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An Evaluative Model to Assess the Organizational Efficiency in Training Corporations

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Future Data and Security Engineering (FDSE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10018))

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Abstract

In an organisation any optimization process of its issues faces increasing challenges and requires new approaches to the organizational phenomenon. Indeed, in this work it is addressed the problematic of efficiency dynamics through intangible variables that may support a different view of the corporations. It focuses on the challenges that information management and the incorporation of context brings to competitiveness. Thus, in this work it is presented the analysis and development of an intelligent decision support system in terms of a formal agenda built on a Logic Programming based methodology to problem solving, complemented with an attitude to computing grounded on Artificial Neural Networks. The proposed model is in itself fairly precise, with an overall accuracy, sensitivity and specificity with values higher than 90 %. The proposed solution is indeed unique, catering for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in a quantitative or qualitative arrangement.

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References

  1. Vanagas, P., Mantas, V.: Development of total quality management in kaunas university of technology. Eng. Econ. 59, 67–75 (2008)

    Google Scholar 

  2. Nabitz, U., Klazinga, N., Walburg, J.: The EFQM excellence model: European and Dutch experiences with the EFQM approach in health care. Int. J. Qual. Health Care 12, 191–201 (2000)

    Article  Google Scholar 

  3. Valk, P.: Quality assurance in postgraduate pathology training the Dutch way: regular assessment, monitoring of training programs but no end of training examination. Virchows Arch. 468, 109–113 (2016)

    Article  Google Scholar 

  4. Jianwei, Z., Yuxin, L.: Organizational climate and its effects on organizational variables: an empirical study. Int. J. Psychol. Stud. 2, 190–201 (2010)

    Google Scholar 

  5. Dietz, D., Zwich, T.: The retention effect of training: portability, visibility and credibility. Discussion Paper No 16-011. http://ftp.zew.de/pub/zew-docs/dp/dp16011.pdf

  6. Safanova, K., Podolskii, S.: Improvement of the evaluation of quality of the integrative intellectual resource of the higher educational establishment. Asian Soc. Sci. 11, 112–124 (2015)

    Google Scholar 

  7. Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)

    Google Scholar 

  8. Cortez, P., Rocha, M., Neves, J.: Evolving time series forecasting ARMA models. J. Heuristics 10, 415–429 (2004)

    Article  Google Scholar 

  9. Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)

    Google Scholar 

  10. Pereira, L., Anh, H.: Evolution prospection. In: Nakamatsu, K. (ed.) New Advances in Intelligent Decision Technologies – Results of the First KES International Symposium IDT 2009. Studies in Computational Intelligence, vol. 199, pp. 51–64. Springer, Berlin (2009)

    Google Scholar 

  11. Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J. (eds.) Progress in Artificial Intelligence. LNAI, vol. 4874, pp. 160–169. Springer, Berlin (2007)

    Chapter  Google Scholar 

  12. Lucas, P.: Quality checking of medical guidelines through logical abduction. In: Coenen, F., Preece, A., Mackintosh, A. (eds.) Proceedings of AI-2003 (Research and Developments in Intelligent Systems XX), pp. 309–321. Springer, London (2003)

    Google Scholar 

  13. Machado, J., Abelha, A., Novais, P., Neves, J., Neves, J.: Quality of service in healthcare units. In: Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis – ETI Publication, Ghent (2008)

    Google Scholar 

  14. Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves J.: Artificial neural networks in diabetes control. In: Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370. IEEE Edition (2015)

    Google Scholar 

  15. O’Neil, P., O’Neil, B., Chen, X.: Star Schema Benchmark. Revision 3, 5 June 2009. http://www.cs.umb.edu/~poneil/StarSchemaB.pdf

  16. Vicente, H., Couto, C., Machado, J., Abelha, A., Neves, J.: Prediction of water quality parameters in a reservoir using artificial neural networks. Int. J. Des. Nat. Ecodyn. 7, 309–318 (2012)

    Article  Google Scholar 

  17. Vicente, H., Dias, S., Fernandes, A., Abelha, A., Machado, J., Neves, J.: Prediction of the quality of public water supply using artificial neural networks. J. Water Supply: Res. Technol. – AQUA 61, 446–459 (2012)

    Article  Google Scholar 

  18. Haykin, S.: Neural Networks and Learning Machines. Pearson Education, New Jersey (2009)

    Google Scholar 

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Acknowledgments

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

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Correspondence to José Neves .

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Fernandes, A., Vicente, H., Figueiredo, M., Neves, M., Neves, J. (2016). An Evaluative Model to Assess the Organizational Efficiency in Training Corporations. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2016. Lecture Notes in Computer Science(), vol 10018. Springer, Cham. https://doi.org/10.1007/978-3-319-48057-2_29

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  • DOI: https://doi.org/10.1007/978-3-319-48057-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48056-5

  • Online ISBN: 978-3-319-48057-2

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