- -

PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center

Mostrar el registro completo del ítem

Ruiz, M.; Rodriguez, JJ.; Erlaiz, G.; Olibares, I. (2021). PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center. International Journal of Production Management and Engineering. 9(2):103-112. https://doi.org/10.4995/ijpme.2021.15300

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/170826

Ficheros en el ítem

Thumbnail
Nombre: RuizRodriguezErlaiz ...
Tamaño: 800.0Kb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center
Autor: Ruiz, Maria Rodriguez, Juan José Erlaiz, Gorka Olibares, Iratxe
Fecha difusión:
Resumen:
[EN] This research presents the results of a project called “PHYRON: Cognitive Computing for the creation of an innovative Intelligence Experience Center”, funded by the Basque Government (Economic Development, Sustainability ...[+]
Palabras clave: Cognitive computing , Digitalization , Digital transformation , Predictive models , Algorithms , Big data
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
International Journal of Production Management and Engineering. (eissn: 2340-4876 )
DOI: 10.4995/ijpme.2021.15300
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/ijpme.2021.15300
Agradecimientos:
We would like to thank the Basque Government for their support in the development of this project. Special thanks to the Economic Development, Sustainability and Environment Department.
Tipo: Artículo

References

Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review. https://doi.org/10.3386/w24690

Biecek, P. (2018). DALEX: explainers for complex predictive models in R. The Journal of Machine Learning Re-search, 19(1), 3245-3249.

Bond, A. H., & Gasser, L. (Eds.). (2014). Readings in distributed artificial intelligence. Morgan Kaufmann. [+]
Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review. https://doi.org/10.3386/w24690

Biecek, P. (2018). DALEX: explainers for complex predictive models in R. The Journal of Machine Learning Re-search, 19(1), 3245-3249.

Bond, A. H., & Gasser, L. (Eds.). (2014). Readings in distributed artificial intelligence. Morgan Kaufmann.

Bringsjord, S., & Schimanski, B. (2003, August). What is artificial intelligence? Psychometric AI as an answer. In IJCAI (pp. 887-893).

Engelmore, R. S. (1987). Artificial intelligence and knowledge based systems: origins, methods and opportunities for NDE. In Review of Progress in Quantitative Nondestructive Evaluation (pp. 1-20). Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1893-4_1

Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning.

Hagberg, J., Sundstrom, M., & Egels-Zandén, N. (2016). The digitalization of retailing: an exploratory frame-work. International Journal of Retail & Distribution Management. https://doi.org/10.1108/IJRDM-09-2015-0140

High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks, 1-16.

Hollnagel, E., & Woods, D. D. (1983). Cognitive systems engineering: New wine in new bottles. International journal of man-machine studies, 18(6), 583-600. https://doi.org/10.1016/S0020-7373(83)80034-0

Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64. https://doi.org/10.1016/j.bdr.2015.01.006

Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In 2013 Sixth international conference on contemporary computing (IC3) (pp. 404-409). IEEE. https://doi.org/10.1109/IC3.2013.6612229

Kuhn, M. (2008). Building predictive models in R using the caret package. J Stat Softw, 28(5), 1-26. https://doi.org/10.18637/jss.v028.i05

Forrester (2017): "Predictions 2018: The Honeymoon For AI is Over".

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Mousannif, H., Sabah, H., Douiji, Y., & Sayad, Y. O. (2014, August). From big data to big projects: a step-by-step roadmap. In 2014 International Conference on Future Internet of Things and Cloud (pp. 373-378). IEEE. https://doi.org/10.1109/FiCloud.2014.66

PwC (2017). UK Economic Outlook. Recuperado de http:// www.pwc.co.uk/economics

PWc, (2018): "Bots, Machine Learning, Servicios Cognitivos. Realidad y perspectivas de la Inteligencia Artificial en España, 2018".

Rusk, N. (2016). Deep learning. Nature Methods, 13(1), 35-35. https://doi.org/10.1038/nmeth.3707

Steels, L. (1993). The artificial life roots of artificial intelligence. Artificial life, 1(1_2), 75-110. https://doi.org/10.1162/artl.1993.1.1_2.75

Steyerberg, E. W., Harrell Jr, F. E., Borsboom, G. J., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001). Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of clinical epidemiology, 54(8), 774-781. https://doi.org/10.1016/S0895-4356(01)00341-9

Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6).

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107. https://doi.org/10.1109/TKDE.2013.109

Zhang, X. D. (2020). Machine learning. In A Matrix Algebra Approach to Artificial Intelligence (pp. 223-440). Springer, Singapore. https://doi.org/10.1007/978-981-15-2770-8_6

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem