrigo Amorim; De Bona, Glauber (Orientador)
Unidade: EP
Subjects: JURISPRUDÊNCIA, INTELIGÊNCIA ARTIFICIAL, LINGUAGEM NATURAL, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RUIZ, Rodrigo Amorim. Jurisprudence search based on facts similarity using NLP and ML techniques. 2021. Dissertação (Mestrado) – Universidade de São Paulo, São Paulo, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14022022-122906/. Acesso em: 30 jun. 2024.
APA
Ruiz, R. A. (2021). Jurisprudence search based on facts similarity using NLP and ML techniques (Dissertação (Mestrado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14022022-122906/
NLM
Ruiz RA. Jurisprudence search based on facts similarity using NLP and ML techniques [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14022022-122906/
Vancouver
Ruiz RA. Jurisprudence search based on facts similarity using NLP and ML techniques [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-14022022-122906/
Artigo de periodico
Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900 (2021)
Silva, Isaac José da; Vilão Junior, Claudio O; Costa, Anna Helena Reali ; Bianchi, Reinaldo Augusto da Costa
Source: Journal of Control, Automation and Electrical Systems. Unidade: EP
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, ROBÓTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Isaac José da et al. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900. Journal of Control, Automation and Electrical Systems, v. 32, p. 884–894, 2021Tradução . . Disponível em: https://doi.org/10.1007/s40313-021-00719-8. Acesso em: 30 jun. 2024.
APA
Silva, I. J. da, Vilão Junior, C. O., Costa, A. H. R., & Bianchi, R. A. da C. (2021). Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900. Journal of Control, Automation and Electrical Systems, 32, 884–894. doi:10.1007/s40313-021-00719-8
NLM
Silva IJ da, Vilão Junior CO, Costa AHR, Bianchi RA da C. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900 [Internet]. Journal of Control, Automation and Electrical Systems. 2021 ;32 884–894.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1007/s40313-021-00719-8
Vancouver
Silva IJ da, Vilão Junior CO, Costa AHR, Bianchi RA da C. Intelligent Techniques Laboratory, Universidade de São Paulo (USP), São Paulo 05508-900 [Internet]. Journal of Control, Automation and Electrical Systems. 2021 ;32 884–894.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1007/s40313-021-00719-8
Artigo de periodico
Using neural network approaches to detect mooring line failure (2021)
Saad, Amir Muhammed ; Schopp, Florian ; Barreira, Rodrigo Augusto ; Santos, Ismael Humberto Ferreira dos; Tannuri, Eduardo Aoun ; Gomi, Edson Satoshi ; Reali Costa, Anna Helena
Source: IEEE Access. Unidade: EP
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, AMARRAÇÃO, CABOS DE AMARRAÇÃO, ESTRUTURAS FLUTUANTES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SAAD, Amir Muhammed et al. Using neural network approaches to detect mooring line failure. IEEE Access, v. fe 2021, p. 27678-27695, 2021Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2021.3058592. Acesso em: 30 jun. 2024.
APA
Saad, A. M., Schopp, F., Barreira, R. A., Santos, I. H. F. dos, Tannuri, E. A., Gomi, E. S., & Reali Costa, A. H. (2021). Using neural network approaches to detect mooring line failure. IEEE Access, fe 2021, 27678-27695. doi:10.1109/ACCESS.2021.3058592
NLM
Saad AM, Schopp F, Barreira RA, Santos IHF dos, Tannuri EA, Gomi ES, Reali Costa AH. Using neural network approaches to detect mooring line failure [Internet]. IEEE Access. 2021 ; fe 2021 27678-27695.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/ACCESS.2021.3058592
Vancouver
Saad AM, Schopp F, Barreira RA, Santos IHF dos, Tannuri EA, Gomi ES, Reali Costa AH. Using neural network approaches to detect mooring line failure [Internet]. IEEE Access. 2021 ; fe 2021 27678-27695.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1109/ACCESS.2021.3058592
Artigo de periodico
Solar irradiance prediction with machine learning algorithms: a brazilian case study on photovoltaic electricity generation (2021)
Viscondi, Gabriel de Freitas; Souza, Solange Nice Alves de
Source: Energies. Unidade: EP
Subjects: ENERGIA SOLAR, REDES NEURAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VISCONDI, Gabriel de Freitas e SOUZA, Solange Nice Alves de. Solar irradiance prediction with machine learning algorithms: a brazilian case study on photovoltaic electricity generation. Energies, v. 14, n. 18, p. 1-15, 2021Tradução . . Disponível em: https://doi.org/10.3390/en14170000. Acesso em: 30 jun. 2024.
APA
Viscondi, G. de F., & Souza, S. N. A. de. (2021). Solar irradiance prediction with machine learning algorithms: a brazilian case study on photovoltaic electricity generation. Energies, 14( 18), 1-15. doi:10.3390/en14170000
NLM
Viscondi G de F, Souza SNA de. Solar irradiance prediction with machine learning algorithms: a brazilian case study on photovoltaic electricity generation [Internet]. Energies. 2021 ; 14( 18): 1-15.[citado 2024 jun. 30 ] Available from: https://doi.org/10.3390/en14170000
Vancouver
Viscondi G de F, Souza SNA de. Solar irradiance prediction with machine learning algorithms: a brazilian case study on photovoltaic electricity generation [Internet]. Energies. 2021 ; 14( 18): 1-15.[citado 2024 jun. 30 ] Available from: https://doi.org/10.3390/en14170000
Tese (Doutorado)
The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. (2021)
Pinheiro, Rafael Fernandes; Colón, Diego (Orientador)
Unidade: EP
Subjects: REDES NEURAIS, DOENÇA DE ALZHEIMER
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PINHEIRO, Rafael Fernandes. The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. 2021. Tese (Doutorado) – Universidade de São Paulo, São Paulo, 2021. Disponível em: https://www.teses.usp.br/teses/disponiveis/3/3139/tde-10022022-115037/. Acesso em: 30 jun. 2024.
APA
Pinheiro, R. F. (2021). The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. (Tese (Doutorado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/3/3139/tde-10022022-115037/
NLM
Pinheiro RF. The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3139/tde-10022022-115037/
Vancouver
Pinheiro RF. The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. [Internet]. 2021 ;[citado 2024 jun. 30 ] Available from: https://www.teses.usp.br/teses/disponiveis/3/3139/tde-10022022-115037/
Artigo de periodico
Evaluation of fischer-tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network (2021)
Garona, Higor Azevedo; Cavalcanti, Fábio Machado ; Abreu, Thiago Ferreira de ; Schmal, Martin ; Alves, Rita Maria de Brito
Source: Journal of Cleaner Production. Unidade: EP
Subjects: REDES NEURAIS, DIÓXIDO DE CARBONO, CATALISADORES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARONA, Higor Azevedo et al. Evaluation of fischer-tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network. Journal of Cleaner Production, v. 321, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.jclepro.2021.129003. Acesso em: 30 jun. 2024.
APA
Garona, H. A., Cavalcanti, F. M., Abreu, T. F. de, Schmal, M., & Alves, R. M. de B. (2021). Evaluation of fischer-tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network. Journal of Cleaner Production, 321, 1-13. doi:10.1016/j.jclepro.2021.129003
NLM
Garona HA, Cavalcanti FM, Abreu TF de, Schmal M, Alves RM de B. Evaluation of fischer-tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network [Internet]. Journal of Cleaner Production. 2021 ;321 1-13.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1016/j.jclepro.2021.129003
Vancouver
Garona HA, Cavalcanti FM, Abreu TF de, Schmal M, Alves RM de B. Evaluation of fischer-tropsch synthesis to light olefins over Co- and Fe-based catalysts using artificial neural network [Internet]. Journal of Cleaner Production. 2021 ;321 1-13.[citado 2024 jun. 30 ] Available from: https://doi.org/10.1016/j.jclepro.2021.129003

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