Plataforma en la nube para la clasificación de objetos utilizando inteligencia artificial
DOI:
https://doi.org/10.52428/20758944.v18i53.253Keywords:
Artificial Intelligence, Cloud platform, Computer`s ScienceAbstract
Artificial Intelligence (AI) is one of the areas that attracts the most interest in the field of Computer Science, both from the scientific-academic point of view and from its applications in multiple sectors of activity. Throughout the history of Artificial Intelligence, developments and applications have emerged that have made it a consolidated area of knowledge and of proven economic and social interest. However, in recent years, AI has undergone exceptional development, motivated by the appearance of technologies that have represented a great advance in the discipline and by the availability of hardware resources that have made its application viable in different domains.
Society's interest in Artificial Intelligence has grown proportionally to this technological development. Proof of this is that governments and administrations at all levels of the State in different countries around the world have promoted a multitude of programs to finance research, development and innovation activities in AI. In addition, many companies have decided to invest in AI to apply this technology at different points in their value chain.
Although there is no official and unique definition of Artificial Intelligence, all of them agree that it is a field of Computer Science that seeks to develop computer systems that exhibit intellectual characteristics similar to humans, such as the ability to reason, learn, generalize, solve problems, perceive and use natural language.
This project is focused on proposing a cloud platform for classifying objects using Artificial Intelligence, its main application will be to increase knowledge regarding this field and contribute to different solutions that may arise during its use.
Downloads
References
Arriola, I. (2018). Detección de objetos basada en Deep Learning y aplicada a vehículos autónomos. https://addi.ehu.es/handle/10810/28983?show=full
CEPAL. (2019, September 3). Cumbre de Inteligencia Artificial en América Latina. Comisión Económica Para América Latina y El Caribe. https://www.cepal.org/es/notas/cumbre-inteligencia-artificial-america-latina
Clavo, D. (2017, July 13). Clasificación de redes neuronales artificiales. https://www.diegocalvo.es/clasificacion-de-redes-neuronales-artificiales/
Enciclopedia de visión artificial de aprendizaje profundo. (2021, October 10). VGG, GoogLeNet. https://wikidocs-net.translate.goog/137251?_x_tr_sl=auto&_x_tr_tl=es&_x_tr_hl=es&_x_tr_pto=wapp
García Villanueva, M., & Romero Muñoz, L. (2020). Diseño de una arquitectura de Red Neuronal Convolucional para la clasificación de objetos. Ciencia Nicolaita.
Iglesias, E., Garcia, A., Puig, P., & Benzaqué, I. (2020). Inteligencia artificial: Gran oportunidad del siglo XXI: Documento de reflexión y propuesta de actuación. https://publications.iadb.org/publications/spanish/document/Inteligencia-artificial-Gran-oportunidad-del-siglo-XXI-Documento-de-reflexion-y-propuesta-de-actuacion.pdf
Kriesel, D. (2007). A brief Introduction on Neural Networks (Citeseer, Ed.). https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.135.6774&rep=rep1&type=pdf
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision, 128(2), 261–318. https://doi.org/10.1007/S11263-019-01247-4/FIGURES/21 DOI: https://doi.org/10.1007/s11263-019-01247-4
Manzanares González, A. (2018). Detector de baches con Deep Learning. Universidad Pompeu Fabra.
Nielsen, M. (2019, December). Neural networks and deep learning. http://neuralnetworksanddeeplearning.com/about.html
Oracle. (2022). Inteligencia Artificial, Machine Learning, Deep Learning: una historia de muñecas rusas. https://www.oracle.com/es/database/cloud/algoritmos-machine-learning.html
Rawat, W., & Wang, Z. (2017). Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Computation, 29(9), 2352–2449. https://doi.org/10.1162/NECO_A_00990 DOI: https://doi.org/10.1162/neco_a_00990
TensorFlow. (2022, October 18). TensorFlow Datasets. 2022. https://www.tensorflow.org/datasets/catalog/cats_vs_dogs?hl=en
UNIVERSIDADE DA CORUÑA, UNIVERSIDADE DE SANTIAGO DE COMPOSTELA, & UNIVERSIDADE DE VIGO. (2021). MÁSTER UNIVERSITARIO EN INTELIGENCIA ARTIFICIAL / MASTER IN ARTIFICIAL INTELLIGENCE. https://www.uvigo.gal/sites/uvigo.gal/files/contents/paragraph-file/2021-09/MemoriaVerificacionMIA_SUG.pdf
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Mauricio Peredo, Edgar Ramos
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
