Administration 4.0: The Challenge of Institutional Competitiveness as a Requisite for Development

  • Pedro T. Nevado-Batalla MorenoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)


Public Administration must live up to the standards of the new environment 4.0. This economic-industrial paradigm is concerned with the whole society. The need for modernization in Public Administration brings to light the directly proportional relationship between institutional and economic competitiveness.


Public administration Competitiveness citizens Development eGovernment policy Technology 


  1. 1.
    Gazafroudi, A.S., Pinto, T., Prieto-Castrillo, F., Prieto, J., Corchado, J.M., Jozi, A., Vale, Z., Venayagamoorthy, G.K.: Organization-based multi-agent structure of the smart home electricity system. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1327–1334. IEEE (2017)Google Scholar
  2. 2.
    Gazafroudi, A.S., Prieto-Castrillo, F., Pinto, T., Corchado, J.M.: Organization-based multi-agent system of local electricity market: bottom-up approach. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 281–283. Springer (2017)Google Scholar
  3. 3.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010). Scholar
  4. 4.
    Nihan, C.E.: Healthier? More efficient? Fairer? An overview of the main ethical issues raised by the use of ubicomp in the workplace. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 2(1), 29 (2013). ISSN 2255-2863Google Scholar
  5. 5.
    Chamoso, P., Rivas, A., Martín-Limorti, J.J., Rodríguez, S.: A hash based image matching algorithm for social networks. Advances in Intelligent Systems and Computing, vol. 619, pp. 183–190 (2018). Scholar
  6. 6.
    Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Omatu, S., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014). Scholar
  7. 7.
    Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Advances in Case-Based Reasoning, Proceedings, vol. 3155, pp. 533–546 (2004)Google Scholar
  8. 8.
    Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32(4), 307–313 (2002). Scholar
  9. 9.
    Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artif. Intell. Eng. 13(4), 351–357 (1999). Scholar
  10. 10.
    Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10. Scholar
  11. 11.
    Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003).
  12. 12.
    Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004).
  13. 13.
    Corchado, J., Fyfe, C., Lees, B.: Unsupervised learning for financial forecasting. In: Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No. 98TH8367), pp. 259–263 (1998).
  14. 14.
    Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012). Scholar
  15. 15.
    Martínez-Martín, E., Escrig, M.T., Pobil, A.P.D.: A qualitative acceleration model based on intervals. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 2(2), 17 (2013). ISSN 2255-2863Google Scholar
  16. 16.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl. Based Syst. 16(5–6), 321–328 (2003). Scholar
  17. 17.
    Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004). Scholar
  18. 18.
    Fernández-Riverola, F., Díaz, F., Corchado, J.M.: Reducing the memory size of a fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007). Scholar
  19. 19.
    Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002). Scholar
  20. 20.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001). Scholar
  21. 21.
    Coria, J.A.G., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4 PART 1), 1189–1205 (2014). Scholar
  22. 22.
    García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: middleware infrastructure to simulate intelligent agents. Advances in Intelligent and Soft Computing, vol. 91 (2011). Scholar
  23. 23.
    García, O., Chamoso, P., Prieto, J., Rodríguez, S., De La Prieta, F.: A serious game to reduce consumption in smart buildings. Communications in Computer and Information Science, vol. 722, pp. 481–493 (2017). Scholar
  24. 24.
    Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3) (2002)Google Scholar
  25. 25.
    Glez-Peña, D., Díaz, F., Hernández, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009). Scholar
  26. 26.
    Palanca, J., Del Val, E., García-Fornes, A., Billhardt, H., Corchado, J.M., Julian, V.: Designing a goal-oriented smart-home environment. Inf. Syst. Front. 20(1), 125–142 (2017)CrossRefGoogle Scholar
  27. 27.
    Rodríguez-Fernandez, J., Pinto, T., Silva, F., Praca, I., Vale, Z., Corchado, J.M.: Bilateral contract prices estimation using a Q-learning based approach. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6 (2017)Google Scholar
  28. 28.
    Macek, K., Rojicek, J., Kontes, G., Rovas, D.V.: Black-box optimization for buildings and its enhancement by advanced communication infrastructure. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 2(2), 53 (2013). ISSN 2255-2863Google Scholar
  29. 29.
    Laza, R., Pavn, R., Corchado, J.M.: A reasoning model for CBR_BDI agents using an adaptable fuzzy inference system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3040, pp. 96–106. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Li, T., De la Prieta Pintado, F., Corchado, J.M., Bajo, J.: Multi-source homogeneous data clustering for multi-target detection from cluttered background with misdetection. Appl. Soft Comput. J. 60, 436–446 (2017)CrossRefGoogle Scholar
  31. 31.
    Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016). Scholar
  32. 32.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014).
  33. 33.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014).
  34. 34.
    Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electron. Eng. 18(12), 1913–1939 (2017)CrossRefGoogle Scholar
  35. 35.
    Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015). Scholar
  36. 36.
    Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009). Scholar
  37. 37.
    Méndez, J.R., Fdez-Riverola, F., Díaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4065, pp. 106–120 (2006). Scholar
  38. 38.
    Méndez, J.R., Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Corchado, J.M.: Tracking concept drift at feature selection stage in SpamHunting: An anti-spam instance-based reasoning system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 4106, pp. 504–518 (2006). Scholar
  39. 39.
    Teixido, M., Palleja, T., Tresanchez, M., Font, D., Moreno, J., Fernández, A., Palacín, J., Rebate, C.: Optimization of the virtual mouse HeadMouse to foster its classroom use by children with physical disabilities. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 2(4), 1–8 (2013)Google Scholar
  40. 40.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  41. 41.
    García, Ó., Prieto, J., Alonso, R.S., Corchado, J.M.: A framework to improve energy efficient behaviour at home through activity and context monitoring. Sensors 17(8), 1749 (2017)CrossRefGoogle Scholar
  42. 42.
    Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. (2015). Scholar
  43. 43.
    Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 6077 (2010). Scholar
  44. 44.
    Rodríguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: INES 2010 - 14th International Conference on Intelligent Engineering Systems, Proceedings (2010).
  45. 45.
    Romero, S., Fardoun, H.M., Penichet, V.M.R., Gallud, J.A.: Tweacher: new proposal for online social networks impact in secondary education. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 2(1), 9 (2013). ISSN 2255-2863Google Scholar
  46. 46.
    Gazafroudi, A.S., Pinto, T., Castrillo, F.P., Rodríguez, J.M.C., Abrishambaf, O., Jozi, A., Vale, Z.: Energy flexibility assessment of a multi agent-based smart home energy system. In: 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB), Salamanca (2017)Google Scholar
  47. 47.
    Shokri Gazafroudi, A., Prieto Castrillo, F., Pinto, T., Prieto Tejedor, J., Corchado Rodríguez, J.M., Bajo Pérez, J.: Energy flexibility management based on predictive dispatch model of domestic energy management system. Energies 10(9), 1397 (2017)CrossRefGoogle Scholar
  48. 48.
    Sittón, I., Rodríguez, S.: Pattern extraction for the design of predictive models in Industry 4.0. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 258–261 (2017)Google Scholar
  49. 49.
    Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. International J. Ambient Comput. Intell. 1(1), 15–26 (2009). Scholar
  50. 50.
    Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems. Inf. Sci. 222, 47–65 (2013). Scholar
  51. 51.
    Oliveira, T., Neves, J., Novais, P.: Guideline formalization and knowledge representation for clinical decision support. Adv. Distrib. Comput. Artif. Intell. (ADCAIJ) 1(2), 1–11 (2012). ISSN 2255-2863Google Scholar
  52. 52.
    Li, T., Corchado, J.M., Prieto, J.: Convergence of distributed flooding and its application for distributed Bayesian filtering. IEEE Trans. Signal Inf. Process. Over Netw. 3(3), 580–591 (2017)MathSciNetCrossRefGoogle Scholar
  53. 53.
    Li, T., Sun, S.: Online adapting the magnitude of target birth intensity in the PHD Filter. Adv. Distrib. Comput. Artif. Intell. J. 2(4), 31 (2013). ISSN 2255-2863Google Scholar
  54. 54.
    Wang, X., Li, T., Sun, S., Corchado, J.M.: A survey of recent advances in particle filters and remaining challenges for multitarget tracking. Sensors (Switzerland), 17(12), Article no. 2707 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of SalamancaSalamancaSpain

Personalised recommendations