Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase

Debido al aumento de estudiantes en la universidad y el gran tamaño de los cursos, en especial los de cátedra de la facultad de medicina, se evidencia la necesidad de agilizar el proceso de toma de asistencia de los estudiantes y docentes. En este trabajo se especifican los requerimientos de un sist...

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Autores:
Jurado García, Miguel Eugenio
Padilla Porras, Andrés Felipe
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2018
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/1315
Acceso en línea:
http://hdl.handle.net/20.500.12749/1315
Palabra clave:
Perception of faces
Facial recognition
Neural Networks
Computers
Artificial intelligence
Systems Engineering
Investigations
Analysis
Artificial vision
Automation
Neural networks
Artificial intelligence
Percepción de caras
Reconocimiento facial
Redes neuronales
Computadores
Inteligencia artificial
Ingeniería de sistemas
Investigaciones
Análisis
Inteligencia artificial
Redes neuronales
Automatización
Visión artificial
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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dc.title.spa.fl_str_mv Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
dc.title.translated.eng.fl_str_mv Facial recognition system with neural networks for taking assistance in classrooms
title Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
spellingShingle Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
Perception of faces
Facial recognition
Neural Networks
Computers
Artificial intelligence
Systems Engineering
Investigations
Analysis
Artificial vision
Automation
Neural networks
Artificial intelligence
Percepción de caras
Reconocimiento facial
Redes neuronales
Computadores
Inteligencia artificial
Ingeniería de sistemas
Investigaciones
Análisis
Inteligencia artificial
Redes neuronales
Automatización
Visión artificial
title_short Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
title_full Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
title_fullStr Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
title_full_unstemmed Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
title_sort Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase
dc.creator.fl_str_mv Jurado García, Miguel Eugenio
Padilla Porras, Andrés Felipe
dc.contributor.advisor.spa.fl_str_mv Lobo Quintero, René Alejandro
dc.contributor.author.spa.fl_str_mv Jurado García, Miguel Eugenio
Padilla Porras, Andrés Felipe
dc.contributor.cvlac.*.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001007017
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dc.contributor.orcid.*.fl_str_mv https://orcid.org/0000-0003-2989-5357
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación Preservación e Intercambio Digital de Información y Conocimiento - Prisma
Grupo de Investigaciones Clínicas
dc.subject.keywords.eng.fl_str_mv Perception of faces
Facial recognition
Neural Networks
Computers
Artificial intelligence
Systems Engineering
Investigations
Analysis
Artificial vision
Automation
Neural networks
Artificial intelligence
topic Perception of faces
Facial recognition
Neural Networks
Computers
Artificial intelligence
Systems Engineering
Investigations
Analysis
Artificial vision
Automation
Neural networks
Artificial intelligence
Percepción de caras
Reconocimiento facial
Redes neuronales
Computadores
Inteligencia artificial
Ingeniería de sistemas
Investigaciones
Análisis
Inteligencia artificial
Redes neuronales
Automatización
Visión artificial
dc.subject.lemb.spa.fl_str_mv Percepción de caras
Reconocimiento facial
Redes neuronales
Computadores
Inteligencia artificial
Ingeniería de sistemas
Investigaciones
Análisis
dc.subject.proposal.none.fl_str_mv Inteligencia artificial
Redes neuronales
Automatización
Visión artificial
description Debido al aumento de estudiantes en la universidad y el gran tamaño de los cursos, en especial los de cátedra de la facultad de medicina, se evidencia la necesidad de agilizar el proceso de toma de asistencia de los estudiantes y docentes. En este trabajo se especifican los requerimientos de un sistema de reconocimiento facial para la toma de asistencia automatizada en aulas de clase basado en redes neuronales convolucionales y se muestran resultados del desempeño del sistema en un aula de clase de la Universidad Autónoma de Bucaramanga.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2020-06-26T17:56:24Z
dc.date.available.none.fl_str_mv 2020-06-26T17:56:24Z
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.local.spa.fl_str_mv Trabajo de Grado
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dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
url http://hdl.handle.net/20.500.12749/1315
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reponame:Repositorio Institucional UNAB
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Padilla Porras, Andrés Felipe (2018). Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB
Talaviya, G., Ramteke, R., & Shete, A. (2013). Wireless Fingerprint Based College Attendance System Using Zigbee Technology. International Journal Of Engineering And Advanced Technology, (3), 201-203. Retrieved from https://pdfs.semanticscholar.org/a873/5eb75d3f1411798525fdc65875a8237b0c99.pdf
Nawaz, T., Pervaiz, S., Korrani, A., & Ud-Din, A. (2009). Development of Academic Attendence Monitoring System Using Fingerprint Identification. International Journal Of Computer Science And Network Security, (9), 164-168. Retrieved from https://www.researchgate.net/profile/Tabassam_Nawaz/publication/242098052_Development_of_Academic_Attendence_Monitoring_System_Using_Fingerprint_Identification/links/5576abb008ae7521586c3c2b.pdf
Masalha, F., & Hirzallah, N. (2014). A Students Attendance System Using QR Code. International Journal Of Advanced Computer Science And Applications, (3), 75-79. Retrieved from https://thesai.org/Downloads/Volume5No3/Paper_10-A_Students_Attendance_System_Using_QR_Code.pdf
Sajid, M., Hussain, R., & Usman, M. (2014). A conceptual Model For Automates Attendace Marking System Using Facial Recognition. Ninth International Conference on Digital Information Mangement (Págs. 7-10). Phitsanulok: IEEE.
Methi, D., Chauhan, A., & Gupta, D. (2017). Attendance System Using Face Recognition. International Journal Of Advanced Research In Science, Engineering And Technology, (4), 3897-3902. Retrieved from https://www.ijarset.com/upload/2017/may/11-IJARSET-DIVYAGUPTA.pdf
Kawaguchi, Yohei & Shoji, Tetsuo. (2005). Face Recognition-based Lecture Attendance System, Retrieved from https://www.researchgate.net/publication/241608617_Face_Recognition-based_Lecture_Attendance_System
Balcoh, N., Yousaf, H., Ahmad, W., & Baig, I. (2012). Algorithm for Efficient Attendance Management: Face Recognition based approach. International Journal Of Computer Science Issues, 9(4), 146-150.
Qrcode.com. (2017). History of QR Code | QRcode.com | DENSO WAVE. [online] Available at: http://www.qrcode.com/en/history/ [Accessed 21 Sep. 2017].
Shiffman, D. (2013). The Nature of Code. 1st ed. Shannon Fry, p.445.
Rouse, M. (2015). Framework. whatis. Retrieved 23 September 2017, from http://whatis.techtarget.com/definition/framework
What is Zigbee?. (2014). Zigbee Alliance. Retrieved 23 September 2017, from http://www.zigbee.org/what-is-zigbee/
Viola, P., & Jones, M. (2004). Robust Real-Time Face Detection. International Journal Of Computer Vision, 57(2), 137-154.
Cireșan, D., Meier, U., Masci, J., Gambardella, L. and Schmidhuber, J. (2011). Flexible, High Performance Convolutional Neural Networks for Image Classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, [online] 2, pp.1237-1242. Available at: http://people.idsia.ch/~juergen/ijcai2011.pdf [Accessed 20 Sep. 2017].
Gimeno Hernández, R. (2010). Estudio de técnicas de Reconocimiento facial.
Raspberry Pi. (2017). What is a Raspberry Pi?. [online] Available at: https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/ [Accessed 2 Oct. 2017].
Abdallah, A. S., Abbott, A. L., & El-Nasr, M. A. (2007, May). A new face detection technique using 2D DCT and self organizing feature map. In Proc. of World Academy of Science, Engineering and Technology (Vol. 21, pp. 15-19)
The Database of Faces. (2002). The Database of Faces. [online] Available at: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html [Accessed 5 Sep. 2017]
Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence, 24(4), 509-522
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720.
Adini, Y., Moses, Y., & Ullman, S. (1997). Face recognition: The problem of compensating for changes in illumination direction. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 721-732.
Phillips, P. J. (1999). Support vector machines applied to face recognition. In Advances in Neural Information Processing Systems(pp. 803-809).
Liu, C., & Wechsler, H. (2000). Evolutionary pursuit and its application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(6), 570-582.
Gordon, G. G. (1991, September). Face recognition based on depth maps and surface curvature. In Geometric Methods in Computer Vision (Vol. 1570, pp. 234-248). International Society for Optics and Photonics.
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830.
Shyam, R., & Singh, Y. N. (2014, December). Evaluation of eigenfaces and fisherfaces using bray curtis dissimilarity metric. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1-6). IEEE.
Redmon, J., & Angelova, A. (2015, May). Real-time grasp detection using convolutional neural networks. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 1316-1322). IEEE.
Hongxun, Y., Wen, G., Mingbao, L., & Lizhuang, Z. (2000). Eigen features technique and its application. In Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on (Vol. 2, pp. 1153-1158). IEEE.
Bedre, J. S., & Sapkal, S. (2012). Comparative Study of Face Recognition Techniques: A Review. Emerging Trends in Computer Science and Information Technology–2012 (ETCSIT2012) Proceedings published in International Journal of Computer Applications®(IJCA), 12.
Heiselet, B., Serre, T., Pontil, M., & Poggio, T. (2001). Component-based face detection. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on (Vol. 1, pp. I-I). IEEE.
Virdee-Chapman, B. (2017). Face Recognition: Kairos vs Microsoft vs Google vs Amazon vs OpenCV. Kairos. Retrieved 30 October 2017, from https://www.kairos.com/blog/face-recognition-kairos-vs-microsoft-vs-google-vs-amazon-vs-opencv
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier.
Classification Performance Metrics - NLP-FOR-HACKERS. (2018). Retrieved from https://nlpforhackers.io/classification-performance-metrics/
Atribución-NoComercial-SinDerivadas 2.5 Colombia
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spelling Lobo Quintero, René Alejandro1d887956-8aa0-4a34-9cd0-366291c1a31e-1Jurado García, Miguel Eugeniob47bef77-93c2-4edd-bf46-ce46d4bd9061-1Padilla Porras, Andrés Felipe5af9bdef-3ac7-476c-b6e4-9c308b147006-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001007017https://scholar.google.es/citations?hl=es#user=9vJhVRoAAAAJhttps://orcid.org/0000-0003-2989-5357Grupo de Investigación Preservación e Intercambio Digital de Información y Conocimiento - PrismaGrupo de Investigaciones Clínicas2020-06-26T17:56:24Z2020-06-26T17:56:24Z2018http://hdl.handle.net/20.500.12749/1315instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABDebido al aumento de estudiantes en la universidad y el gran tamaño de los cursos, en especial los de cátedra de la facultad de medicina, se evidencia la necesidad de agilizar el proceso de toma de asistencia de los estudiantes y docentes. En este trabajo se especifican los requerimientos de un sistema de reconocimiento facial para la toma de asistencia automatizada en aulas de clase basado en redes neuronales convolucionales y se muestran resultados del desempeño del sistema en un aula de clase de la Universidad Autónoma de Bucaramanga.1. INTRODUCCIÓN 4 2. PLANTEAMIENTO DEL PROBLEMA 5 3. PLANTEAMIENTO DE LA SOLUCIÓN 6 4. OBJETIVOS 8 4.1. OBJETIVO GENERAL 8 4.2. OBJETIVOS ESPECIFICOS 8 5. RESULTADOS ESPERADOS 8 5.1. Objetivo específico 1 8 5.2. Objetivo específico 2 8 5.3. Objetivo específico 3 8 5.4. Objetivo específico 4 9 5.5. Objetivo específico 5 9 6. ESTADO DEL ARTE 10 7. MARCO TEORICO 22 7.1. Framework 22 7.2. Red neuronal 22 7.3. CNN 22 7.4. Darknet 23 7.5. Código QR 23 7.6. Zigbee 24 7.7. Minucia 24 7.8. Haar Features 24 7.9. Viola Jones 26 7.10. PCA (Principal Component Analysis) 26 7.11. LDA (Linear Discriminant Analysis) 27 7.12. DCT (Discrete Cosine Transform) por bloques 27 7.13. Raspberry 28 8. METODOLOGÍA 29 9. RESULTADOS OBTENIDOS 31 9.1. Objetivo específico 1 31 9.2. Objetivo específico 2 36 9.3. Objetivo específico 3 39 9.4. Objetivo específico 4 44 9.5. Objetivo específico 5 45 10. Conclusiones 53 11. REFERENCIAS 56 12. Anexos 59PregradoDue to the increase in students at the university and the large size of the courses, especially those of the faculty of medicine, the need to speed up the process of taking attendance of students and teachers is evident. In this work, the requirements of a facial recognition system for automated attendance taking in classrooms based on convolutional neural networks are specified and results of the performance of the system in a classroom of the Universidad Autónoma de Bucaramanga are shown.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 2.5 ColombiaSistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de claseFacial recognition system with neural networks for taking assistance in classroomsIngeniero de SistemasBucaramanga (Colombia)UNAB Campus BucaramangaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería de Sistemasinfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/redcol/resource_type/TPPerception of facesFacial recognitionNeural NetworksComputersArtificial intelligenceSystems EngineeringInvestigationsAnalysisArtificial visionAutomationNeural networksArtificial intelligencePercepción de carasReconocimiento facialRedes neuronalesComputadoresInteligencia artificialIngeniería de sistemasInvestigacionesAnálisisInteligencia artificialRedes neuronalesAutomatizaciónVisión artificialPadilla Porras, Andrés Felipe (2018). Sistema de reconocimiento facial con redes neuronales para la toma de asistencia en aulas de clase. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNABTalaviya, G., Ramteke, R., & Shete, A. (2013). Wireless Fingerprint Based College Attendance System Using Zigbee Technology. International Journal Of Engineering And Advanced Technology, (3), 201-203. Retrieved from https://pdfs.semanticscholar.org/a873/5eb75d3f1411798525fdc65875a8237b0c99.pdfNawaz, T., Pervaiz, S., Korrani, A., & Ud-Din, A. (2009). Development of Academic Attendence Monitoring System Using Fingerprint Identification. International Journal Of Computer Science And Network Security, (9), 164-168. Retrieved from https://www.researchgate.net/profile/Tabassam_Nawaz/publication/242098052_Development_of_Academic_Attendence_Monitoring_System_Using_Fingerprint_Identification/links/5576abb008ae7521586c3c2b.pdfMasalha, F., & Hirzallah, N. (2014). A Students Attendance System Using QR Code. International Journal Of Advanced Computer Science And Applications, (3), 75-79. Retrieved from https://thesai.org/Downloads/Volume5No3/Paper_10-A_Students_Attendance_System_Using_QR_Code.pdfSajid, M., Hussain, R., & Usman, M. (2014). A conceptual Model For Automates Attendace Marking System Using Facial Recognition. Ninth International Conference on Digital Information Mangement (Págs. 7-10). Phitsanulok: IEEE.Methi, D., Chauhan, A., & Gupta, D. (2017). Attendance System Using Face Recognition. International Journal Of Advanced Research In Science, Engineering And Technology, (4), 3897-3902. Retrieved from https://www.ijarset.com/upload/2017/may/11-IJARSET-DIVYAGUPTA.pdfKawaguchi, Yohei & Shoji, Tetsuo. (2005). Face Recognition-based Lecture Attendance System, Retrieved from https://www.researchgate.net/publication/241608617_Face_Recognition-based_Lecture_Attendance_SystemBalcoh, N., Yousaf, H., Ahmad, W., & Baig, I. (2012). Algorithm for Efficient Attendance Management: Face Recognition based approach. International Journal Of Computer Science Issues, 9(4), 146-150.Qrcode.com. (2017). History of QR Code | QRcode.com | DENSO WAVE. [online] Available at: http://www.qrcode.com/en/history/ [Accessed 21 Sep. 2017].Shiffman, D. (2013). The Nature of Code. 1st ed. Shannon Fry, p.445.Rouse, M. (2015). Framework. whatis. Retrieved 23 September 2017, from http://whatis.techtarget.com/definition/frameworkWhat is Zigbee?. (2014). Zigbee Alliance. Retrieved 23 September 2017, from http://www.zigbee.org/what-is-zigbee/Viola, P., & Jones, M. (2004). Robust Real-Time Face Detection. International Journal Of Computer Vision, 57(2), 137-154.Cireșan, D., Meier, U., Masci, J., Gambardella, L. and Schmidhuber, J. (2011). Flexible, High Performance Convolutional Neural Networks for Image Classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, [online] 2, pp.1237-1242. Available at: http://people.idsia.ch/~juergen/ijcai2011.pdf [Accessed 20 Sep. 2017].Gimeno Hernández, R. (2010). Estudio de técnicas de Reconocimiento facial.Raspberry Pi. (2017). What is a Raspberry Pi?. [online] Available at: https://www.raspberrypi.org/help/what-%20is-a-raspberry-pi/ [Accessed 2 Oct. 2017].Abdallah, A. S., Abbott, A. L., & El-Nasr, M. A. (2007, May). A new face detection technique using 2D DCT and self organizing feature map. In Proc. of World Academy of Science, Engineering and Technology (Vol. 21, pp. 15-19)The Database of Faces. (2002). The Database of Faces. [online] Available at: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html [Accessed 5 Sep. 2017]Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE transactions on pattern analysis and machine intelligence, 24(4), 509-522Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 711-720.Adini, Y., Moses, Y., & Ullman, S. (1997). Face recognition: The problem of compensating for changes in illumination direction. IEEE Transactions on pattern analysis and machine intelligence, 19(7), 721-732.Phillips, P. J. (1999). Support vector machines applied to face recognition. In Advances in Neural Information Processing Systems(pp. 803-809).Liu, C., & Wechsler, H. (2000). Evolutionary pursuit and its application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(6), 570-582.Gordon, G. G. (1991, September). Face recognition based on depth maps and surface curvature. In Geometric Methods in Computer Vision (Vol. 1570, pp. 234-248). International Society for Optics and Photonics.Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830.Shyam, R., & Singh, Y. N. (2014, December). Evaluation of eigenfaces and fisherfaces using bray curtis dissimilarity metric. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1-6). IEEE.Redmon, J., & Angelova, A. (2015, May). Real-time grasp detection using convolutional neural networks. In Robotics and Automation (ICRA), 2015 IEEE International Conference on (pp. 1316-1322). IEEE.Hongxun, Y., Wen, G., Mingbao, L., & Lizhuang, Z. (2000). Eigen features technique and its application. 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Retrieved from https://nlpforhackers.io/classification-performance-metrics/Atribución-NoComercial-SinDerivadas 2.5 ColombiaORIGINAL2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdf2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdfTesisapplication/pdf3547275https://repository.unab.edu.co/bitstream/20.500.12749/1315/1/2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdf81b0deb3cbe59cb9d6126438db72b35cMD51open accessLicencia_Andres_merged.pdfLicencia_Andres_merged.pdfLicenciaapplication/pdf595735https://repository.unab.edu.co/bitstream/20.500.12749/1315/3/Licencia_Andres_merged.pdf3f07131ee7a7b7aab44a2eda3368c7fcMD53metadata only accessTHUMBNAIL2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdf.jpg2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdf.jpgIM Thumbnailimage/jpeg4630https://repository.unab.edu.co/bitstream/20.500.12749/1315/2/2018_Tesis_Jurado_Garcia_Miguel_Eugenio.pdf.jpgde15ab4495ba5e4a1f315fb387807933MD52open accessLicencia_Andres_merged.pdf.jpgLicencia_Andres_merged.pdf.jpgIM Thumbnailimage/jpeg10939https://repository.unab.edu.co/bitstream/20.500.12749/1315/4/Licencia_Andres_merged.pdf.jpg2a8fad3b77af4247a5f5a33cb3667094MD54metadata only access20.500.12749/1315oai:repository.unab.edu.co:20.500.12749/13152024-10-10 22:02:01.307open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.co

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