Search Results - "Redes Neurales de la Computación"
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Authors: Alberto Guevara-Tirado
Source: Gaceta Médica de México, Vol 161, Iss 3 (2025)
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2
Authors: Alberto Guevara Tirado
Source: Revista Cubana de Medicina Militar, Vol 54, Iss 1 (2025)
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Authors: Alberto Guevara Tirado
Source: Revista del Nacional (Itauguá), Volume: 17, Article number: e1700105, Published: 02 JAN 2025
Subject Terms: redes neurales de la computación, toma de decisiones asistida por computador, diagnóstico por computador, Neural Networks, Computer, Diagnosis, Computer-Assisted, Insulin Resistance, resistencia a la Insulina, glucemia, Glicemia, Decision Making, Computer-Assisted
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Authors: et al.
Contributors: et al.
Subject Terms: Sistemas de Apoyo a Decisiones Clínicas - organización & administración, Decision Support Systems, Clinical - organization & administration, Registros Electrónicos de Salud, Electronic Health Records, Servicio de Urgencia en Hospital - estadística & datos numéricos, Emergency Service, Hospital - statistics & numerical data, Hospitalización, Hospitalization, Modelos Logísticos, Logistic Models, Aprendizaje Automático, Machine Learning, Redes Neurales de la Computación, Neural Networks, Computer, Medición de Riesgo - métodos, Risk Assessment - methods, https://id.nlm.nih.gov/mesh/D020000, https://id.nlm.nih.gov/mesh/D057286, https://id.nlm.nih.gov/mesh/D004636, https://id.nlm.nih.gov/mesh/D006760, https://id.nlm.nih.gov/mesh/D016015, https://id.nlm.nih.gov/mesh/D000069550, https://id.nlm.nih.gov/mesh/D016571, https://id.nlm.nih.gov/mesh/D018570
File Description: 14 páginas; application/pdf
Relation: J. Med. Syst.; 13; 19; 49; Journal of Medical Systems; https://hdl.handle.net/10495/45461
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Authors: Guevara Tirado, Alberto
Source: Revista Cubana de Medicina Militar; Vol. 54 No. 1 (2025): January - March; e025075908 ; Revista Cubana de Medicina Militar; Vol. 54 Núm. 1 (2025): Enero - marzo; e025075908 ; 1561-3046
Subject Terms: abdominal circumference, body mass index, body weigh, computational neural networks, computer-assisted decision making, height, circunferencia abdominal, estatura, índice de masa corporal, peso corporal, redes neurales de la computación, toma de decisiones asistida por computador
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Authors: Alberto Guevara-Tirado
Source: Revista de Patología Respiratoria, Vol 27, Iss 4 (2024)
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Authors: et al.
Source: Revista Brasileira de Medicina do Esporte v.27 n.4 2021
Revista brasileira de medicina do esporte
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)
instacron:SBMEE
Revista Brasileira de Medicina do Esporte, Volume: 27, Issue: 4, Pages: 367-371, Published: 20 AUG 2021Subject Terms: Imágenes biológicas, Reconhecimento de imagem, Biological images, Redes neurales de la computación, Image recognition, Index, Reconocimiento de imagen, 03 medical and health sciences, 0302 clinical medicine, Imagens biológicas, Índice, Neural networks, computer, Redes neurais de computação
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Access URL: https://www.scielo.br/j/rbme/a/Vb73Z5Vbhbx4znsn85LHw9f/?lang=en&format=pdf
https://www.academicoo.com/artigo/analise-de -rede-neural-artificial-na-predicao-do-indice-de -exercicio-aerobio-baseada-em-algoritimo
https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1288608
https://rbme.org/detalhes/1701
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http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922021000400367&lng=en&tlng=en -
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Authors: Min Lin
Source: Revista Brasileira de Medicina do Esporte v.27 n.4 2021
Revista brasileira de medicina do esporte
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)
instacron:SBMEE
Revista Brasileira de Medicina do Esporte, Volume: 27, Issue: 4, Pages: 405-409, Published: 20 AUG 2021Subject Terms: 0209 industrial biotechnology, Redes neurales de la computación, Diagnóstico por imagem, 02 engineering and technology, Exercício físico, Ejercicio físico, 01 natural sciences, Neural networks, computer, 0103 physical sciences, Diagnóstico por Imagen, Diagnostic imaging, Redes neurais de computação, Exercise
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Access URL: https://www.scielo.br/j/rbme/a/KWGmq8gj389XRmtFvQz7mXQ/?lang=en&format=pdf
https://search.bvsalud.org/gim/resource/pt/biblio-1288596
http://rbme.org/detalhes/1709
https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1288596
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Authors:
Source: Revista Colombiana de Cirugía, Vol 38, Iss 3 (2023)
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Authors: et al.
Contributors: et al.
Subject Terms: Inteligencia artificial, Aprendizaje automático, Asbestosis, Mesotelioma maligno, Neoplasia pulmonar, Redes neurales de la computación, Biomarcadores de tumor, Radiografía torácica, Tomografía computarizada por rayos X, Minería de datos, Artificial intelligence, Machine learning, Malignant mesothelioma, Lung neoplasm, Convolutional neural network, Tumor biomarkers, Thoracic radiography, X-ray computed tomography, Data mining, WA450
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Relation: 1. Lipman K, de Gooijer CJ, Boellaard TN, van der Heijden F, Beets-Tan RGH, Bodalal Z, et al. Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid. Eur Radiol. 2023;33(5):3557–65. https://doi.org/10.1007/s00330-022-09304-2; 2. Lima LA de, Abe JM, Martinez AAG, Sakamoto LS, de Lima LP. Application of architecture using AI in the training of a set of pixels of the image at aid decision-making diagnostic cancer. Procedia Comput Sci [Internet]. 2021; 192:1740–9. https://doi.org/10.1016/j.procs.2021.08.179; 3. Alam TM, Shaukat K, Hameed IA, Khan WA, Sarwar MU, Iqbal F, et al. A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining. Biomed Signal Process Control [Internet]. 2021; 68:102726. https://doi.org/10.1016/j.bspc.2021.102726; 4. Chicco D, Rovelli C. Computational prediction of diagnosis and feature selection on mesothelioma patient health records. PLoS One. 2019;14(1). https://doi.org/10.1371/journal.pone.0208737; 5. Koul A, Bawa RK, Kumar Y. Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review. Archives of Computational Methods in Engineering. 2023 Mar 1;30(2):831–64. https://doi.org/10.1007/s11831-022-09818-4; 6. Min Kim H, Ko T, Young Choi I, Myong JP. Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image. Int J Med Inform [Internet]. 2022; 158:104667. https://doi.org/10.1016/j.ijmedinf.2021.; 7. Zadsafar F, Tabrizchi H, Parvizpour S, Razmara J, Lotfi S. A model for mesothelioma cancer diagnosis based on feature selection using Harris hawk optimization algorithm. Computer Methods and Programs in Biomedicine Update [Internet]. 2022; 2:100078. http://dx.doi.org/10.1016/j.cmpbup.2022.100078; 8. Galateau Salle F, Le Stang N, Tirode F, Courtiol P, Nicholson AG, Tsao MS, et al. Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center. Journal of Thoracic Oncology [Internet]. 2020;15(6):1037–53. https://doi.org/10.1016/j.jtho.2020.01.025; 9. Di Gilio A, Catino A, Lombardi A, Palmisani J, Facchini L, Mongelli T, et al. Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways. Cancers (Basel). 2020;12(5). https://doi.org/10.3390/cancers12051262; 11. Agarwal S, Yadav AS, Dinesh V, Vatsav KSS, Prakash KSS, Jaiswal S. By artificial intelligence algorithms and machine learning models to diagnosis cancer. Mater Today Proc [Internet]. 2023;80:2969–75. http://dx.doi.org/10.1016/j.matpr.2021.07.088; 12. Adams SJ, Stone E, Baldwin DR, Vliegenthart R, Lee P, Fintelmann FJ. Lung cancer screening. The Lancet [Internet]. 2023;401(10374):390–408. https://doi.org/10.1016/s0140-6736(22)01694-4; 13. Huang Y, Si Y, Hu B, Zhang Y, Wu S, Wu D, et al. Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images. Comput Biol Med [Internet]. 2022; 150:106137. https://doi.org/10.1016/j.compbiomed.2022.106137; 14. Yin Y, Cui Q, Zhao J, Wu Q, Sun Q, Wang H qiang, et al. Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells. Am J Pathol [Internet]. 2024;194(7):1294–305. https://doi.org/10.1016/j.ajpath.2024.03.013; 15. Alam MS, Wang D, Sowmya A. DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images. Sci Rep. 2024 Dec 1;14(1). https://doi.org/10.1038/s41598-024-61024-3; 16. Eastwood M, Marc ST, Gao X, Sailem H, Offman J, Karteris E, et al. Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data. Artif Intell Med [Internet]. 2023; 143:102628. https://doi.org/10.1016/j.artmed.2023.102628; 17. Thakral G, Gambhir S. Early Detection of Lung Cancer with Low-Dose CT Scan Using Artificial Intelligence: A Comprehensive Survey. SN Comput Sci. 2024 Jun 1;5(5). http://dx.doi.org/10.1007/s42979-024-02811-7; 18. Shenouda M, Gudmundsson E, Li F, Straus CM, Kindler HL, Dudek AZ, et al. Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance). Journal of Imaging Informatics in Medicine [Internet]. 2024 Sep 12 [cited 2024 Nov 19]. https://doi.org/10.1007/s10278-024-01092-z; 19. Moirangthem A, Lepcha OS, Panigrahi R, Brahma B, Bhoi AK. Early Malignant Mesothelioma Detection Using Ensemble of Naive Bayes Under Decorate Ensemble Framework. Journal of The Institution of Engineers (India): Series B. 2024 Apr 1;105(2):251–64. http://dx.doi.org/10.1007/s40031-023-00988-8; 20. Hakkarainen AJ, Randen-Brady R, Wolff H, Mäyränpää MI, Sajantila A. Deep Learning Neural Network-Guided Detection of Asbestos Bodies in Bronchoalveolar Lavage Samples. Acta Cytol. 2023. https://doi.org/10.1159/000534149; 21. Courtiol P, Maussion C, Moarii M, Pronier E, Pilcer S, Sefta M, et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat Med. 2019 Oct 1;25(10):1519–25. https://doi.org/10.1038/s41591-019-0583-3; 22. Zauderer MG, Martin A, Egger J, Rizvi H, Offin M, Rimner A, et al. The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study. Lancet Digit Health [Internet]. 2021;3(9): e565–76. https://doi.org/10.1016/s2589-7500(21)00104-7; 23. Li N, Yang CX, Zhou SC, Song SY, Jin YY, Wang D, et al. Combination of Plasma-Based Metabolomics and Machine Learning Algorithm Provides a Novel Diagnostic Strategy for Malignant Mesothelioma. DIAGNOSTICS. 2021;11(7). https://doi.org/10.3390/diagnostics11071281; 24. Xie XJ, Liu SY, Chen JY, Zhao Y, Jiang J, Wu L, et al. Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation. Lung Cancer [Internet]. 2021; 157:30–9. https://doi.org/10.1016/j.lungcan.2021.04.023; 25. W GLKB, Boellaard TN, J de GC, Bogveradze N, Hong EK, Landolfi F, et al. Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients. J Thorac Imaging. 2024. https://doi.org/10.1097/rti.0000000000000759; 26. Benlala I, D DSB, Dournes G, Menant M, Gramond C, Thaon I, et al. Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects. Int J Environ Res Public Health. 2022. https://doi.org/10.3390/ijerph19031417; 27. Li Y, Cai B, Wang B, Lv Y, He W, Xie X, et al. Differentiating malignant pleural mesothelioma and metastatic pleural disease based on a machine learning model with primary CT signs: A multicentre study. Heliyon [Internet]. 2022;8(11): e11383. https://doi.org/10.1016/j.heliyon.2022.e11383; 28. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020 Oct 1;18(10):2119–26. https://doi.org/10.11124/jbies-20-00167; 29. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med [Internet]. 2018 Oct 2 [cited 2025 Jan 22];169(7):467–73. https://doi.org/10.7326/m18-0850; 30. Zhang Q, Wang H, Yoon SW, Won D, Srihari K. Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks. Procedia Manuf [Internet]. 2019; 39:363–70. http://dx.doi.org/10.1016/j.promfg.2020.01.375; 31. Sousa AM, Castelo-Fernandez C, Osaku D, Bagatin E, Reis F, Falcao AX. An Approach for Asbestos-related Pleural Plaque Detection. Annu Int Conf IEEE Eng Med Biol Soc. 2020. https://doi.org/10.1109/embc44109.2020.9176605; 32. Huang ML, Chou YC. Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network. Comput Methods Programs Biomed [Internet]. 2019; 180:105016. https://doi.org/10.1016/j.cmpb.2019.105016; 33. Alam TM, Shaukat K, Mahboob H, Sarwar MU, Iqbal F, Nasir A, et al. A Machine Learning Approach for Identification of Malignant Mesothelioma Etiological Factors in an Imbalanced Dataset. COMPUTER JOURNAL. 2022;65(7):1740–51. https://doi.org/10.1093/comjnl/bxab015; 34. Gupta S, Gupta MK. Computational Model for Prediction of Malignant Mesothelioma Diagnosis. COMPUTER JOURNAL. 2023;66(1):86–100. http://dx.doi.org/10.1093/comjnl/bxab146; 35. Eastwood M, Sailem H, Marc ST, Gao X, Offman J, Karteris E, et al. MesoGraph: Automatic profiling of mesothelioma subtypes from histological images. Cell Rep Med [Internet]. 2023;4(10):101226. https://doi.org/10.1016/j.xcrm.2023.101226; 36. Devnath L, Luo S, Summons P, Wang D. Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs. Comput Biol Med [Internet]. 2021; 129:104125. https://doi.org/10.1016/j.compbiomed.2020.104125; 37. Jurmeister P, Leitheiser M, Wolkenstein P, Klauschen F, Capper D, Brcic L. DNA methylation-based machine learning classification distinguishes pleural mesothelioma from chronic pleuritis, pleural carcinosis, and pleomorphic lung carcinomas. Lung Cancer [Internet]. 2022; 170:105–13. https://doi.org/10.1016/j.lungcan.2022.06.008; 38. Cohen MW, Ghidotti A, Regazzoni D. Bi-level Analysis of Computed Tomography Images of Malignant Pleural Mesothelioma: Deep Learning-Based Classification and Subsequent Three-Dimensional Reconstruction. J Comput Inf Sci Eng. 2024;24(6). https://doi.org/10.1115/1.4064410; 39. Yang HY. Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals. PLoS One. 2019;14(4). https://doi.org/10.1371/journal.pone.0214808; 40. Zhang L, Rong R, Li Q, Yang DM, Yao B, Luo D, et al. A deep learning-based model for screening and staging pneumoconiosis. Sci Rep. 2021 Dec 1;11(1). https://doi.org/10.1038/s41598-020-77924-z; 41. Alam MdS, Wang D, Sowmya A. AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images. Artif Intell Med [Internet]. 2024; 154:102917. https://doi.org/10.1016/j.artmed.2024.102917; 42. Zhang Y, Qian F, Teng J, Wang H, Yu H, Chen Q, et al. China lung cancer screening (CLUS) version 2.0 with new techniques implemented: Artificial intelligence, circulating molecular biomarkers and autofluorescence bronchoscopy. Lung Cancer [Internet]. 2023; 181:107262. https://doi.org/10.1016/j.lungcan.2023.107262; https://hdl.handle.net/20.500.12495/13938; instname:Universidad El Bosque; reponame:Repositorio Institucional Universidad El Bosque; repourl:https://repositorio.unbosque.edu.co
Availability: https://hdl.handle.net/20.500.12495/13938
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Authors: et al.
Subject Terms: Odontología, Ortodoncia, Inteligencia artificial, Tomógrafos Computarizados por Rayos X, Macrodatos, Toma de Decisiones Clínicas, Informática, Aprendizaje automático, Redes Neurales de la Computación, Diseño Asistido por Computadora, Impresión Tridimensional
File Description: application/pdf; 21-25
Availability: http://sedici.unlp.edu.ar/handle/10915/155638
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Authors: et al.
Contributors: et al.
Subject Terms: Redes Neurales de la Computación, Neural Networks, Computer, Cinética, Kinetics, Tilapia, Hidrólisis enzimática, Enzymatic hydrolysis, Oreochromis, http://aims.fao.org/aos/agrovoc/c_27512, http://aims.fao.org/aos/agrovoc/c_26596, https://id.nlm.nih.gov/mesh/D016571, https://id.nlm.nih.gov/mesh/D007700, https://id.nlm.nih.gov/mesh/D017210
File Description: 10 páginas; application/pdf
Relation: Int. Food. Res. J.; 1410; 1401; 29; International Food Research Journal; https://hdl.handle.net/10495/39527
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Authors: et al.
Source: SEDICI (UNLP)
Universidad Nacional de La Plata
instacron:UNLPSubject Terms: Ortodoncia, Informática, Diseño Asistido por Computadora, Toma de Decisiones Clínicas, Tomógrafos Computarizados por Rayos X, Odontología, Redes Neurales de la Computación, Impresión Tridimensional, Macrodatos, Inteligencia artificial, Aprendizaje automático
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Access URL: http://sedici.unlp.edu.ar/handle/10915/155638
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Authors: et al.
Contributors: et al.
Subject Terms: Direct interface circuits, Interface sensor, Resistive sensor, Time-based measurement, Calibration methods, Uncertainty, Incertidumbre, Redes neurales de la computación, Computadoras de mano, Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Therapeutics::Patient Care::Hospitalization::Patient Discharge, Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Probability::Uncertainty, Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans, Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Neural Networks (Computer), Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithms, Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Computer Systems::Computers::Microcomputers::Computers, Handheld, Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Computer Systems::Computers::Computers, Hybrid::Analog-Digital Conversion, Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Computer Systems::Computers, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Calibration
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Relation: https://www.mdpi.com/1424-8220/21/4/1524/htm#; Hidalgo-López JA, Oballe-Peinado Ó, Castellanos-Ramos J, Sánchez-Durán JA. Two-Capacitor Direct Interface Circuit for Resistive Sensor Measurements. Sensors. 2021 Feb 22;21(4):1524; http://hdl.handle.net/10668/3771; PMC7926314
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Authors: et al.
Contributors: et al.
Subject Terms: Face recognition, Assistant mobile robots, Cross-pose face recognition, MAPIR Faces, Human-robot interaction, Reconocimiento facial, Redes neurales de la computación, Medical Subject Headings::Anatomy::Body Regions::Head::Face, Medical Subject Headings::Anatomy::Body Regions::Head, Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans, Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Artificial Intelligence::Neural Networks (Computer), Medical Subject Headings::Disciplines and Occupations::Natural Science Disciplines::Physics::Electronics::Robotics, Medical Subject Headings::Health Care::Health Services Administration::Patient Care Management::Delivery of Health Care
File Description: application/pdf
Relation: https://www.mdpi.com/1424-8220/21/2/659/htm; Baltanas SF, Ruiz-Sarmiento JR, Gonzalez-Jimenez J. Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots. Sensors (Basel). 2021 Jan 19;21(2):659; http://hdl.handle.net/10668/3965; PMC7833400
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Source: file:///D:/backup2017/Escritorio/Establishing_an_acquisition_and_processing.89.pdf
Subject Terms: Functional dynamic network connectivity, Functional magnetic resonance imaging, Low-to-middle income countries, Neural networks, Resting state networks, Imagen por resonancia magnética, Acceso a internet, Pobreza, Redes neurales de la computación
File Description: 7 p.; application/pdf
Relation: Medicine (Baltimore); 28; 99; https://repositorio.fucsalud.edu.co/handle/001/2622
Availability: https://repositorio.fucsalud.edu.co/handle/001/2622
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Authors:
Source: Cadernos de Saúde Pública, Vol 36, Iss 8 (2020)
Cadernos de Saúde Pública v.36 n.8 2020
Cadernos de Saúde Pública
Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
Cadernos de Saúde Pública, Volume: 36, Issue: 8, Article number: e00038319, Published: 28 AUG 2020Subject Terms: Social Vulnerability, Vulnerabilidade Social, Redes Neurales de la Computación, Vulnerabilidad Social, Indicadores Sociales, 03 medical and health sciences, Social Indicators, Computer Neural Networks, Medicine, Indicadores Sociais, Redes Neurais de Computadores, Public aspects of medicine, RA1-1270, 0305 other medical science
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Access URL: http://www.scielo.br/j/csp/a/rTgtTmXkG4BJXJhPYVLBfQp/?format=pdf&lang=pt
https://doaj.org/article/a9b4b5111df642ef8ac405a2cd548a81
https://www.ncbi.nlm.nih.gov/pubmed/32876125
https://www.scielo.br/pdf/csp/v36n8/1678-4464-csp-36-08-e00038319.pdf
http://www.scielo.br/j/csp/a/rTgtTmXkG4BJXJhPYVLBfQp/
https://www.scielosp.org/article/csp/2020.v36n8/e00038319/
https://www.scielo.br/j/csp/a/rTgtTmXkG4BJXJhPYVLBfQp/
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000805012
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000805012&lng=en&tlng=en -
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Authors:
Contributors:
Subject Terms: Aprendizaje Profundo, Deep Learning, Redes Neurales de la Computación, Neural Networks, Computer, Seguridad alimentaria, Food Security, Salmonella, Aprendizaje automático (inteligencia artificial), Machine learning, Avicultura, Aviculture, Procesamiento de imágenes, Image processing, https://id.nlm.nih.gov/mesh/D000077321, https://id.nlm.nih.gov/mesh/D016571, https://id.nlm.nih.gov/mesh/D000082302, https://id.nlm.nih.gov/mesh/D012475
File Description: 58 páginas; application/pdf
Availability: https://hdl.handle.net/10495/40366
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Contributors:
Subject Terms: Inclusión Social, Social Inclusion, Lengua de Signos, Sign Language, Redes Neurales de la Computación, Neural Networks, Computer, Aprendizaje automático (inteligencia artificial), Machine learning, Discapacidad auditiva, https://id.nlm.nih.gov/mesh/D000083644, https://id.nlm.nih.gov/mesh/D012813, https://id.nlm.nih.gov/mesh/D016571
File Description: 74 páginas; application/pdf
Availability: https://hdl.handle.net/10495/38597
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Authors: et al.
Contributors: et al.
Subject Terms: Kernel density estimation, Aprendizaje profundo, Kernel methods, Kernel Density Esitmation Approximation, Aproximaciones de la estimación de la densidad del nucleo, Matriz de densidad, Redes Neurales de la Computación, Métodos del núcleo, Density Matrix, Estimación de la densidad del núcleo, Estimación de la densidad neuronal, Aprendizaje a gran escala, Machine Learning, Neural Density Estimation, Deep Learning, Deep Kernel, Large-scale learning, Neural Networks, Computer, Random Fourier Features, 000 - Ciencias de la computación, información y obras generales
File Description: xv, 113 páginas; application/pdf
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