Zobrazeno 1 - 10
of 306
pro vyhledávání: '"automatización de procesos"'
Publikováno v:
Serie Científica de la Universidad de las Ciencias Informáticas, Vol 17, Iss 8, Pp 99-114 (2024)
El artículo discute la implementación de un sistema de gestión empresarial automatizado con inteligencia artificial (IA) para abordar los desafíos y problemáticas comunes en la gestión empresarial y financiera. El sistema de IA puede integrar t
Externí odkaz:
https://doaj.org/article/d90c37006d2d477abee11eefc65b15f5
Autor:
Oscar Andrés Tobar Rosero, Sebastián Giraldo Ríos, Paulina Arregocés Guerra, Juan Carlos Rodríguez Suárez, Leonardo Vásquez Ruiz, Germán Darío Zapata Madrigal
Publikováno v:
Ciencia e Ingeniería Neogranadina, Vol 34, Iss 2 (2024)
El sector eléctrico requiere herramientas tecnológicas apropiadas para asegurar un monitoreo efectivo y la implementación de acciones eficaces ante contingencias que puedan surgir durante su operación. Existen diversas ofertas tecnológicas en el
Externí odkaz:
https://doaj.org/article/4a8d17816b8946bf93db399c6524c3f8
Publikováno v:
Revista Científica UISRAEL, Vol 9, Iss 3, Pp 47-72 (2022)
Hoy en día las organizaciones y de modo similar, las instituciones de educación superior, se enfrentan a diversos retos; entre los que se encuentra el de tener un modelo de gestión dinámico, flexible y sobre todo innovador. En consecuencia, se co
Externí odkaz:
https://doaj.org/article/60f656993186435a92a14ae70d1b2ffa
Publikováno v:
Revista Ingenio, Vol 18, Iss 1, Pp 33-40 (2021)
El estándar ISO 50001 para la gestión de la energía es bien conocido en el contexto de las Pequeñas y Medianas Empresas, aunque su aplicación no es común. Por otra parte, ha aparecido en esta década un paradigma que es el de Industria 4.0 (I 4
Externí odkaz:
https://doaj.org/article/8447cff9ea634d778d75526912156e4c
En este proyecto, se llevó a cabo un estudio con el objetivo de desarrollar un sistema de reconocimiento óptico de caracteres (OCR) diseñado para identificar y almacenar la información de las boletas de pago de docentes en la UGEL Ferreñafe. Est
Externí odkaz:
http://hdl.handle.net/20.500.12423/7422
Autor:
Alma Rosa Galindo Monfil, Nancy Araceli Olivares Ruiz, Brenda Marina Martínez Herrera, Cecilia Ostos Cruz
Publikováno v:
Interconectando Saberes, Iss 8 (2019)
En este documento se sintetiza el trabajo realizado para la construcción de un sistema de información web, el cual automatiza los procesos en el registro de participantes, almacenamiento de información y obtención de resultados de los ganadores d
Externí odkaz:
https://doaj.org/article/6295dde8c6534fbbb37589e2fdd7dc36
El siguiente plan de mejora se realiza como opción de grado con el fin de obtener el título profesional en Negocios Internacionales; a lo largo del mismo, se presentará el desarrollo de este desde mi cargo como practicante en el departamento de IM
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3896::26bdfd130a36ed806b335eb6d21ecfa3
https://hdl.handle.net/11634/50287
https://hdl.handle.net/11634/50287
Publikováno v:
S. Russel and P. Norvig, ((Artificial Intelligence: A Modern Approach)), Pearson Education Limited, Third Edition, 2016.
Millán Gómez, Sim´on (2006). Procedimientos de Mecanizado. Madrid: Editorial Paraninfo, 19/05/2022.
Oberg, Erik, 1881-; McCauley, Christopher J. (2012). Machinery’s handbook : a reference book for the mechanical engineer, designer, manufacturing engineer, draftsman, toolmaker, and machinist (29th ed edici´on). Industrial Press
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ((ImageNet Classification with Deep Convolutional Neural Networks)), Comunications of the ACM, Vol 60 No 6 p´ags 84-90, 2017
Dan Ciresan, Ueli Meier and Jurguen Schmidhuber, ((Multi-Column Deep Neural Networks for Image Classification ))
A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way, https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neuralnetworks-the-eli5-way-3bd2b1164a53, 19/05/2022
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, ((Rich Feature hierarchies for accurate object the tension and semantic segmentation)) CVPR. 2014.
Albert Soto, ((YOLO object detector for onboard driving images)) Escola d´Enginyeria Universidad Autónoma de Barcelona.
LeCun, Y., Huang, F., Bottou, L., ((Learning methods for generic object recognition with invariance to pose and lighting)) In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004. Volume 2 (2004). IEEE, II–97.
Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
Griffin, G., Holub, A., Perona, P, ((Caltech-256 object category dataset. )) Technical Report 7694, California Institute of Technology, 2007.
YOLO architecture, https://www.researchgate.net/figure/YOLO-architecture-YOLOarchitecture-is-inspired-by-GooLeNet-model-for-image fig2 329038564, 20/05/2022.
Fei-Fei, L., Fergus, R., Perona, P., ((Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories)), Comput. Vision Image Understanding 106, 1 (2007), 59–70.
Krizhevsky, A, ((Learning multiple layers of features from tiny images)). Master’s thesis, Department of Computer Science, University of Toronto, 2009
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., ((Going deeper with convolutions)) 2014
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei ((ImageNet: A large-scale hierarchical image database)). 2009. Conference on computer vision
Hyndman, Rob J.; Koehler, Anne B. (2006). .Another look at measures of forecast accuracy”. International Journal of Forecasting.
Jaccard index, https://www.researchgate.net/publication/239604848 The Probabilistic Basis of Jaccard%23/05/2022.
Tom Fawcett, An introduction to ROC analysis, Institute for the Study of Learning and Expertise, (2005) https://people.inf.elte.hu/kiss/11dwhdm/roc.pdf23/05/2022.
Powers David, Evaluation: From Precision, Recall and FFactor to ROC, Informedness, Markedness & Correlation 2007, https://web.archive.org/web/20191114213255/https://www.flinders.edu.au/science engineering/fms/School-CSEM/publications/tech repsresearch artfcts/TRRA 2007.pdf 19/11/2022.
Information Retrieval, https://en.wikipedia.org/w/index.php?title=Information retrievaldirection=next&19/11/2022
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning.
Cross Entropy, The Mathematics of Information Coding, Extraction and Distribution, by George Cybenko, Dianne P. O’Leary, Jorma Rissanen, 1999.
Shapiro, L. G. & Stockman, G. C: C¸ omputer Vision”, page 137, 150. Prentice Hall, 2001.
Vaswani A., Shazeer N., Parmar N., Uskoreit J., Jones L., Gomez A., Lukasz K., Polosukhin I. ((Attention Is All You Need )) 31st Conference on Neural Information Processing Systems. 2017. Long Beach, CA, USA.
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X, Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (( Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale)) . 2021. Google Research, Brain team.
He K., Zhang X., Ren S., Sun J. (( Deep Residual Learning fro image Recognition )) . 2015. Microsoft Research.
Lui Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. ((A ConvNet for the 2020s )). 2022. Facebook AI Research
Ulrich M., Follmann P., Neudeck J. ((A comparison of shape-based matching with deep-learning-based object detection)) Technisches Messen. 2019. DOI 10.1515/teme-2019-0076.
Ren, Shaoqing He, Kaiming Girshick, Ross Sun, Jian. ((Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI 10.1109/TPAMI.2016.2577031.
Detection Evaluation, https://cocodataset.org/home, 19/11/2022.
Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh. ((MobileNetV2: Inverted Residuals and Linear Bottlenecks)), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
Mudumbi, Terry Bian, Naizheng Zhang, Yiyi Hazoume, Florian. ((An Approach Combined the Faster RCNN and Mobilenet for Logo Detection)), Journal of Physics: Conference Series. 2019. 10.1088/1742-6596/1284/1/012072.
Srivastava, Animesh and Dalvi, Anuj and Britto, Cyrus and Rai, Harshit and Shelke, Kavita ((Explicit Content Detection using Faster R-CNN and SSD MobileNet v2.)) 2020. Int. Res. J. Eng. Technol, 7, 5572–5577.
Tan L, Huangfu T, Wu L, Chen W. ((Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification)) Research Square. 2021. DOI: 10.21203/rs.3.rs-668895/v1.
Ahmed, Khaled R. ((Smart Pothole Detection Using Deep Learning Based on Dilated Convolution)) Sensors. 2021. DOI: 10.3390/s21248406.
Girshick, Ross. ((Fast R-CNN)). Proceedings of the IEEE international conference on computer vision. 2015. p. 1440-1448.
Karim, Shahid Zhang, Ye Yin, Shoulin Bibi, Irfana Brohi, Ali. ((A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems)) 2020. DOI: 10.3233/MGS-200330.
Short-Term Load Forecasting based on ResNet and LSTM-Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-structure-of-ResNet-12 fig1 329954455 [accessed 23 Nov, 2022]
Everingham, M., Eslami, S., Gool, L. V., Williams, C., Winn, J., Zisserman, A. ((The pascal visual object classes challenge: A retrospective )) IJCV. 2015.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ((ImageNet large scale visual recognition challenge)) IJCV. 2015.
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., Zitnick, L. ((Microsoft COCO: Common objects in context)) In ECCV. 2015.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A. ((Places: A 10 million image database for scene recognition)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., PontTuset, J., et al. ((The open images dataset v4: Unified image classi-fication, object detection, and visual relationship detection at scale)) arXiv:1811.00982. 2018.
Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R. ((Benchmarking detection transfer learning with vision transformers)) arXiv preprint arXiv:2111.11429. 2021.
Liu, L., Ouyang, W., Wang, X. et al. ((Deep Learning for Generic Object Detection: A Survey)) Int J Comput Vis. 2020. https://doi.org/10.1007/s11263-019-01247-4
Chuanqi T., Fuchun S., Tao K.,Wenchang Z., Chao Y. Chunfang L.(( A Survey on Deep Transfer Learning)) The 27th International Conference on Artificial Neural Networks. 2018
Ze L., Yutong L., Yue C., Han H., Yixuan W., Zheng Z., Stephen L. Baining G.((Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)) 2021
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Millán Gómez, Sim´on (2006). Procedimientos de Mecanizado. Madrid: Editorial Paraninfo, 19/05/2022.
Oberg, Erik, 1881-; McCauley, Christopher J. (2012). Machinery’s handbook : a reference book for the mechanical engineer, designer, manufacturing engineer, draftsman, toolmaker, and machinist (29th ed edici´on). Industrial Press
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, ((ImageNet Classification with Deep Convolutional Neural Networks)), Comunications of the ACM, Vol 60 No 6 p´ags 84-90, 2017
Dan Ciresan, Ueli Meier and Jurguen Schmidhuber, ((Multi-Column Deep Neural Networks for Image Classification ))
A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way, https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neuralnetworks-the-eli5-way-3bd2b1164a53, 19/05/2022
Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, ((Rich Feature hierarchies for accurate object the tension and semantic segmentation)) CVPR. 2014.
Albert Soto, ((YOLO object detector for onboard driving images)) Escola d´Enginyeria Universidad Autónoma de Barcelona.
LeCun, Y., Huang, F., Bottou, L., ((Learning methods for generic object recognition with invariance to pose and lighting)) In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004. Volume 2 (2004). IEEE, II–97.
Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
Griffin, G., Holub, A., Perona, P, ((Caltech-256 object category dataset. )) Technical Report 7694, California Institute of Technology, 2007.
YOLO architecture, https://www.researchgate.net/figure/YOLO-architecture-YOLOarchitecture-is-inspired-by-GooLeNet-model-for-image fig2 329038564, 20/05/2022.
Fei-Fei, L., Fergus, R., Perona, P., ((Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories)), Comput. Vision Image Understanding 106, 1 (2007), 59–70.
Krizhevsky, A, ((Learning multiple layers of features from tiny images)). Master’s thesis, Department of Computer Science, University of Toronto, 2009
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., ((Going deeper with convolutions)) 2014
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei ((ImageNet: A large-scale hierarchical image database)). 2009. Conference on computer vision
Hyndman, Rob J.; Koehler, Anne B. (2006). .Another look at measures of forecast accuracy”. International Journal of Forecasting.
Jaccard index, https://www.researchgate.net/publication/239604848 The Probabilistic Basis of Jaccard%23/05/2022.
Tom Fawcett, An introduction to ROC analysis, Institute for the Study of Learning and Expertise, (2005) https://people.inf.elte.hu/kiss/11dwhdm/roc.pdf23/05/2022.
Powers David, Evaluation: From Precision, Recall and FFactor to ROC, Informedness, Markedness & Correlation 2007, https://web.archive.org/web/20191114213255/https://www.flinders.edu.au/science engineering/fms/School-CSEM/publications/tech repsresearch artfcts/TRRA 2007.pdf 19/11/2022.
Information Retrieval, https://en.wikipedia.org/w/index.php?title=Information retrievaldirection=next&19/11/2022
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning.
Cross Entropy, The Mathematics of Information Coding, Extraction and Distribution, by George Cybenko, Dianne P. O’Leary, Jorma Rissanen, 1999.
Shapiro, L. G. & Stockman, G. C: C¸ omputer Vision”, page 137, 150. Prentice Hall, 2001.
Vaswani A., Shazeer N., Parmar N., Uskoreit J., Jones L., Gomez A., Lukasz K., Polosukhin I. ((Attention Is All You Need )) 31st Conference on Neural Information Processing Systems. 2017. Long Beach, CA, USA.
Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X, Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (( Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale)) . 2021. Google Research, Brain team.
He K., Zhang X., Ren S., Sun J. (( Deep Residual Learning fro image Recognition )) . 2015. Microsoft Research.
Lui Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. ((A ConvNet for the 2020s )). 2022. Facebook AI Research
Ulrich M., Follmann P., Neudeck J. ((A comparison of shape-based matching with deep-learning-based object detection)) Technisches Messen. 2019. DOI 10.1515/teme-2019-0076.
Ren, Shaoqing He, Kaiming Girshick, Ross Sun, Jian. ((Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015. DOI 10.1109/TPAMI.2016.2577031.
Detection Evaluation, https://cocodataset.org/home, 19/11/2022.
Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh. ((MobileNetV2: Inverted Residuals and Linear Bottlenecks)), Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.
Mudumbi, Terry Bian, Naizheng Zhang, Yiyi Hazoume, Florian. ((An Approach Combined the Faster RCNN and Mobilenet for Logo Detection)), Journal of Physics: Conference Series. 2019. 10.1088/1742-6596/1284/1/012072.
Srivastava, Animesh and Dalvi, Anuj and Britto, Cyrus and Rai, Harshit and Shelke, Kavita ((Explicit Content Detection using Faster R-CNN and SSD MobileNet v2.)) 2020. Int. Res. J. Eng. Technol, 7, 5572–5577.
Tan L, Huangfu T, Wu L, Chen W. ((Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification)) Research Square. 2021. DOI: 10.21203/rs.3.rs-668895/v1.
Ahmed, Khaled R. ((Smart Pothole Detection Using Deep Learning Based on Dilated Convolution)) Sensors. 2021. DOI: 10.3390/s21248406.
Girshick, Ross. ((Fast R-CNN)). Proceedings of the IEEE international conference on computer vision. 2015. p. 1440-1448.
Karim, Shahid Zhang, Ye Yin, Shoulin Bibi, Irfana Brohi, Ali. ((A brief review and challenges of object detection in optical remote sensing imagery. Multiagent and Grid Systems)) 2020. DOI: 10.3233/MGS-200330.
Short-Term Load Forecasting based on ResNet and LSTM-Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-structure-of-ResNet-12 fig1 329954455 [accessed 23 Nov, 2022]
Everingham, M., Eslami, S., Gool, L. V., Williams, C., Winn, J., Zisserman, A. ((The pascal visual object classes challenge: A retrospective )) IJCV. 2015.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ((ImageNet large scale visual recognition challenge)) IJCV. 2015.
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., Zitnick, L. ((Microsoft COCO: Common objects in context)) In ECCV. 2015.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A. ((Places: A 10 million image database for scene recognition)) IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., PontTuset, J., et al. ((The open images dataset v4: Unified image classi-fication, object detection, and visual relationship detection at scale)) arXiv:1811.00982. 2018.
Li, Y., Xie, S., Chen, X., Dollar, P., He, K., Girshick, R. ((Benchmarking detection transfer learning with vision transformers)) arXiv preprint arXiv:2111.11429. 2021.
Liu, L., Ouyang, W., Wang, X. et al. ((Deep Learning for Generic Object Detection: A Survey)) Int J Comput Vis. 2020. https://doi.org/10.1007/s11263-019-01247-4
Chuanqi T., Fuchun S., Tao K.,Wenchang Z., Chao Y. Chunfang L.(( A Survey on Deep Transfer Learning)) The 27th International Conference on Artificial Neural Networks. 2018
Ze L., Yutong L., Yue C., Han H., Yixuan W., Zheng Z., Stephen L. Baining G.((Swin Transformer: Hierarchical Vision Transformer using Shifted Windows)) 2021
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
La tarea de clasificación de tornillos hasta el momento es solo ejecutada por humanos. De hecho, las fotos no son aceptadas como insumo para la clasificación de tornillos debido a que existe información que no se puede determinar con las imágenes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c34106a5c58b795de12e91760a924e44
https://repository.urosario.edu.co/handle/10336/38281
https://repository.urosario.edu.co/handle/10336/38281
El uso de tecnologías en procesos cotidianos de las empresas en la actualidad está teniendo un gran apogeo e interés por parte del nivel estratégico de éstas. El proceso de almacén, en donde la distribuidora de medicamentos permitió la impleme
Externí odkaz:
http://hdl.handle.net/20.500.12423/7425
Se plantea la creación de un bot de WhatsApp para automatizar la tediosa tarea de cotizar componentes eléctricos y electrónicos con distintos proveedores. El objetivo principal es facilitar la toma de decisiones en este proceso. Se llevará a cabo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2144::3e284e7070195c4610424c571786ad3a
https://hdl.handle.net/11059/14690
https://hdl.handle.net/11059/14690