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Tese (Doutorado)
Graph Neural Networks contributions and advancements (2024)
Gonçalves, Thales ; Nonato, Luis Gustavo (Orientador)
Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, GRAFOS ALEATÓRIOS
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ABNT
GONÇALVES, Thales. Graph Neural Networks contributions and advancements. 2024. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2024. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-16072024-103201/. Acesso em: 04 nov. 2024.
APA
Gonçalves, T. (2024). Graph Neural Networks contributions and advancements (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-16072024-103201/
NLM
Gonçalves T. Graph Neural Networks contributions and advancements [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-16072024-103201/
Vancouver
Gonçalves T. Graph Neural Networks contributions and advancements [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-16072024-103201/
Artigo de periodico
Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil (2024)
Silva, João Pedro; Rodrigues Junior, José Fernando
Source: International Journal of Semantic Computing. Unidade: ICMC
Subjects: APRENDIZAGEM PROFUNDA, REDES NEURAIS, INDICADORES SOCIOECONÔMICOS, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO
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ABNT
SILVA, João Pedro e RODRIGUES JUNIOR, José Fernando. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil. International Journal of Semantic Computing, v. 18, n. 3, p. 329-345, 2024Tradução . . Disponível em: https://doi.org/10.1142/S1793351X24420017. Acesso em: 04 nov. 2024.
APA
Silva, J. P., & Rodrigues Junior, J. F. (2024). Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil. International Journal of Semantic Computing, 18( 3), 329-345. doi:10.1142/S1793351X24420017
NLM
Silva JP, Rodrigues Junior JF. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil [Internet]. International Journal of Semantic Computing. 2024 ; 18( 3): 329-345.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1142/S1793351X24420017
Vancouver
Silva JP, Rodrigues Junior JF. Utilizing satellite imagery to predict socioeconomic indicators: a study conducted in Brazil [Internet]. International Journal of Semantic Computing. 2024 ; 18( 3): 329-345.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1142/S1793351X24420017
Artigo de periodico
Unsupervised heterogeneous graph neural networks for one-class tasks: exploring early fusion operators (2024)
Gôlo, Marcos Paulo Silva ; Moraes Junior, Marcelo Isaias de ; Goularte, Rudinei ; Marcacini, Ricardo Marcondes
Source: Journal on Interactive Systems. Unidade: ICMC
Subjects: REDES NEURAIS, TEORIA DOS GRAFOS
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ABNT
GÔLO, Marcos Paulo Silva et al. Unsupervised heterogeneous graph neural networks for one-class tasks: exploring early fusion operators. Journal on Interactive Systems, v. 15, n. 1, p. 517-529, 2024Tradução . . Disponível em: https://doi.org/10.5753/jis.2024.4109. Acesso em: 04 nov. 2024.
APA
Gôlo, M. P. S., Moraes Junior, M. I. de, Goularte, R., & Marcacini, R. M. (2024). Unsupervised heterogeneous graph neural networks for one-class tasks: exploring early fusion operators. Journal on Interactive Systems, 15( 1), 517-529. doi:10.5753/jis.2024.4109
NLM
Gôlo MPS, Moraes Junior MI de, Goularte R, Marcacini RM. Unsupervised heterogeneous graph neural networks for one-class tasks: exploring early fusion operators [Internet]. Journal on Interactive Systems. 2024 ; 15( 1): 517-529.[citado 2024 nov. 04 ] Available from: https://doi.org/10.5753/jis.2024.4109
Vancouver
Gôlo MPS, Moraes Junior MI de, Goularte R, Marcacini RM. Unsupervised heterogeneous graph neural networks for one-class tasks: exploring early fusion operators [Internet]. Journal on Interactive Systems. 2024 ; 15( 1): 517-529.[citado 2024 nov. 04 ] Available from: https://doi.org/10.5753/jis.2024.4109
Trabalho de evento-anais periodico
Bipartite graph coarsening for text classification using graph neural networks (2024)
Santos, Nícolas Roque dos ; Minatel, Diego ; Valejo, Alan Demetrius Baria ; Lopes, Alneu de Andrade
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, REDES NEURAIS
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ABNT
SANTOS, Nícolas Roque dos et al. Bipartite graph coarsening for text classification using graph neural networks. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-49018-7_42. Acesso em: 04 nov. 2024. , 2024
APA
Santos, N. R. dos, Minatel, D., Valejo, A. D. B., & Lopes, A. de A. (2024). Bipartite graph coarsening for text classification using graph neural networks. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-49018-7_42
NLM
Santos NR dos, Minatel D, Valejo ADB, Lopes A de A. Bipartite graph coarsening for text classification using graph neural networks [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 589-604.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_42
Vancouver
Santos NR dos, Minatel D, Valejo ADB, Lopes A de A. Bipartite graph coarsening for text classification using graph neural networks [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 589-604.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_42
Tese (Doutorado)
Leveraging Modular Architectures and End-to-End Learning for Autonomous Driving in Unmapped Environments (2024)
Rosero, Luis Alberto Rosero ; Osório, Fernando Santos (Orientador)
Unidade: ICMC
Subjects: VEÍCULOS AUTÔNOMOS, TRAJETÓRIA, TOMADA DE DECISÃO, PREDIÇÃO, REDES NEURAIS, PERCEPÇÃO
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ABNT
ROSERO, Luis Alberto Rosero. Leveraging Modular Architectures and End-to-End Learning for Autonomous Driving in Unmapped Environments. 2024. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2024. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-09082024-163642/. Acesso em: 04 nov. 2024.
APA
Rosero, L. A. R. (2024). Leveraging Modular Architectures and End-to-End Learning for Autonomous Driving in Unmapped Environments (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-09082024-163642/
NLM
Rosero LAR. Leveraging Modular Architectures and End-to-End Learning for Autonomous Driving in Unmapped Environments [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-09082024-163642/
Vancouver
Rosero LAR. Leveraging Modular Architectures and End-to-End Learning for Autonomous Driving in Unmapped Environments [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-09082024-163642/
Artigo de periodico
A deep learning workflow enhanced with optical flow fields for flood risk estimation (2024)
Ranieri, Caetano Mazzoni ; Souza, Thais Luiza Donega e ; Nishijima, Marislei ; Krishnamachari, Bhaskar ; Ueyama, Jó
Source: Applied Intelligence. Unidades: ICMC, IRI, EACH
Subjects: ENCHENTES URBANAS, APRENDIZAGEM PROFUNDA, PROCESSAMENTO DE IMAGENS, VISÃO COMPUTACIONAL, REDES NEURAIS
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RANIERI, Caetano Mazzoni et al. A deep learning workflow enhanced with optical flow fields for flood risk estimation. Applied Intelligence, v. 54, p. 5536-5557, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10489-024-05466-2. Acesso em: 04 nov. 2024.
APA
Ranieri, C. M., Souza, T. L. D. e, Nishijima, M., Krishnamachari, B., & Ueyama, J. (2024). A deep learning workflow enhanced with optical flow fields for flood risk estimation. Applied Intelligence, 54, 5536-5557. doi:10.1007/s10489-024-05466-2
NLM
Ranieri CM, Souza TLD e, Nishijima M, Krishnamachari B, Ueyama J. A deep learning workflow enhanced with optical flow fields for flood risk estimation [Internet]. Applied Intelligence. 2024 ; 54 5536-5557.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s10489-024-05466-2
Vancouver
Ranieri CM, Souza TLD e, Nishijima M, Krishnamachari B, Ueyama J. A deep learning workflow enhanced with optical flow fields for flood risk estimation [Internet]. Applied Intelligence. 2024 ; 54 5536-5557.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s10489-024-05466-2
Artigo de periodico
On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments (2024)
Alves, Caroline Lourenço ; Ciba, Manuel ; Toutain, Thaise Graziele Lima de ; Porto, Joel Augusto Moura ; Sena, Eduardo Pondé de ; Thielemann, Christiane ; Rodrigues, Francisco
Source: Journal of Physics: Complexity. Unidades: ICMC, IFSC
Subjects: VISÃO COMPUTACIONAL, APRENDIZADO COMPUTACIONAL, TOPOLOGIA EM COMPUTAÇÃO, REDES NEURAIS, TEORIA DOS GRAFOS, CÓRTEX CEREBRAL (ANÁLISE), CÉREBRO (ANÁLISE;ESTUDO)
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ABNT
ALVES, Caroline Lourenço et al. On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments. Journal of Physics: Complexity, v. 5, n. Ja 2024, p. 015002-1-015002-23, 2024Tradução . . Disponível em: https://doi.org/10.1088/2632-072X/ad1c68. Acesso em: 04 nov. 2024.
APA
Alves, C. L., Ciba, M., Toutain, T. G. L. de, Porto, J. A. M., Sena, E. P. de, Thielemann, C., & Rodrigues, F. (2024). On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments. Journal of Physics: Complexity, 5( Ja 2024), 015002-1-015002-23. doi:10.1088/2632-072X/ad1c68
NLM
Alves CL, Ciba M, Toutain TGL de, Porto JAM, Sena EP de, Thielemann C, Rodrigues F. On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments [Internet]. Journal of Physics: Complexity. 2024 ; 5( Ja 2024): 015002-1-015002-23.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1088/2632-072X/ad1c68
Vancouver
Alves CL, Ciba M, Toutain TGL de, Porto JAM, Sena EP de, Thielemann C, Rodrigues F. On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments [Internet]. Journal of Physics: Complexity. 2024 ; 5( Ja 2024): 015002-1-015002-23.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1088/2632-072X/ad1c68
Trabalho de evento
Modelling graph neural network by aggregating the activation maps of self-organizing map (2024)
Martins, Luan Vinicius de Carvalho ; Donghong, Ji; Liang, Zhao
Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidades: FFCLRP, ICMC
Subjects: REDES NEURAIS, TEORIA DOS GRAFOS
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ABNT
MARTINS, Luan Vinicius de Carvalho e DONGHONG, Ji e LIANG, Zhao. Modelling graph neural network by aggregating the activation maps of self-organizing map. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/IJCNN60899.2024.10650514. Acesso em: 04 nov. 2024.
APA
Martins, L. V. de C., Donghong, J., & Liang, Z. (2024). Modelling graph neural network by aggregating the activation maps of self-organizing map. In Proceedings. Piscataway: IEEE. doi:10.1109/IJCNN60899.2024.10650514
NLM
Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514
Vancouver
Martins LV de C, Donghong J, Liang Z. Modelling graph neural network by aggregating the activation maps of self-organizing map [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/IJCNN60899.2024.10650514
Trabalho de evento
Machine learning techniques for improving multiclass anomaly detection on conveyor belts (2024)
Matos, Saulo Neves ; Coletti, Otavio Ferracioli ; Zimmer, Rafael; Ugucioni Filho, Fernando; Carvalho, Ricardo C. C. L. de; Silva, Victor Rodrigues da; Franco, Jorge Luiz; Pinto, Thomas Vargas Barsante e ; Barros, Luiz Guilherme Dias de ; Ranieri, Caetano Mazzoni ; Lopes, Bruno Eduardo; Silva, Diego Furtado ; Ueyama, Jó ; Pessin, Gustavo
Source: Proceedings. Conference titles: IEEE International Instrumentation and Measurement Technology Conference - I2MTC. Unidades: ICMC, EESC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, MANUTENÇÃO PREDITIVA, TRANSPORTADORES DE CORREIA
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ABNT
MATOS, Saulo Neves et al. Machine learning techniques for improving multiclass anomaly detection on conveyor belts. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/I2MTC60896.2024.10561167. Acesso em: 04 nov. 2024.
APA
Matos, S. N., Coletti, O. F., Zimmer, R., Ugucioni Filho, F., Carvalho, R. C. C. L. de, Silva, V. R. da, et al. (2024). Machine learning techniques for improving multiclass anomaly detection on conveyor belts. In Proceedings. Piscataway: IEEE. doi:10.1109/I2MTC60896.2024.10561167
NLM
Matos SN, Coletti OF, Zimmer R, Ugucioni Filho F, Carvalho RCCL de, Silva VR da, Franco JL, Pinto TVB e, Barros LGD de, Ranieri CM, Lopes BE, Silva DF, Ueyama J, Pessin G. Machine learning techniques for improving multiclass anomaly detection on conveyor belts [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/I2MTC60896.2024.10561167
Vancouver
Matos SN, Coletti OF, Zimmer R, Ugucioni Filho F, Carvalho RCCL de, Silva VR da, Franco JL, Pinto TVB e, Barros LGD de, Ranieri CM, Lopes BE, Silva DF, Ueyama J, Pessin G. Machine learning techniques for improving multiclass anomaly detection on conveyor belts [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/I2MTC60896.2024.10561167
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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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: 04 nov. 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 nov. 04 ] 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 nov. 04 ] Available from: https://doi.org/10.1016/j.physa.2024.129518
Artigo de periodico
Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets (2024)
Spolaôr, Newton ; Lee, Huei Diana ; Mendes, Ana Isabel; Nogueira, Conceição Veloso; Parmezan, Antonio Rafael Sabino ; Takaki, Weber Shoity Resende ; Coy, Claudio Saddy Rodrigues ; Wu, Feng Chung; Fonseca-Pinto, Rui
Source: Multimedia Tools and Applications. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, NEOPLASIAS CUTÂNEAS
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ABNT
SPOLAÔR, Newton et al. Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets. Multimedia Tools and Applications, v. 83, n. 9, p. 27305-27329, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11042-023-16529-w. Acesso em: 04 nov. 2024.
APA
Spolaôr, N., Lee, H. D., Mendes, A. I., Nogueira, C. V., Parmezan, A. R. S., Takaki, W. S. R., et al. (2024). Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets. Multimedia Tools and Applications, 83( 9), 27305-27329. doi:10.1007/s11042-023-16529-w
NLM
Spolaôr N, Lee HD, Mendes AI, Nogueira CV, Parmezan ARS, Takaki WSR, Coy CSR, Wu FC, Fonseca-Pinto R. Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets [Internet]. Multimedia Tools and Applications. 2024 ; 83( 9): 27305-27329.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s11042-023-16529-w
Vancouver
Spolaôr N, Lee HD, Mendes AI, Nogueira CV, Parmezan ARS, Takaki WSR, Coy CSR, Wu FC, Fonseca-Pinto R. Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets [Internet]. Multimedia Tools and Applications. 2024 ; 83( 9): 27305-27329.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s11042-023-16529-w
Dissertação (Mestrado)
ConSentiment: Análise de Sentimentos baseada em Aspectos a partir de Consenso de Modelos em Grafos (2024)
Marcon, Guilherme dos Santos; Marcacini, Ricardo Marcondes (Orientador)
Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, APRENDIZAGEM PROFUNDA, ANÁLISE DE TEXTO
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ABNT
MARCON, Guilherme dos Santos. ConSentiment: Análise de Sentimentos baseada em Aspectos a partir de Consenso de Modelos em Grafos. 2024. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2024. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-17062024-085235/. Acesso em: 04 nov. 2024.
APA
Marcon, G. dos S. (2024). ConSentiment: Análise de Sentimentos baseada em Aspectos a partir de Consenso de Modelos em Grafos (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-17062024-085235/
NLM
Marcon G dos S. ConSentiment: Análise de Sentimentos baseada em Aspectos a partir de Consenso de Modelos em Grafos [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-17062024-085235/
Vancouver
Marcon G dos S. ConSentiment: Análise de Sentimentos baseada em Aspectos a partir de Consenso de Modelos em Grafos [Internet]. 2024 ;[citado 2024 nov. 04 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-17062024-085235/
Artigo de periodico
Using full-text content to characterize and identify best seller books: a study of early 20th-century literature (2024)
Silva, Giovana Daniele da ; Silva, Filipi Nascimento ; Arruda, Henrique Ferraz de ; Souza, Bárbara Côrtes e ; Costa, Luciano da Fontoura ; Amancio, Diego Raphael
Source: PLOS One. Unidades: IFSC, ICMC
Subjects: CIÊNCIA DA COMPUTAÇÃO, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
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ABNT
SILVA, Giovana Daniele da et al. Using full-text content to characterize and identify best seller books: a study of early 20th-century literature. PLOS One, v. 19, n. 4, p. e0302070-1-e0302070-20 + supporting information, 2024Tradução . . Disponível em: https://doi.org/10.1371/journal.pone.0302070. Acesso em: 04 nov. 2024.
APA
Silva, G. D. da, Silva, F. N., Arruda, H. F. de, Souza, B. C. e, Costa, L. da F., & Amancio, D. R. (2024). Using full-text content to characterize and identify best seller books: a study of early 20th-century literature. PLOS One, 19( 4), e0302070-1-e0302070-20 + supporting information. doi:10.1371/journal.pone.0302070
NLM
Silva GD da, Silva FN, Arruda HF de, Souza BC e, Costa L da F, Amancio DR. Using full-text content to characterize and identify best seller books: a study of early 20th-century literature [Internet]. PLOS One. 2024 ; 19( 4): e0302070-1-e0302070-20 + supporting information.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1371/journal.pone.0302070
Vancouver
Silva GD da, Silva FN, Arruda HF de, Souza BC e, Costa L da F, Amancio DR. Using full-text content to characterize and identify best seller books: a study of early 20th-century literature [Internet]. PLOS One. 2024 ; 19( 4): e0302070-1-e0302070-20 + supporting information.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1371/journal.pone.0302070
Artigo de periodico
Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: a systematic review (2024)
Giraldo-Roldán, Daniela ; Araujo, Anna Luiza Damaceno ; Moraes, Matheus Cardoso ; Silva, Viviane Mariano da ; Ribeiro, Erin Crespo Cordeiro ; Pinto, Matheus Henrique de Cerqueira ; Saldivia-Siracusa, Cristina ; Sousa-Neto, Sebastião Silvério ; Perez-de-Oliveira, Maria Eduarda ; Lopes, Márcio Ajudarte ; Kowalski, Luiz Paulo ; Carvalho, Andre Carlos Ponce de Leon Ferreira de ; Santos-Silva, Alan Roger dos ; Vargas, Pablo Agustin
Source: Journal of Oral Pathology and Medicine. Unidades: FM, ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, TOMOGRAFIA COMPUTADORIZADA DE EMISSÃO, DIAGNÓSTICO POR COMPUTADOR, RADIOGRAFIA PANORÂMICA
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ABNT
GIRALDO-ROLDÁN, Daniela et al. Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: a systematic review. Journal of Oral Pathology and Medicine, v. 53, n. 7, p. 415-433, 2024Tradução . . Disponível em: https://doi.org/10.1111/jop.13548. Acesso em: 04 nov. 2024.
APA
Giraldo-Roldán, D., Araujo, A. L. D., Moraes, M. C., Silva, V. M. da, Ribeiro, E. C. C., Pinto, M. H. de C., et al. (2024). Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: a systematic review. Journal of Oral Pathology and Medicine, 53( 7), 415-433. doi:10.1111/jop.13548
NLM
Giraldo-Roldán D, Araujo ALD, Moraes MC, Silva VM da, Ribeiro ECC, Pinto MH de C, Saldivia-Siracusa C, Sousa-Neto SS, Perez-de-Oliveira ME, Lopes MA, Kowalski LP, Carvalho ACP de LF de, Santos-Silva AR dos, Vargas PA. Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: a systematic review [Internet]. Journal of Oral Pathology and Medicine. 2024 ; 53( 7): 415-433.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1111/jop.13548
Vancouver
Giraldo-Roldán D, Araujo ALD, Moraes MC, Silva VM da, Ribeiro ECC, Pinto MH de C, Saldivia-Siracusa C, Sousa-Neto SS, Perez-de-Oliveira ME, Lopes MA, Kowalski LP, Carvalho ACP de LF de, Santos-Silva AR dos, Vargas PA. Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: a systematic review [Internet]. Journal of Oral Pathology and Medicine. 2024 ; 53( 7): 415-433.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1111/jop.13548
Artigo de periodico
Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning (2024)
Souza, Thais Luiza Donega e ; Ranieri, Caetano Mazzoni ; Panangadan, Anand ; Ueyama, Jó ; Nishijima, Marislei
Source: Multimedia Tools and Applications. Unidades: ICMC, IRI, EACH
Subjects: REDES NEURAIS, CINEMA, CARTAZ, QUALIDADE DO PRODUTO, APRENDIZAGEM PROFUNDA, DESIGN GRÁFICO, PROPAGANDA PELO CINEMA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Thais Luiza Donega e et al. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning. Multimedia Tools and Applications, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11042-024-20174-2. Acesso em: 04 nov. 2024.
APA
Souza, T. L. D. e, Ranieri, C. M., Panangadan, A., Ueyama, J., & Nishijima, M. (2024). Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning. Multimedia Tools and Applications. doi:10.1007/s11042-024-20174-2
NLM
Souza TLD e, Ranieri CM, Panangadan A, Ueyama J, Nishijima M. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning [Internet]. Multimedia Tools and Applications. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s11042-024-20174-2
Vancouver
Souza TLD e, Ranieri CM, Panangadan A, Ueyama J, Nishijima M. Is a poster a strong signal of film quality?: evaluating the predictive power of visual elements using deep learning [Internet]. Multimedia Tools and Applications. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s11042-024-20174-2
Artigo de periodico
Artist similarity based on heterogeneous graph neural networks (2024)
Silva, Angelo Cesar Mendes da ; Silva, Diego Furtado ; Marcacini, Ricardo Marcondes
Source: IEEE/ACM Transactions on Audio, Speech, and Language Processing. Unidade: ICMC
Subjects: REDES NEURAIS, RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE PADRÕES, MÚSICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Angelo Cesar Mendes da e SILVA, Diego Furtado e MARCACINI, Ricardo Marcondes. Artist similarity based on heterogeneous graph neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, v. 32, p. 3717-3729, 2024Tradução . . Disponível em: https://doi.org/10.1109/TASLP.2024.3437170. Acesso em: 04 nov. 2024.
APA
Silva, A. C. M. da, Silva, D. F., & Marcacini, R. M. (2024). Artist similarity based on heterogeneous graph neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 32, 3717-3729. doi:10.1109/TASLP.2024.3437170
NLM
Silva ACM da, Silva DF, Marcacini RM. Artist similarity based on heterogeneous graph neural networks [Internet]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2024 ; 32 3717-3729.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/TASLP.2024.3437170
Vancouver
Silva ACM da, Silva DF, Marcacini RM. Artist similarity based on heterogeneous graph neural networks [Internet]. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2024 ; 32 3717-3729.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/TASLP.2024.3437170
Artigo de periodico
Ensemble of convolutional neural networks for COVID-19 localization on chest X-ray images (2024)
Marcomini, Karem Daiane
Source: Big Data and Cognitive Computing. Unidade: ICMC
Subjects: REDES NEURAIS, RECONHECIMENTO DE IMAGEM, RAIOS X, COVID-19
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARCOMINI, Karem Daiane. Ensemble of convolutional neural networks for COVID-19 localization on chest X-ray images. Big Data and Cognitive Computing, v. 8, n. 8, p. 1-19, 2024Tradução . . Disponível em: https://doi.org/10.3390/bdcc8080084. Acesso em: 04 nov. 2024.
APA
Marcomini, K. D. (2024). Ensemble of convolutional neural networks for COVID-19 localization on chest X-ray images. Big Data and Cognitive Computing, 8( 8), 1-19. doi:10.3390/bdcc8080084
NLM
Marcomini KD. Ensemble of convolutional neural networks for COVID-19 localization on chest X-ray images [Internet]. Big Data and Cognitive Computing. 2024 ; 8( 8): 1-19.[citado 2024 nov. 04 ] Available from: https://doi.org/10.3390/bdcc8080084
Vancouver
Marcomini KD. Ensemble of convolutional neural networks for COVID-19 localization on chest X-ray images [Internet]. Big Data and Cognitive Computing. 2024 ; 8( 8): 1-19.[citado 2024 nov. 04 ] Available from: https://doi.org/10.3390/bdcc8080084
Artigo de periodico
Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers (2024)
Rocha Filho, Geraldo Pereira ; Meneguette, Rodolfo Ipolito ; Mendonça, Fabio Lucio Lopes de ; Enamoto, Liriam Michi ; Pessin, Gustavo ; Gonçalves, Vinicius Pereira
Source: Neural Computing and Applications. Unidade: ICMC
Subjects: EMOÇÕES, VOZ, REDES NEURAIS, RECONHECIMENTO DE VOZ
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ROCHA FILHO, Geraldo Pereira et al. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, v. 36, p. 19939–19950, 2024Tradução . . Disponível em: https://doi.org/10.1007/s00521-024-10249-4. Acesso em: 04 nov. 2024.
APA
Rocha Filho, G. P., Meneguette, R. I., Mendonça, F. L. L. de, Enamoto, L. M., Pessin, G., & Gonçalves, V. P. (2024). Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers. Neural Computing and Applications, 36, 19939–19950. doi:10.1007/s00521-024-10249-4
NLM
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Vancouver
Rocha Filho GP, Meneguette RI, Mendonça FLL de, Enamoto LM, Pessin G, Gonçalves VP. Toward an emotion efficient architecture based on the sound spectrum from the voice of Portuguese speakers [Internet]. Neural Computing and Applications. 2024 ; 36 19939–19950.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1007/s00521-024-10249-4
Artigo de periodico
BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification (2024)
Santos, Anderson Paulo Avila ; Almeida, Breno Lívio Silva de ; Bonidia, Robson Parmezan ; Stadler, Peter F ; Štefanič, Polonca; Mandic-Mulec, Ines; Rocha, Ulisses Nunes da ; Sanches, Danilo Sipoli ; Carvalho, Andre Carlos Ponce de Leon Ferreira de
Source: RNA Biology. Unidades: ICMC, EESC E ICMC
Subjects: BIOINFORMÁTICA, APRENDIZAGEM PROFUNDA, REDES NEURAIS, RNA, REGULAÇÃO GÊNICA, GENÔMICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Anderson Paulo Avila et al. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, v. 21, n. 1, p. 1-12, 2024Tradução . . Disponível em: https://doi.org/10.1080/15476286.2024.2329451. Acesso em: 04 nov. 2024.
APA
Santos, A. P. A., Almeida, B. L. S. de, Bonidia, R. P., Stadler, P. F., Štefanič, P., Mandic-Mulec, I., et al. (2024). BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, 21( 1), 1-12. doi:10.1080/15476286.2024.2329451
NLM
Santos APA, Almeida BLS de, Bonidia RP, Stadler PF, Štefanič P, Mandic-Mulec I, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification [Internet]. RNA Biology. 2024 ; 21( 1): 1-12.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1080/15476286.2024.2329451
Vancouver
Santos APA, Almeida BLS de, Bonidia RP, Stadler PF, Štefanič P, Mandic-Mulec I, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification [Internet]. RNA Biology. 2024 ; 21( 1): 1-12.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1080/15476286.2024.2329451
Trabalho de evento
Efficient parameter mining and freezing for continual object detection (2024)
Menezes, Angelo Garangau ; Peterlevitz, Augusto José ; Chinelatto, Mateus Antonio ; Carvalho, Andre Carlos Ponce de Leon Ferreira de
Source: Proceedings. Conference titles: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MENEZES, Angelo Garangau et al. Efficient parameter mining and freezing for continual object detection. 2024, Anais.. Setúbal: SciTePress, 2024. Disponível em: https://doi.org/10.5220/0012362300003660. Acesso em: 04 nov. 2024.
APA
Menezes, A. G., Peterlevitz, A. J., Chinelatto, M. A., & Carvalho, A. C. P. de L. F. de. (2024). Efficient parameter mining and freezing for continual object detection. In Proceedings. Setúbal: SciTePress. doi:10.5220/0012362300003660
NLM
Menezes AG, Peterlevitz AJ, Chinelatto MA, Carvalho ACP de LF de. Efficient parameter mining and freezing for continual object detection [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.5220/0012362300003660
Vancouver
Menezes AG, Peterlevitz AJ, Chinelatto MA, Carvalho ACP de LF de. Efficient parameter mining and freezing for continual object detection [Internet]. Proceedings. 2024 ;[citado 2024 nov. 04 ] Available from: https://doi.org/10.5220/0012362300003660

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