Advertisement

Artificial and Natural Intelligence Integration

  • Juan C. Alvarado-Pérez
  • Diego H. Peluffo-Ordóńez
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)

Abstract

The large amount of data generated by different activities -academic, scientific, business and industrial activities, among others- contains meaningful information that allows developing processes and techniques, which have scientific validity to optimally explore such information. Doing so, we get new knowledge to properly make decisions. Nowadays a new and innovative field is rapidly growing in importance that is Artificial Intelligence, which involves computer processing devices of modern machines and human reasoning. By synergistically combining them –in other words, performing an integration of natural and artificial intelligence-, it is possible to discover knowledge in a more effective way in order to find hidden trends and patterns belonging to the predictive model database. As well, allowing for new observations and considerations from beforehand known data by using data analysis methods as well as the knowledge and skills (of holistic, flexible and parallel type) from human reasoning. This work briefly reviews main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this field integration approaches. As well, key aspects to conceptually compare them are provided.

Keywords

Data mining visualization machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Diaz, N.V., Serrano-Garcia, I.: Pensabas que emocionarse era sencillo? Las emociones como fenómenos biológicos, cognoscitivos y sociales // Did you think it was easy to get excited about? Emotions such as biological, social and cognitive phenomena. Rev. Puertorriqueña Psicol. 13(1) (ene. 2014)Google Scholar
  2. 2.
    Tufféry, S.: Data Mining and Statistics for Decision Making. John Wiley & Sons (2011)Google Scholar
  3. 3.
    Pethuru, R.: Data Visualization: Creating Mindś Eye. In: Handbook of Research on Cloud Infrastructures for Big Data Analytics. IGI Global (2014)Google Scholar
  4. 4.
    Cook, K., Earnshaw, R., Stasko, J.: Guest Editors’ Introduction: Discovering the Unexpected. IEEE Comput. Graph. Appl. 27(5), 15–19 (2007)CrossRefGoogle Scholar
  5. 5.
    Keim, D.A., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual Analytics: Definition, Process, and Challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Koh, L.C., Slingsby, A., Dykes, J., Kam, T.S.: Developing and Applying a User-Centered Model for the Design and Implementation of Information Visualization Tools. In: 2011 15th International Conference on Information Visualisation (IV), pp. 90–95 (2011)Google Scholar
  7. 7.
    Huang, M.-J., Tsou, Y.-L., Lee, S.-C.: Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl.-Based Syst. 19(6), 396–403 (2006)CrossRefGoogle Scholar
  8. 8.
    Roselli, M.: Maduración cerebral y desarrollo cognoscitivo. Rev. Latinoam. Cienc. Soc. Niñez Juv. 1(1) (May 2011)Google Scholar
  9. 9.
    Kononenko, I., Kukar, M.: Machine Learning and Data Mining. Elsevier (2007)Google Scholar
  10. 10.
    Torres Ponjuán, D.: Aproximaciones a la visualización como disciplina científica. ACIMED 20(6), 161–174 (2009)Google Scholar
  11. 11.
    Alonso, F., Martínez, L., Pérez, A., Valente, J.P.: Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned. Expert Syst. Appl. 39(8), 7524–7535 (2012)CrossRefGoogle Scholar
  12. 12.
    Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, pp. 12–20 (2009)Google Scholar
  13. 13.
    Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Short Review of Dimensionality Reduction Methods Based on Stochastic Neighbour Embedding. In: Villmann, T., Schleif, F.-M., Kaden, M., Lange, M. (eds.) Advances in Self-Organizing Maps and Learning. AISC, vol. 295, pp. 65–74. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Aguilar, D.A.G., Guerrero, C.S., Sanchez, R.T., Penalvo, F.G.: Visual Analytics to Support E-learning (ene. 2010)Google Scholar
  15. 15.
    Puolamäki, K., Bertone, A., Therón, R., Huisman, O., Johansson, J., Miksch, S., Papapetrou, P., Rinzivillo, S.: Mastering The Information Age – Solving Problems with Visual Analytics. In: Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.) Mastering the Information Age Solving Problems with Visual Analytics, Germany (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Juan C. Alvarado-Pérez
    • 1
    • 2
  • Diego H. Peluffo-Ordóńez
    • 2
  1. 1.Universidad de SalamancaSalamancaSpain
  2. 2.Universidad Cooperativa de ColombiaPastoColombia

Personalised recommendations