Uso del aprendizaje de máquina en diferentes sectores industriales

En la actualidad, la confiabilidad y eficiencia de las empresas están estrechamente relacionadas con su capacidad para resolver problemas de manera efectiva. El aprendizaje automático (Machine Learning) ha emergido como una herramienta clave para lograr esta eficiencia, facilitando la optimización d...

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Autores:: Duarte Vargas, Ciro Adrian
Castillo Marquez, David

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2024

Institución:: Universidad Libre

Repositorio:: RIU - Repositorio Institucional UniLibre

Idioma:

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network_acronym_str	RULIBRE2
network_name_str	RIU - Repositorio Institucional UniLibre
repository_id_str
dc.title.spa.fl_str_mv	Uso del aprendizaje de máquina en diferentes sectores industriales
dc.title.alternative.spa.fl_str_mv	Use of Machine Learning in different industrial sectors
title	Uso del aprendizaje de máquina en diferentes sectores industriales
spellingShingle	Uso del aprendizaje de máquina en diferentes sectores industriales redes neuronales artificiales aprendizaje profundo inteligencia artificial aprendizaje automático industria 4.0 artificial neural network deep learning artificial intelligence machine learning industry 4.0 Industria Machine Learning
title_short	Uso del aprendizaje de máquina en diferentes sectores industriales
title_full	Uso del aprendizaje de máquina en diferentes sectores industriales
title_fullStr	Uso del aprendizaje de máquina en diferentes sectores industriales
title_full_unstemmed	Uso del aprendizaje de máquina en diferentes sectores industriales
title_sort	Uso del aprendizaje de máquina en diferentes sectores industriales
dc.creator.fl_str_mv	Duarte Vargas, Ciro Adrian Castillo Marquez, David
dc.contributor.advisor.none.fl_str_mv	Villamizar Estrada, Avilio
dc.contributor.author.none.fl_str_mv	Duarte Vargas, Ciro Adrian Castillo Marquez, David
dc.subject.spa.fl_str_mv	redes neuronales artificiales aprendizaje profundo inteligencia artificial aprendizaje automático industria 4.0
topic	redes neuronales artificiales aprendizaje profundo inteligencia artificial aprendizaje automático industria 4.0 artificial neural network deep learning artificial intelligence machine learning industry 4.0 Industria Machine Learning
dc.subject.subjectenglish.spa.fl_str_mv	artificial neural network deep learning artificial intelligence machine learning industry 4.0
dc.subject.lemb.spa.fl_str_mv	Industria Machine Learning
description	En la actualidad, la confiabilidad y eficiencia de las empresas están estrechamente relacionadas con su capacidad para resolver problemas de manera efectiva. El aprendizaje automático (Machine Learning) ha emergido como una herramienta clave para lograr esta eficiencia, facilitando la optimización de procesos en una variedad de sectores industriales. El artículo explora cómo el aprendizaje automático está revolucionando múltiples industrias al mejorar la automatización de tareas, el análisis de datos y la toma de decisiones. Al integrar inteligencia artificial (IA) y redes neuronales artificiales (Artificial Neural Networks), el aprendizaje automático está contribuyendo significativamente a la creación de procesos más eficientes y adaptativos, avanzando así hacia la Industria 4.0. Además, el artículo presenta varios casos de éxito donde el aprendizaje automático ha sido esencial para alcanzar mejoras destacadas en diferentes sectores. Estos ejemplos demuestran el impacto positivo de esta tecnología en la optimización de operaciones y en la capacidad de las empresas para adaptarse y prosperar en un entorno cada vez más digitalizado.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-10-15T15:36:44Z
dc.date.available.none.fl_str_mv	2024-10-15T15:36:44Z
dc.date.created.none.fl_str_mv	2024-10-09
dc.type.local.spa.fl_str_mv	Tesis de Pregrado
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dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/bachelorThesis
format	http://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10901/30203
url	https://hdl.handle.net/10901/30203
dc.relation.references.spa.fl_str_mv	Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. http://arxiv.org/abs/1605.08695 Abraham, A. (2020). Handbook of measuring system design. Wiley. softcomputing.net Alamro, H., Mtouaa, W., Aljameel, S., Salama, A. S., Hamza, M. A., & Othman, A. Y. (2023). Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity. IEEE Access, 11, 72509–72517. https://doi.org/10.1109/ACCESS.2023.3294263 Ali Abdulalem, S. H. O. T. A. E. E. (2022). MDPI Financial Fraud Detection Based on Machine Learning A. https://www.mdpi.com/2076-3417/12/19/9637 Aljabri, M., Altamimi, H. S., Albelali, S. A., Al-Harbi, M., Alhuraib, H. T., Alotaibi, N. K., Alahmadi, A. A., AlHaidari, F., Mohammad, R. M. A., & Salah, K. (2022). Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions. IEEE Access, 10, 121395–121417. https://doi.org/10.1109/ACCESS.2022.3222307 Aracena, C., Villena, F., Arias, F., & Dunstan, J. (2022). Applications of machine learning in healthcare. Revista Medica Clinica Las Condes, 33(6), 568–575. https://doi.org/10.1016/j.rmclc.2022.10.001 Basáez, E., & Mora, J. (2021). 556 I N F O R M A C I Ó N D E L A R T Í C U L O Salud e inteligencia artificial: ¿cómo hemos evolucionado? Artificial intelligence in health: where are we in 2022? https://doi.org/ Bhuiyan, M. R., & Wree, P. (2023). Animal Behavior for Chicken Identification and Monitoring the Health Condition Using Computer Vision: A Systematic Review. IEEE Access, 11, 126601–126610. https://doi.org/10.1109/access.2023.3331092 Castrillon, S. O., Maria, L., Marín, G., Horacio, H., Villegas, J., César, C., & Escobar, P. (2021). Machine learning aplicado en la clasificación y predicción de la depresión: Una revisión sistemática. Cortés, Y., Berenice, C., Landeta, I., Manuel, J., Chacón, B., Guadalupe, J., Pereyra, A., & Osorio, L. (2017). PDF generado a partir de XML-JATS4R por Redalyc Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto El Entorno de la Industria 4.0: Implicaciones y Perspectivas Futuras. https://www.redalyc.org/articulo.oa?id=94454631006 Donepudi, P. K. (2019). Automation and Machine Learning in Transforming the Financial Industry. Asian Business Review, 9. https://doi.org/10.18034/abr.v9i3.494 Elbasi, E., Mostafa, N., Alarnaout, Z., Zreikat, A. I., Cina, E., Varghese, G., Shdefat, A., Topcu, A. E., Abdelbaki, W., Mathew, S., & Zaki, C. (2023). Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review. In IEEE Access (Vol. 11, pp. 171–202). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3232485 González-García, C. (2018). En qué consiste el aprendizaje automático (machine learning) y qué está aportando a la Neurociencia Cognitiva. Cienc. Cogn, 12(2), 48-50. Gutiérrez, C., & López, M. (2022). Health in the digital age. Revista Medica Clinica Las Condes, 33(6), 562–567. https://doi.org/10.1016/j.rmclc.2022.11.001 Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine Learning and Deep Learning Approaches for CyberSecurity: A Review. In IEEE Access (Vol. 10, pp. 19572–19585). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3151248 Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. https://doi.org/10.1007/s12525-021-00475-2/Published Kumar, V., Saheb, S. S., Preeti, Ghayas, A., Kumari, S., Chandel, J. K., Pandey, S. K., & Kumar, S. (2023). AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector. Big Data Mining and Analytics, 6(4), 478–490. https://doi.org/10.26599/BDMA.2022.9020037 Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1). https://doi.org/10.3390/risks7010029 Masna, N. V. R., Chen, C., Mandal, S., & Bhunia, S. (2019). Robust Authentication of Consumables With Extrinsic Tags and Chemical Fingerprinting. IEEE Access, 7, 14396–14409. https://doi.org/10.1109/ACCESS.2019.2893518 Met, I., Erkoc, A., & Seker, S. E. (2023). Performance, Efficiency, and Target Setting for Bank Branches: Time Series With Automated Machine Learning. IEEE Access, 11, 1000–1010. https://doi.org/10.1109/ACCESS.2022.3233529 NetSec. (2024, 28 mayo). Microsoft 365 Email Spam Filtering. NetSec.News. https://www.netsec.news/microsoft-365-email-spam-filtering/ Ordóñez, H., Cobos, C., & Bucheli, V. (2020). Modelo de machine learning para la predicción de las tendencias de hurto en Colombia Machine learning model for predicting theft trends in Colombia. https://www.proquest.com/openview/fb8bfe36673b48be2d035ee8a035c307/1?pq-origsite=gscholar&cbl=1006393 Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of global health, 8(2). PayPal. (2023). Harnessing the power of machine learning for payment fraud detection. PayPal. https://paypal.com/us/brc/article/payment-fraud-detection-machine-learning Pedrero Victor, Cortez Erick, Grandon Katiuska, & Ureta Joaquin. (2021). Generalidades del Machine Learning y su aplicación en la gestión sanitaria en Servicios de Urgencia. Rev Med Chile, 248–254. https://www.scielo.cl/scielo.php?pid=S0034-98872021000200248&script=sci_arttext Pineda, J. M. (2022). Predictive models in health based on machine learning. Revista Medica Clinica Las Condes, 33(6), 583–590. https://doi.org/10.1016/j.rmclc.2022.11.002 Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches with Special Emphasis on Palm Oil Yield Prediction. In IEEE Access (Vol. 9, pp. 63406–63439). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3075159 Rosero-Montalvo, P. D., Gordillo-Gordillo, C. A., & Hernandez, W. (2023). Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse. IEEE Access, 11, 57843–57853. https://doi.org/10.1109/ACCESS.2023.3283986 Sandoval, L. (2018). ENERO-DICIEMBRE 2018 Derechos Reservados • Escuela Especializada en Ingeniería ITCA-FEPADE (Vol. 11). http://redicces.org.sv/jspui/handle/10972/3626 Shu Yee, O., Sagadevan, S., & Hashimah Ahamed Hassain Malim, N. (2018). Credit Card Fraud Detection Using Machine Learning As Data Mining Technique. 10. https://jtec.utem.edu.my/jtec/article/view/3571 Siemens Healthineers. (2021) Aritificial Intelligence in radiology. https://www.siemens-healthineers.com/medical-imaging/digital-transformation-of-radiology/ai-in-radiology Wijaya, D. R., Syarwan, N. F., Nugraha, M. A., Ananda, D., Fahrudin, T., & Handayani, R. (2023). Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization. IEEE Access, 11, 62484–62495. https://doi.org/10.1109/ACCESS.2023.3286980 Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950 Zaytsev, A. (2023, octubre 28). Case study: How Cargill leverages AI to transform its global operations. AIX \| AI Expert Network; AIX. https://aiexpert.network/case-study-how-cargill-leverages-ai-to-transform-its-global-operations/ Zhang, S., Xie, X., & Xu, Y. (2020). A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access, 8, 128250–128263. https://doi.org/10.1109/ACCESS.2020.3008433 Zhao, G., Jia, P., Huang, C., Zhou, A., & Fang, Y. (2020). A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks. IEEE Access, 8, 65462–65471. https://doi.org/10.1109/ACCESS.2020.2984286
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spelling	Villamizar Estrada, AvilioDuarte Vargas, Ciro AdrianCastillo Marquez, DavidCúcuta2024-10-15T15:36:44Z2024-10-15T15:36:44Z2024-10-09https://hdl.handle.net/10901/30203En la actualidad, la confiabilidad y eficiencia de las empresas están estrechamente relacionadas con su capacidad para resolver problemas de manera efectiva. El aprendizaje automático (Machine Learning) ha emergido como una herramienta clave para lograr esta eficiencia, facilitando la optimización de procesos en una variedad de sectores industriales. El artículo explora cómo el aprendizaje automático está revolucionando múltiples industrias al mejorar la automatización de tareas, el análisis de datos y la toma de decisiones. Al integrar inteligencia artificial (IA) y redes neuronales artificiales (Artificial Neural Networks), el aprendizaje automático está contribuyendo significativamente a la creación de procesos más eficientes y adaptativos, avanzando así hacia la Industria 4.0. Además, el artículo presenta varios casos de éxito donde el aprendizaje automático ha sido esencial para alcanzar mejoras destacadas en diferentes sectores. Estos ejemplos demuestran el impacto positivo de esta tecnología en la optimización de operaciones y en la capacidad de las empresas para adaptarse y prosperar en un entorno cada vez más digitalizado.Universidad Libre - Facultad de Ingenierías - Ingeniería en Tecnologías de la Información y las ComunicacionesCurrently, the reliability and efficiency of companies are increasingly tied to their ability to solve problems effectively within their respective sectors. Machine Learning has emerged as a crucial tool to achieve this efficiency, driving process optimization across various industrial sectors. The article highlights how machine learning is transforming multiple industries by enhancing task automation, data analysis, and decision-making. By leveraging artificial intelligence (AI) and artificial neural networks, machine learning facilitates the creation of more efficient and adaptive processes, significantly contributing to the evolution towards Industry 4.0. The article also presents several success stories where machine learning has been fundamental in achieving notable improvements in different sectors. These examples illustrate the positive impact of this technology on optimizing operations and enhancing the ability of companies to adapt and thrive in an increasingly digitalized environment.PDFhttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadas 2.5 Colombiainfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2redes neuronales artificialesaprendizaje profundointeligencia artificialaprendizaje automáticoindustria 4.0artificial neural networkdeep learningartificial intelligencemachine learningindustry 4.0IndustriaMachine LearningUso del aprendizaje de máquina en diferentes sectores industrialesUse of Machine Learning in different industrial sectorsTesis de Pregradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisAbadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., … Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. http://arxiv.org/abs/1605.08695Abraham, A. (2020). Handbook of measuring system design. Wiley. softcomputing.netAlamro, H., Mtouaa, W., Aljameel, S., Salama, A. S., Hamza, M. A., & Othman, A. Y. (2023). Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cybersecurity. IEEE Access, 11, 72509–72517. https://doi.org/10.1109/ACCESS.2023.3294263Ali Abdulalem, S. H. O. T. A. E. E. (2022). MDPI Financial Fraud Detection Based on Machine Learning A. https://www.mdpi.com/2076-3417/12/19/9637Aljabri, M., Altamimi, H. S., Albelali, S. A., Al-Harbi, M., Alhuraib, H. T., Alotaibi, N. K., Alahmadi, A. A., AlHaidari, F., Mohammad, R. M. A., & Salah, K. (2022). Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions. IEEE Access, 10, 121395–121417. https://doi.org/10.1109/ACCESS.2022.3222307Aracena, C., Villena, F., Arias, F., & Dunstan, J. (2022). Applications of machine learning in healthcare. Revista Medica Clinica Las Condes, 33(6), 568–575. https://doi.org/10.1016/j.rmclc.2022.10.001Basáez, E., & Mora, J. (2021). 556 I N F O R M A C I Ó N D E L A R T Í C U L O Salud e inteligencia artificial: ¿cómo hemos evolucionado? Artificial intelligence in health: where are we in 2022? https://doi.org/Bhuiyan, M. R., & Wree, P. (2023). Animal Behavior for Chicken Identification and Monitoring the Health Condition Using Computer Vision: A Systematic Review. IEEE Access, 11, 126601–126610. https://doi.org/10.1109/access.2023.3331092Castrillon, S. O., Maria, L., Marín, G., Horacio, H., Villegas, J., César, C., & Escobar, P. (2021). Machine learning aplicado en la clasificación y predicción de la depresión: Una revisión sistemática.Cortés, Y., Berenice, C., Landeta, I., Manuel, J., Chacón, B., Guadalupe, J., Pereyra, A., & Osorio, L. (2017). PDF generado a partir de XML-JATS4R por Redalyc Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto El Entorno de la Industria 4.0: Implicaciones y Perspectivas Futuras. https://www.redalyc.org/articulo.oa?id=94454631006Donepudi, P. K. (2019). Automation and Machine Learning in Transforming the Financial Industry. Asian Business Review, 9. https://doi.org/10.18034/abr.v9i3.494Elbasi, E., Mostafa, N., Alarnaout, Z., Zreikat, A. I., Cina, E., Varghese, G., Shdefat, A., Topcu, A. E., Abdelbaki, W., Mathew, S., & Zaki, C. (2023). Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review. In IEEE Access (Vol. 11, pp. 171–202). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3232485González-García, C. (2018). En qué consiste el aprendizaje automático (machine learning) y qué está aportando a la Neurociencia Cognitiva. Cienc. Cogn, 12(2), 48-50.Gutiérrez, C., & López, M. (2022). Health in the digital age. Revista Medica Clinica Las Condes, 33(6), 562–567. https://doi.org/10.1016/j.rmclc.2022.11.001Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine Learning and Deep Learning Approaches for CyberSecurity: A Review. In IEEE Access (Vol. 10, pp. 19572–19585). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3151248Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. https://doi.org/10.1007/s12525-021-00475-2/PublishedKumar, V., Saheb, S. S., Preeti, Ghayas, A., Kumari, S., Chandel, J. K., Pandey, S. K., & Kumar, S. (2023). AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector. Big Data Mining and Analytics, 6(4), 478–490. https://doi.org/10.26599/BDMA.2022.9020037Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1). https://doi.org/10.3390/risks7010029Masna, N. V. R., Chen, C., Mandal, S., & Bhunia, S. (2019). Robust Authentication of Consumables With Extrinsic Tags and Chemical Fingerprinting. IEEE Access, 7, 14396–14409. https://doi.org/10.1109/ACCESS.2019.2893518Met, I., Erkoc, A., & Seker, S. E. (2023). Performance, Efficiency, and Target Setting for Bank Branches: Time Series With Automated Machine Learning. IEEE Access, 11, 1000–1010. https://doi.org/10.1109/ACCESS.2022.3233529NetSec. (2024, 28 mayo). Microsoft 365 Email Spam Filtering. NetSec.News. https://www.netsec.news/microsoft-365-email-spam-filtering/Ordóñez, H., Cobos, C., & Bucheli, V. (2020). Modelo de machine learning para la predicción de las tendencias de hurto en Colombia Machine learning model for predicting theft trends in Colombia. https://www.proquest.com/openview/fb8bfe36673b48be2d035ee8a035c307/1?pq-origsite=gscholar&cbl=1006393Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of global health, 8(2).PayPal. (2023). Harnessing the power of machine learning for payment fraud detection. PayPal. https://paypal.com/us/brc/article/payment-fraud-detection-machine-learningPedrero Victor, Cortez Erick, Grandon Katiuska, & Ureta Joaquin. (2021). Generalidades del Machine Learning y su aplicación en la gestión sanitaria en Servicios de Urgencia. Rev Med Chile, 248–254. https://www.scielo.cl/scielo.php?pid=S0034-98872021000200248&script=sci_arttextPineda, J. M. (2022). Predictive models in health based on machine learning. Revista Medica Clinica Las Condes, 33(6), 583–590. https://doi.org/10.1016/j.rmclc.2022.11.002Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches with Special Emphasis on Palm Oil Yield Prediction. In IEEE Access (Vol. 9, pp. 63406–63439). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3075159Rosero-Montalvo, P. D., Gordillo-Gordillo, C. A., & Hernandez, W. (2023). Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse. IEEE Access, 11, 57843–57853. https://doi.org/10.1109/ACCESS.2023.3283986Sandoval, L. (2018). ENERO-DICIEMBRE 2018 Derechos Reservados • Escuela Especializada en Ingeniería ITCA-FEPADE (Vol. 11). http://redicces.org.sv/jspui/handle/10972/3626Shu Yee, O., Sagadevan, S., & Hashimah Ahamed Hassain Malim, N. (2018). 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IEEE Access, 8, 65462–65471. https://doi.org/10.1109/ACCESS.2020.2984286THUMBNAILPortada.jpgPortada.jpgPortadaimage/jpeg79655http://repository.unilibre.edu.co/bitstream/10901/30203/10/Portada.jpg81ac477a70ab6affe954c5a2fea899fcMD510LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repository.unilibre.edu.co/bitstream/10901/30203/9/license.txt8a4605be74aa9ea9d79846c1fba20a33MD59ORIGINALUso del aprendizaje de máquina en diferentes sectores industriales.pdfUso del aprendizaje de máquina en diferentes sectores industriales.pdfArticuloapplication/pdf418662http://repository.unilibre.edu.co/bitstream/10901/30203/2/Uso%20del%20aprendizaje%20de%20m%c3%a1quina%20en%20diferentes%20sectores%20industriales.pdf8c34aa00c608f3f027c575e969758d9aMD52RESUMEN.pdfRESUMEN.pdfapplication/pdf59570http://repository.unilibre.edu.co/bitstream/10901/30203/3/RESUMEN.pdf90b3906d96d1a59d042d35227a5220feMD53Resolucion David.pdfResolucion David.pdfapplication/pdf750939http://repository.unilibre.edu.co/bitstream/10901/30203/5/Resolucion%20David.pdfd57b30110d71de9bb13624c193eead2eMD55Resolución Ciro.pdfResolución Ciro.pdfapplication/pdf807498http://repository.unilibre.edu.co/bitstream/10901/30203/6/Resolucio%cc%81n%20Ciro.pdf74d0b71d9c203ff341cdb33b7b36c53bMD56Autorización repositorio.pdfAutorización repositorio.pdfapplication/pdf2394349http://repository.unilibre.edu.co/bitstream/10901/30203/8/Autorizaci%c3%b3n%20repositorio.pdfcf57734400df92d89a23a9d4a0f6b0b9MD5810901/30203oai:repository.unilibre.edu.co:10901/302032024-10-15 10:43:09.891Repositorio Institucional Unilibrerepositorio@unilibrebog.edu.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

Uso del aprendizaje de máquina en diferentes sectores industriales

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