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dc.contributor.author | Ruiz, Maria | es_ES |
dc.contributor.author | Rodriguez, Juan José | es_ES |
dc.contributor.author | Erlaiz, Gorka | es_ES |
dc.contributor.author | Olibares, Iratxe | es_ES |
dc.date.accessioned | 2021-07-29T09:56:17Z | |
dc.date.available | 2021-07-29T09:56:17Z | |
dc.date.issued | 2021-07-28 | |
dc.identifier.uri | http://hdl.handle.net/10251/170826 | |
dc.description.abstract | [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 and Environment Department). The project started in April 2019 and it will end in December 2021. Its main objective was to arrange an industrial research about cognitive computing. The main aim was the application of these systems for the development of an Intelligent Experience Center (IExC) to facilitate: i) enrichment of processes, products and services, in general client experiences, ii) automatic generation of technical predictions related to the product and the client behaviour through the exploitation of acquired knowledge, and iii) rationalization and automation of the processes that are involved in the after sale services both at technical and management level. The technological outcome presented in this paper is built using cognitive engines to enable learning from the client experience, and predictive models to anticipate client necessities. | es_ES |
dc.description.sponsorship | 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. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | International Journal of Production Management and Engineering | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Cognitive computing | es_ES |
dc.subject | Digitalization | es_ES |
dc.subject | Digital transformation | es_ES |
dc.subject | Predictive models | es_ES |
dc.subject | Algorithms | es_ES |
dc.subject | Big data | es_ES |
dc.title | PHYRON: cognitive computing for the creation of an innovative Intelligence Experience Center | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/ijpme.2021.15300 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | 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 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/ijpme.2021.15300 | es_ES |
dc.description.upvformatpinicio | 103 | es_ES |
dc.description.upvformatpfin | 112 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 9 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 2340-4876 | |
dc.relation.pasarela | OJS\15300 | es_ES |
dc.description.references | Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review. https://doi.org/10.3386/w24690 | es_ES |
dc.description.references | Biecek, P. (2018). DALEX: explainers for complex predictive models in R. The Journal of Machine Learning Re-search, 19(1), 3245-3249. | es_ES |
dc.description.references | Bond, A. H., & Gasser, L. (Eds.). (2014). Readings in distributed artificial intelligence. Morgan Kaufmann. | es_ES |
dc.description.references | Bringsjord, S., & Schimanski, B. (2003, August). What is artificial intelligence? Psychometric AI as an answer. In IJCAI (pp. 887-893). | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | High, R. (2012). The era of cognitive systems: An inside look at IBM Watson and how it works. IBM Corporation, Redbooks, 1-16. | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | Forrester (2017): "Predictions 2018: The Honeymoon For AI is Over". | es_ES |
dc.description.references | LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | PwC (2017). UK Economic Outlook. Recuperado de http:// www.pwc.co.uk/economics | es_ES |
dc.description.references | PWc, (2018): "Bots, Machine Learning, Servicios Cognitivos. Realidad y perspectivas de la Inteligencia Artificial en España, 2018". | es_ES |
dc.description.references | Rusk, N. (2016). Deep learning. Nature Methods, 13(1), 35-35. https://doi.org/10.1038/nmeth.3707 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6). | es_ES |
dc.description.references | 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 | es_ES |
dc.description.references | 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 | es_ES |