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Trabalho de evento-anais periodico
DIF-SR: a differential item functioning-based sample reweighting method (2024)
Minatel, Diego ; Parmezan, Antonio Rafael Sabino ; Cúri, Mariana ; Lopes, Alneu de Andrade
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP. Unidade: ICMC
Subjects: TEORIA DE RESPOSTA AO ITEM, APRENDIZADO COMPUTACIONAL
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ABNT
MINATEL, Diego et al. DIF-SR: a differential item functioning-based sample reweighting method. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-49018-7_45. Acesso em: 15 nov. 2024. , 2024
APA
Minatel, D., Parmezan, A. R. S., Cúri, M., & Lopes, A. de A. (2024). DIF-SR: a differential item functioning-based sample reweighting method. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-031-49018-7_45
NLM
Minatel D, Parmezan ARS, Cúri M, Lopes A de A. DIF-SR: a differential item functioning-based sample reweighting method [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 630-645.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_45
Vancouver
Minatel D, Parmezan ARS, Cúri M, Lopes A de A. DIF-SR: a differential item functioning-based sample reweighting method [Internet]. Lecture Notes in Computer Science. 2024 ; 14469 630-645.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-49018-7_45
Artigo de periodico
Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose (2024)
Piccinin, Ana Carolina Varanda; Soares, Andrey Coatrini ; Franco, Giuliana Thalina ; Bondancia, Thalita Jessika ; Soares, Juliana Coatrini ; Oliveira Junior, Osvaldo Novais de ; Mattoso, Luiz Henrique Capparelli
Source: Frontiers in Sensors. Unidade: IFSC
Subjects: SENSOR, MONITORAMENTO AMBIENTAL, APRENDIZADO COMPUTACIONAL, SENSORES BIOMÉDICOS, FILMES, PECUÁRIA LEITEIRA
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PICCININ, Ana Carolina Varanda et al. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose. Frontiers in Sensors, v. 5, 2024Tradução . . Disponível em: https://doi.org/10.3389/fsens.2024.1401077. Acesso em: 15 nov. 2024.
APA
Piccinin, A. C. V., Soares, A. C., Franco, G. T., Bondancia, T. J., Soares, J. C., Oliveira Junior, O. N. de, & Mattoso, L. H. C. (2024). Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose. Frontiers in Sensors, 5. doi:10.3389/fsens.2024.1401077
NLM
Piccinin ACV, Soares AC, Franco GT, Bondancia TJ, Soares JC, Oliveira Junior ON de, Mattoso LHC. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose [Internet]. Frontiers in Sensors. 2024 ; 5[citado 2024 nov. 15 ] Available from: https://doi.org/10.3389/fsens.2024.1401077
Vancouver
Piccinin ACV, Soares AC, Franco GT, Bondancia TJ, Soares JC, Oliveira Junior ON de, Mattoso LHC. Electronic tongue made of gelatin self-supporting films on printed electrodes to detect lactose [Internet]. Frontiers in Sensors. 2024 ; 5[citado 2024 nov. 15 ] Available from: https://doi.org/10.3389/fsens.2024.1401077
Artigo de periodico
A data-driven method for water quality analysis and prediction for localized irrigation (2024)
Silva, Roberto Fray da ; Benso, Marcos Roberto ; Correa, Fernando Elias ; Messias, Tâmara Guindo ; Mendonça, Fernando Campos ; Marques, Patricia Angélica Alves ; Duarte, Sergio Nascimento ; Mendiondo, Eduardo Mario ; Delbem, Alexandre Cláudio Botazzo ; Saraiva, Antonio Mauro
Source: AgriEngineering. Unidades: ESALQ, EESC, ICMC, EP, IEA
Subjects: APRENDIZADO COMPUTACIONAL, ESTUDO DE CASO, IRRIGAÇÃO, QUALIDADE DA ÁGUA
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SILVA, Roberto Fray da et al. A data-driven method for water quality analysis and prediction for localized irrigation. AgriEngineering, v. 6, n. 2, p. 1771-1793, 2024Tradução . . Disponível em: https://doi.org/10.3390/agriengineering6020103. Acesso em: 15 nov. 2024.
APA
Silva, R. F. da, Benso, M. R., Correa, F. E., Messias, T. G., Mendonça, F. C., Marques, P. A. A., et al. (2024). A data-driven method for water quality analysis and prediction for localized irrigation. AgriEngineering, 6( 2), 1771-1793. doi:10.3390/agriengineering6020103
NLM
Silva RF da, Benso MR, Correa FE, Messias TG, Mendonça FC, Marques PAA, Duarte SN, Mendiondo EM, Delbem ACB, Saraiva AM. A data-driven method for water quality analysis and prediction for localized irrigation [Internet]. AgriEngineering. 2024 ; 6( 2): 1771-1793.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agriengineering6020103
Vancouver
Silva RF da, Benso MR, Correa FE, Messias TG, Mendonça FC, Marques PAA, Duarte SN, Mendiondo EM, Delbem ACB, Saraiva AM. A data-driven method for water quality analysis and prediction for localized irrigation [Internet]. AgriEngineering. 2024 ; 6( 2): 1771-1793.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agriengineering6020103
Trabalho de evento
The lattice overparametrization paradigm for the machine learning of lattice operators (2024)
Marcondes, Diego ; Barrera, Junior
Source: Proceedings. Conference titles: International Conference on Discrete Geometry and Mathematical Morphology - DGMM. Unidade: IME
Subjects: PROCESSAMENTO DE IMAGENS, APRENDIZADO COMPUTACIONAL, OPERADORES
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ABNT
MARCONDES, Diego e BARRERA, Junior. The lattice overparametrization paradigm for the machine learning of lattice operators. 2024, Anais.. Cham: Springer, 2024. Disponível em: https://doi.org/10.1007/978-3-031-57793-2_16. Acesso em: 15 nov. 2024.
APA
Marcondes, D., & Barrera, J. (2024). The lattice overparametrization paradigm for the machine learning of lattice operators. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-57793-2_16
NLM
Marcondes D, Barrera J. The lattice overparametrization paradigm for the machine learning of lattice operators [Internet]. Proceedings. 2024 ;[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-57793-2_16
Vancouver
Marcondes D, Barrera J. The lattice overparametrization paradigm for the machine learning of lattice operators [Internet]. Proceedings. 2024 ;[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-57793-2_16
Artigo de periodico
Solid-state NMR characterization of mefloquine resinate complexes designed for taste-masking pediatric formulations (2024)
Borré, Leandro Bandeira; Sousa, Eduardo Gomes Rodrigues de ; Gil, Rosane Aguiar da Silva San; Baptista, Mateus Marinho; Leitão, Alexandre Amaral ; Almeida, João Monnerat Araujo Ribeiro de ; Carr, Olivia; Oliveira Junior, Osvaldo Novais de ; Shimizu, Flávio Makoto ; Guimarães, Thiago Frances
Source: Pharmaceuticals. Unidade: IFSC
Subjects: MALÁRIA, FÁRMACOS, RESSONÂNCIA MAGNÉTICA NUCLEAR, APRENDIZADO COMPUTACIONAL
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BORRÉ, Leandro Bandeira et al. Solid-state NMR characterization of mefloquine resinate complexes designed for taste-masking pediatric formulations. Pharmaceuticals, v. 17, n. 7, p. 870-1-870-16 + supplementary materials, 2024Tradução . . Disponível em: https://doi.org/10.3390/ph17070870. Acesso em: 15 nov. 2024.
APA
Borré, L. B., Sousa, E. G. R. de, Gil, R. A. da S. S., Baptista, M. M., Leitão, A. A., Almeida, J. M. A. R. de, et al. (2024). Solid-state NMR characterization of mefloquine resinate complexes designed for taste-masking pediatric formulations. Pharmaceuticals, 17( 7), 870-1-870-16 + supplementary materials. doi:10.3390/ph17070870
NLM
Borré LB, Sousa EGR de, Gil RA da SS, Baptista MM, Leitão AA, Almeida JMAR de, Carr O, Oliveira Junior ON de, Shimizu FM, Guimarães TF. Solid-state NMR characterization of mefloquine resinate complexes designed for taste-masking pediatric formulations [Internet]. Pharmaceuticals. 2024 ; 17( 7): 870-1-870-16 + supplementary materials.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/ph17070870
Vancouver
Borré LB, Sousa EGR de, Gil RA da SS, Baptista MM, Leitão AA, Almeida JMAR de, Carr O, Oliveira Junior ON de, Shimizu FM, Guimarães TF. Solid-state NMR characterization of mefloquine resinate complexes designed for taste-masking pediatric formulations [Internet]. Pharmaceuticals. 2024 ; 17( 7): 870-1-870-16 + supplementary materials.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/ph17070870
Artigo de periodico
Soundscape characterization using autoencoders and unsupervised learning (2024)
Nieto-Mora, Daniel Alexis ; Oliveira, Maria Cristina Ferreira de ; Sanchez-Giraldo, Camilo ; Duque-Muñoz, Leonardo ; Isaza-Narváez, Claudia ; Martínez-Vargas, Juan David
Source: Sensors. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, APRENDIZAGEM PROFUNDA, RECONHECIMENTO DE PADRÕES, ACÚSTICA, MONITORAMENTO AMBIENTAL
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NIETO-MORA, Daniel Alexis et al. Soundscape characterization using autoencoders and unsupervised learning. Sensors, v. 24, n. 8, p. 1-21, 2024Tradução . . Disponível em: https://doi.org/10.3390/s24082597. Acesso em: 15 nov. 2024.
APA
Nieto-Mora, D. A., Oliveira, M. C. F. de, Sanchez-Giraldo, C., Duque-Muñoz, L., Isaza-Narváez, C., & Martínez-Vargas, J. D. (2024). Soundscape characterization using autoencoders and unsupervised learning. Sensors, 24( 8), 1-21. doi:10.3390/s24082597
NLM
Nieto-Mora DA, Oliveira MCF de, Sanchez-Giraldo C, Duque-Muñoz L, Isaza-Narváez C, Martínez-Vargas JD. Soundscape characterization using autoencoders and unsupervised learning [Internet]. Sensors. 2024 ; 24( 8): 1-21.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/s24082597
Vancouver
Nieto-Mora DA, Oliveira MCF de, Sanchez-Giraldo C, Duque-Muñoz L, Isaza-Narváez C, Martínez-Vargas JD. Soundscape characterization using autoencoders and unsupervised learning [Internet]. Sensors. 2024 ; 24( 8): 1-21.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/s24082597
Artigo de periodico
Current trends in artificial intelligence and bovine mastitis research: a bibliometric review approach (2024)
Mitsunaga, Thatiane Mendes ; Garcia, Breno Luis Nery ; Pereira, Ligia Beatriz Rizzanti ; Costa, Yuri Campos Braga; Silva, Roberto Fray da ; Delbem, Alexandre Cláudio Botazzo ; Santos, Marcos Veiga dos
Source: Animals. Unidades: ESALQ, ICMC, FMVZ
Subjects: APRENDIZADO COMPUTACIONAL, BIBLIOMETRIA, BIG DATA, BOVINOS LEITEIROS, INTELIGÊNCIA ARTIFICIAL, LEITE, VACAS
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MITSUNAGA, Thatiane Mendes et al. Current trends in artificial intelligence and bovine mastitis research: a bibliometric review approach. Animals, v. 14, p. 1-19, 2024Tradução . . Disponível em: https://doi.org/10.3390/ani14142023. Acesso em: 15 nov. 2024.
APA
Mitsunaga, T. M., Garcia, B. L. N., Pereira, L. B. R., Costa, Y. C. B., Silva, R. F. da, Delbem, A. C. B., & Santos, M. V. dos. (2024). Current trends in artificial intelligence and bovine mastitis research: a bibliometric review approach. Animals, 14, 1-19. doi:10.3390/ani14142023
NLM
Mitsunaga TM, Garcia BLN, Pereira LBR, Costa YCB, Silva RF da, Delbem ACB, Santos MV dos. Current trends in artificial intelligence and bovine mastitis research: a bibliometric review approach [Internet]. Animals. 2024 ; 14 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/ani14142023
Vancouver
Mitsunaga TM, Garcia BLN, Pereira LBR, Costa YCB, Silva RF da, Delbem ACB, Santos MV dos. Current trends in artificial intelligence and bovine mastitis research: a bibliometric review approach [Internet]. Animals. 2024 ; 14 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/ani14142023
Artigo de periodico
Performance of neural networks in the prediction of nitrogen nutrition in strawberry plants (2024)
Regazzo, Jamile Raquel ; Silva, Thiago Lima da ; Tavares, Marcos Silva; Sardinha, Edson José de Souza ; Figueiredo, Caroline Goulart ; Couto, Júlia Luna; Gomes, Tamara Maria ; Tech, Adriano Rogério Bruno ; Baesso, Murilo Mesquita
Source: AgriEngineering. Unidades: FZEA, ESALQ
Subjects: ADUBAÇÃO FOLIAR, APRENDIZADO COMPUTACIONAL, FERTILIZANTES NITROGENADOS, MORANGO, NITROGÊNIO, NUTRIÇÃO VEGETAL, REDES NEURAIS
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ABNT
REGAZZO, Jamile Raquel et al. Performance of neural networks in the prediction of nitrogen nutrition in strawberry plants. AgriEngineering, v. 6, p. 1760–1770, 2024Tradução . . Disponível em: https://doi.org/10.3390/agriengineering6020102. Acesso em: 15 nov. 2024.
APA
Regazzo, J. R., Silva, T. L. da, Tavares, M. S., Sardinha, E. J. de S., Figueiredo, C. G., Couto, J. L., et al. (2024). Performance of neural networks in the prediction of nitrogen nutrition in strawberry plants. AgriEngineering, 6, 1760–1770. doi:10.3390/agriengineering6020102
NLM
Regazzo JR, Silva TL da, Tavares MS, Sardinha EJ de S, Figueiredo CG, Couto JL, Gomes TM, Tech ARB, Baesso MM. Performance of neural networks in the prediction of nitrogen nutrition in strawberry plants [Internet]. AgriEngineering. 2024 ; 6 1760–1770.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agriengineering6020102
Vancouver
Regazzo JR, Silva TL da, Tavares MS, Sardinha EJ de S, Figueiredo CG, Couto JL, Gomes TM, Tech ARB, Baesso MM. Performance of neural networks in the prediction of nitrogen nutrition in strawberry plants [Internet]. AgriEngineering. 2024 ; 6 1760–1770.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agriengineering6020102
Trabalho de evento
Being agile in a data science project (2024)
Ferreira, Renato Cordeiro ; Alves, Isaque ; Alves, Samara; Goldman, Alfredo
Source: Proceedings. Conference titles: International Conference on Agile Software Development - XP. Unidade: IME
Subjects: MÉTODOS ÁGEIS, APRENDIZADO COMPUTACIONAL, ENGENHARIA DE SOFTWARE
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ABNT
FERREIRA, Renato Cordeiro et al. Being agile in a data science project. 2024, Anais.. Cham: Springer, 2024. Disponível em: https://doi.org/10.1007/978-3-031-48550-3_6. Acesso em: 15 nov. 2024.
APA
Ferreira, R. C., Alves, I., Alves, S., & Goldman, A. (2024). Being agile in a data science project. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-48550-3_6
NLM
Ferreira RC, Alves I, Alves S, Goldman A. Being agile in a data science project [Internet]. Proceedings. 2024 ;[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-48550-3_6
Vancouver
Ferreira RC, Alves I, Alves S, Goldman A. Being agile in a data science project [Internet]. Proceedings. 2024 ;[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-48550-3_6
Artigo de periodico
Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms (2024)
Patiño, Claudia Patricia Barrera ; Soares, Jennifer Machado ; Blanco, Kate Cristina ; Bagnato, Vanderlei Salvador
Source: Antibiotics. Unidade: IFSC
Subjects: APRENDIZADO COMPUTACIONAL, RESISTÊNCIA MICROBIANA ÀS DROGAS, STREPTOCOCCUS MUTANS, STREPTOCOCCUS PYOGENES, ESCHERICHIA COLI
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ABNT
PATIÑO, Claudia Patricia Barrera et al. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms. Antibiotics, v. 13, n. 9, p. 821-1-821-21 + supplementary materials, 2024Tradução . . Disponível em: https://doi.org/10.3390/antibiotics13090821. Acesso em: 15 nov. 2024.
APA
Patiño, C. P. B., Soares, J. M., Blanco, K. C., & Bagnato, V. S. (2024). Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms. Antibiotics, 13( 9), 821-1-821-21 + supplementary materials. doi:10.3390/antibiotics13090821
NLM
Patiño CPB, Soares JM, Blanco KC, Bagnato VS. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms [Internet]. Antibiotics. 2024 ; 13( 9): 821-1-821-21 + supplementary materials.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/antibiotics13090821
Vancouver
Patiño CPB, Soares JM, Blanco KC, Bagnato VS. Machine learning in FTIR spectrum for the identification of antibiotic resistance: a demonstration with different species of microorganisms [Internet]. Antibiotics. 2024 ; 13( 9): 821-1-821-21 + supplementary materials.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/antibiotics13090821
Trabalho de evento-anais periodico
Geolocalized transductive graph-based regression applied to sustainability indicators prediction (2024)
Nespolo, Renan Guilherme ; Valejo, Alan Demetrius Baria ; Lopes, Alneu de Andrade
Source: Lecture Notes in Networks and Systems. Conference titles: International Conference on Intelligent Systems Design and Applications - ISDA. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), TEORIA DOS GRAFOS, REGRESSÃO LINEAR, SUSTENTABILIDADE
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ABNT
NESPOLO, Renan Guilherme e VALEJO, Alan Demetrius Baria e LOPES, Alneu de Andrade. Geolocalized transductive graph-based regression applied to sustainability indicators prediction. Lecture Notes in Networks and Systems. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-64847-2_44. Acesso em: 15 nov. 2024. , 2024
APA
Nespolo, R. G., Valejo, A. D. B., & Lopes, A. de A. (2024). Geolocalized transductive graph-based regression applied to sustainability indicators prediction. Lecture Notes in Networks and Systems. Cham: Springer. doi:10.1007/978-3-031-64847-2_44
NLM
Nespolo RG, Valejo ADB, Lopes A de A. Geolocalized transductive graph-based regression applied to sustainability indicators prediction [Internet]. Lecture Notes in Networks and Systems. 2024 ; 1050 471-482.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-64847-2_44
Vancouver
Nespolo RG, Valejo ADB, Lopes A de A. Geolocalized transductive graph-based regression applied to sustainability indicators prediction [Internet]. Lecture Notes in Networks and Systems. 2024 ; 1050 471-482.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-64847-2_44
Trabalho de evento-anais periodico
Item response theory in sample reweighting to build fairer classifiers (2024)
Minatel, Diego ; Santos, Nícolas Roque dos ; Silva, Vinícius Ferreira da; Curi, Mariana ; Lopes, Alneu de Andrade
Source: Communications in Computer and Information Science. Conference titles: Annual International Conference on Information Management and Big Data - SIMBig. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS PARA PROCESSAMENTO, TEORIA DE RESPOSTA AO ITEM
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ABNT
MINATEL, Diego et al. Item response theory in sample reweighting to build fairer classifiers. Communications in Computer and Information Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-63616-5_14. Acesso em: 15 nov. 2024. , 2024
APA
Minatel, D., Santos, N. R. dos, Silva, V. F. da, Curi, M., & Lopes, A. de A. (2024). Item response theory in sample reweighting to build fairer classifiers. Communications in Computer and Information Science. Cham: Springer. doi:10.1007/978-3-031-63616-5_14
NLM
Minatel D, Santos NR dos, Silva VF da, Curi M, Lopes A de A. Item response theory in sample reweighting to build fairer classifiers [Internet]. Communications in Computer and Information Science. 2024 ; 2142 184-198.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-63616-5_14
Vancouver
Minatel D, Santos NR dos, Silva VF da, Curi M, Lopes A de A. Item response theory in sample reweighting to build fairer classifiers [Internet]. Communications in Computer and Information Science. 2024 ; 2142 184-198.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-63616-5_14
Artigo de periodico
The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols (2024)
Lima, Bruna Coelho de ; Demattê, José Alexandre Melo ; Santos, Carlos H. dos; Tiritan, Carlos S; Poppiel, Raul Roberto ; Nanni, Marcos R. ; Falcioni, Renan ; Oliveira, Caio A. de; Vedana, Nicole G; Zimmermann, Guilherme; Reis, Amanda S
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, ESPECTROSCOPIA, FLUORESCÊNCIA, RAIOS X, MATÉRIA ORGÂNICA DO SOLO, NITROGÊNIO, SOLO ARENOSO
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ABNT
LIMA, Bruna Coelho de et al. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols. Remote Sensing, v. 16, p. 1-19, 2024Tradução . . Disponível em: https://doi.org/10.3390/rs16163009. Acesso em: 15 nov. 2024.
APA
Lima, B. C. de, Demattê, J. A. M., Santos, C. H. dos, Tiritan, C. S., Poppiel, R. R., Nanni, M. R., et al. (2024). The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols. Remote Sensing, 16, 1-19. doi:10.3390/rs16163009
NLM
Lima BC de, Demattê JAM, Santos CH dos, Tiritan CS, Poppiel RR, Nanni MR, Falcioni R, Oliveira CA de, Vedana NG, Zimmermann G, Reis AS. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols [Internet]. Remote Sensing. 2024 ; 16 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16163009
Vancouver
Lima BC de, Demattê JAM, Santos CH dos, Tiritan CS, Poppiel RR, Nanni MR, Falcioni R, Oliveira CA de, Vedana NG, Zimmermann G, Reis AS. The use of Vis-NIR-SWIR spectroscopy and X-ray fluorescence in the development of predictive models: a step forward in the quantification of nitrogen, total organic carbon and humic fractions in ferralsols [Internet]. Remote Sensing. 2024 ; 16 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/rs16163009
Artigo de periodico
Learning from machine learning: the case of band-gap directness in semiconductors (2024)
Ogoshi, Elton ; Popolin Neto, Mário ; Acosta, Carlos Mera; Nascimento, Gabriel de Miranda ; Rodrigues, João Nuno Barbosa; Oliveira Junior, Osvaldo Novais de ; Paulovich, Fernando Vieira ; Dalpian, Gustavo Martini
Source: Discover Materials. Unidades: IFSC, IF, ICMC
Subjects: BIG DATA, INTERNET DAS COISAS, APRENDIZADO COMPUTACIONAL
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ABNT
OGOSHI, Elton et al. Learning from machine learning: the case of band-gap directness in semiconductors. Discover Materials, v. 4, p. 6-1-6-14, 2024Tradução . . Disponível em: https://doi.org/10.1007/s43939-024-00073-x. Acesso em: 15 nov. 2024.
APA
Ogoshi, E., Popolin Neto, M., Acosta, C. M., Nascimento, G. de M., Rodrigues, J. N. B., Oliveira Junior, O. N. de, et al. (2024). Learning from machine learning: the case of band-gap directness in semiconductors. Discover Materials, 4, 6-1-6-14. doi:10.1007/s43939-024-00073-x
NLM
Ogoshi E, Popolin Neto M, Acosta CM, Nascimento G de M, Rodrigues JNB, Oliveira Junior ON de, Paulovich FV, Dalpian GM. Learning from machine learning: the case of band-gap directness in semiconductors [Internet]. Discover Materials. 2024 ; 4 6-1-6-14.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/s43939-024-00073-x
Vancouver
Ogoshi E, Popolin Neto M, Acosta CM, Nascimento G de M, Rodrigues JNB, Oliveira Junior ON de, Paulovich FV, Dalpian GM. Learning from machine learning: the case of band-gap directness in semiconductors [Internet]. Discover Materials. 2024 ; 4 6-1-6-14.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/s43939-024-00073-x
Artigo de periodico
Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series (2024)
Souza, Flavio Mauricio da Cunha ; Rocha Filho, Geraldo Pereira ; Guimarães, Frederico Gadelha ; Meneguette, Rodolfo Ipolito ; Pessin, Gustavo
Source: Information. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, TOMADA DE DECISÃO, APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE LINGUAGEM NATURAL, MINÉRIOS, FERRO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Flavio Mauricio da Cunha et al. Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series. Information, v. 15, n. 5, p. 1-18, 2024Tradução . . Disponível em: https://doi.org/10.3390/info15050251. Acesso em: 15 nov. 2024.
APA
Souza, F. M. da C., Rocha Filho, G. P., Guimarães, F. G., Meneguette, R. I., & Pessin, G. (2024). Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series. Information, 15( 5), 1-18. doi:10.3390/info15050251
NLM
Souza FM da C, Rocha Filho GP, Guimarães FG, Meneguette RI, Pessin G. Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series [Internet]. Information. 2024 ; 15( 5): 1-18.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/info15050251
Vancouver
Souza FM da C, Rocha Filho GP, Guimarães FG, Meneguette RI, Pessin G. Navigating market sentiments: a novel approach to iron ore price forecasting with weighted fuzzy time series [Internet]. Information. 2024 ; 15( 5): 1-18.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/info15050251
Artigo de periodico
Assessing credibility in Bayesian networks structure learning (2024)
Barth, Vitor Bruno de Oliveira; Serrão, Fábio Viadanna; Maciel, Carlos Dias
Source: Entropy. Unidade: EESC
Subjects: INFERÊNCIA BAYESIANA, INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, ENGENHARIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARTH, Vitor Bruno de Oliveira e SERRÃO, Fábio Viadanna e MACIEL, Carlos Dias. Assessing credibility in Bayesian networks structure learning. Entropy, v. 26, n. 10, p. 1-22, 2024Tradução . . Disponível em: https://dx.doi.org/10.3390/e26100829. Acesso em: 15 nov. 2024.
APA
Barth, V. B. de O., Serrão, F. V., & Maciel, C. D. (2024). Assessing credibility in Bayesian networks structure learning. Entropy, 26( 10), 1-22. doi:10.3390/e26100829
NLM
Barth VB de O, Serrão FV, Maciel CD. Assessing credibility in Bayesian networks structure learning [Internet]. Entropy. 2024 ; 26( 10): 1-22.[citado 2024 nov. 15 ] Available from: https://dx.doi.org/10.3390/e26100829
Vancouver
Barth VB de O, Serrão FV, Maciel CD. Assessing credibility in Bayesian networks structure learning [Internet]. Entropy. 2024 ; 26( 10): 1-22.[citado 2024 nov. 15 ] Available from: https://dx.doi.org/10.3390/e26100829
Artigo de periodico
Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy (2024)
Tavares, Marcos Silva ; Silva, Carlos Augusto Alves Cardoso ; Regazzo, Jamile Raquel ; Sardinha, Edson José de Souza ; Silva, Thiago Lima da ; Fiorio, Peterson Ricardo ; Baesso, Murilo Mesquita
Source: Agronomy. Unidades: FZEA, ESALQ
Subjects: ALGORITMOS, AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, FEIJÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAVARES, Marcos Silva et al. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, v. 14, p. 1-14, 2024Tradução . . Disponível em: https://doi.org/10.3390/agronomy14081634. Acesso em: 15 nov. 2024.
APA
Tavares, M. S., Silva, C. A. A. C., Regazzo, J. R., Sardinha, E. J. de S., Silva, T. L. da, Fiorio, P. R., & Baesso, M. M. (2024). Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy. Agronomy, 14, 1-14. doi:10.3390/agronomy14081634
NLM
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agronomy14081634
Vancouver
Tavares MS, Silva CAAC, Regazzo JR, Sardinha EJ de S, Silva TL da, Fiorio PR, Baesso MM. Performance of machine learning models in predicting common bean (Phaseolus vulgaris L.) crop nitrogen using NIR spectroscopy [Internet]. Agronomy. 2024 ; 14 1-14.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/agronomy14081634
Trabalho de evento-anais periodico
A study of transductive graph-based regression (2024)
Nespolo, Renan Guilherme ; Valejo, Alan Demetrius Baria ; Lopes, Alneu de Andrade
Source: Lecture Notes in Networks and Systems. Conference titles: International Conference on Intelligent Systems Design and Applications - ISDA. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, TEORIA DOS GRAFOS, REGRESSÃO LINEAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NESPOLO, Renan Guilherme e VALEJO, Alan Demetrius Baria e LOPES, Alneu de Andrade. A study of transductive graph-based regression. Lecture Notes in Networks and Systems. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-64836-6_34. Acesso em: 15 nov. 2024. , 2024
APA
Nespolo, R. G., Valejo, A. D. B., & Lopes, A. de A. (2024). A study of transductive graph-based regression. Lecture Notes in Networks and Systems. Cham: Springer. doi:10.1007/978-3-031-64836-6_34
NLM
Nespolo RG, Valejo ADB, Lopes A de A. A study of transductive graph-based regression [Internet]. Lecture Notes in Networks and Systems. 2024 ; 1047 350-359.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-64836-6_34
Vancouver
Nespolo RG, Valejo ADB, Lopes A de A. A study of transductive graph-based regression [Internet]. Lecture Notes in Networks and Systems. 2024 ; 1047 350-359.[citado 2024 nov. 15 ] Available from: https://doi.org/10.1007/978-3-031-64836-6_34
Artigo de periodico
Many-objectives optimization: a machine learning approach for reducing the number of objectives (2023)
Gaspar-Cunha, António ; Costa, Paulo; Monaco, Francisco José ; Delbem, Alexandre Cláudio Botazzo
Source: Mathematical and Computational Applications. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BENCHMARKS, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GASPAR-CUNHA, António et al. Many-objectives optimization: a machine learning approach for reducing the number of objectives. Mathematical and Computational Applications, v. 28, p. 1-19, 2023Tradução . . Disponível em: https://doi.org/10.3390/mca28010017. Acesso em: 15 nov. 2024.
APA
Gaspar-Cunha, A., Costa, P., Monaco, F. J., & Delbem, A. C. B. (2023). Many-objectives optimization: a machine learning approach for reducing the number of objectives. Mathematical and Computational Applications, 28, 1-19. doi:10.3390/mca28010017
NLM
Gaspar-Cunha A, Costa P, Monaco FJ, Delbem ACB. Many-objectives optimization: a machine learning approach for reducing the number of objectives [Internet]. Mathematical and Computational Applications. 2023 ; 28 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/mca28010017
Vancouver
Gaspar-Cunha A, Costa P, Monaco FJ, Delbem ACB. Many-objectives optimization: a machine learning approach for reducing the number of objectives [Internet]. Mathematical and Computational Applications. 2023 ; 28 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/mca28010017
Artigo de periodico
Analysis of judiciary expenditure and productivity using machine learning techniques (2023)
Vasconcelos, Fernando Freire ; Sátiro, Renato Máximo ; Fávero, Luiz Paulo Lopes ; Bortoloto, Gabriela Troyano ; Corrêa, Hamilton Luiz
Source: Mathematics. Unidades: FEA, ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, DESPESAS PROCESSUAIS, PRODUTIVIDADE, REDES NEURAIS, SISTEMA JUDICIÁRIO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VASCONCELOS, Fernando Freire et al. Analysis of judiciary expenditure and productivity using machine learning techniques. Mathematics, v. 11, p. 1-19, 2023Tradução . . Disponível em: https://doi.org/10.3390/math11143195. Acesso em: 15 nov. 2024.
APA
Vasconcelos, F. F., Sátiro, R. M., Fávero, L. P. L., Bortoloto, G. T., & Corrêa, H. L. (2023). Analysis of judiciary expenditure and productivity using machine learning techniques. Mathematics, 11, 1-19. doi:10.3390/math11143195
NLM
Vasconcelos FF, Sátiro RM, Fávero LPL, Bortoloto GT, Corrêa HL. Analysis of judiciary expenditure and productivity using machine learning techniques [Internet]. Mathematics. 2023 ; 11 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/math11143195
Vancouver
Vasconcelos FF, Sátiro RM, Fávero LPL, Bortoloto GT, Corrêa HL. Analysis of judiciary expenditure and productivity using machine learning techniques [Internet]. Mathematics. 2023 ; 11 1-19.[citado 2024 nov. 15 ] Available from: https://doi.org/10.3390/math11143195

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