An Evaluative Model to Assess the Organizational Efficiency in Training Corporations

  • Ana Fernandes
  • Henrique Vicente
  • Margarida Figueiredo
  • Mariana Neves
  • José NevesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10018)


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.


Optimization Efficiency Logic programming Knowledge representation Artificial neural networks 



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.


  1. 1.
    Vanagas, P., Mantas, V.: Development of total quality management in kaunas university of technology. Eng. Econ. 59, 67–75 (2008)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 5.
    Dietz, D., Zwich, T.: The retention effect of training: portability, visibility and credibility. Discussion Paper No 16-011.
  6. 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. 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. 8.
    Cortez, P., Rocha, M., Neves, J.: Evolving time series forecasting ARMA models. J. Heuristics 10, 415–429 (2004)CrossRefGoogle Scholar
  9. 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. 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. 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)CrossRefGoogle Scholar
  12. 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. 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. 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. 15.
    O’Neil, P., O’Neil, B., Chen, X.: Star Schema Benchmark. Revision 3, 5 June 2009.
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Haykin, S.: Neural Networks and Learning Machines. Pearson Education, New Jersey (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ana Fernandes
    • 1
  • Henrique Vicente
    • 2
    • 3
  • Margarida Figueiredo
    • 2
    • 4
  • Mariana Neves
    • 5
  • José Neves
    • 3
    Email author
  1. 1.Organização Multinacional de FormaçãoLisbonPortugal
  2. 2.Departamento de Química, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  3. 3.Centro AlgoritmiUniversidade do MinhoBragaPortugal
  4. 4.Centro de Investigação em Educação e PsicologiaUniversidade de ÉvoraÉvoraPortugal
  5. 5.DeloitteLondonUK

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