Advertisement

Business Intelligence: Determination of Customers Satisfaction with the Detection of Facial Expression

  • Fariba Rezaei Arefi
  • Fatemeh SaghafiEmail author
  • Masoud Rezaei
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 800)

Abstract

Nowadays, effective use of technology in the competitive world is the most important factor in business success. Since the stakeholders and especially customers are the most important factor in the profitability of organizations, the use of new methods in determining their satisfaction is of particular importance. In this paper, business intelligence for determining customer image satisfaction using image mining technology was performed. First, 400 images of customers’ faces were selected from the database. Investigating the facial expressions and the use of experts suggests that 4 states of 7 faces are sufficient to determine satisfaction. In the next step, the results of the study were extracted with using three algorithms and were analyzed in the following with three Classification Regression Tree (CRT) algorithms, Neural Network and Decision Tree. In the end between image mining algorithms the Bayesian Method and among data mining algorithms the CRT algorithm were detected with the least error for satisfaction detection.

Keywords

Image mining Data mining Business intelligence 

References

  1. 1.
    Khanum, A., Mufti, M., Javed, M.Y., Shafiq, M.Z.: Fuzzy case-based reasoning for facial expression recognition. Fuzzy Sets Syst. 231–250 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Shang, Z., Joshi, J.: Hoey, J.: Continuous facial expression recognition for affective interaction with virtual avatar (2017)Google Scholar
  3. 3.
    Sabri, M., Kurita, T.: Facial Expression Intensity Estimation Using Siamese and Triplet Networks. Neurocomputing. 313, 143–154(3) (2018)CrossRefGoogle Scholar
  4. 4.
    Pandey, K.K., Mohanty, P.K., Parhi, D.R.: Real time navigation strategies for webots using fuzzy controller. IEEE 8th International Conference on Intelligent Systems and Control (ISCO) (2014)Google Scholar
  5. 5.
    Jamshidnezhad, A., Nordin, M.J.: A modified genetic model based on the queen bee algorithm for facial expression classification. J. Comput. Theor. Nanosci. 9, 1109–1114(6) (2012)CrossRefGoogle Scholar
  6. 6.
    Danilov, D.I., Lakhtin, A.S.: Optimization of the algorithm for determining the hausdorff distance for convex polygons. Ural Math. J. 4, 14 (2018)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Piat, F., Tsapatsoulis, N.: Exploring the time course of facial expressions with a fuzzy system. International conference on. Vol. 2. IEEE (2000)Google Scholar
  8. 8.
    Valstar, M., Pantic, M.: Fully automatic facial action unit detection and temporal analysis. IEEE Comput. Soc. (2006)Google Scholar
  9. 9.
    Tumula, S., Fathima, S.S.: Probabilistic graphical models for medical image mining challenges of new generation. In: Knowledge Computing and its Applications. Springer, Singapore (2018)Google Scholar
  10. 10.
    Sudhir, R.: A survey on image mining techniques: theory and applications. Comp. Eng. Intell. Syst. 2(6), 44–53 (2011)Google Scholar
  11. 11.
    Kun-Che, L., Don-Lin, Y.: Image Processing and image mining using decision trees. J. Inf. Sci. Eng. 25, 989–1003 (2009)Google Scholar
  12. 12.
    Parikh, R., Mathai, A., Parikh, S., et al.: Understanding and using sensitivity, specificity and predictive values. Indian J. Ophthalmol. 56(1), 45–50 (2008)CrossRefGoogle Scholar
  13. 13.
    Delen, D., Cemil, K., Ali, U.: Measuring firm performance using financial ratios: a decision tree approach. Expert Syst. Appl. 40(10), 3970–3983 (2013)CrossRefGoogle Scholar
  14. 14.
    Dai, Q., Li, J., Wang, J., Chen, Y., Jiang, Y.: A Bayesian hashing approach and its application to face recognition. Neurocomputing. 213, 5–13 (2016)CrossRefGoogle Scholar
  15. 15.
    Kh.Lekdioui, , R. Messoussi, Y. Ruichek, Y. Chaabi, R. Touahni, "Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier." Signal Process. Image Commun. 58 (2017): 300–312CrossRefGoogle Scholar
  16. 16.
    Guo, Y., Li, G., Wang, J., et al.: Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining. Appl. Therm. Eng. 125, 1402–1413 (2017)CrossRefGoogle Scholar
  17. 17.
    Kellehe, J.D., Namee, B.M., Aoife, D.A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press, Cambridge, MA, USA (2015)Google Scholar
  18. 18.
    Barros, R.C., et al.: A survey of evolutionary algorithms for decision-tree induction. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(3), 291–312 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fariba Rezaei Arefi
    • 1
  • Fatemeh Saghafi
    • 2
    Email author
  • Masoud Rezaei
    • 3
  1. 1.Faculty of New science and Technology, University of TehranTehranIran
  2. 2.Faculty of ManagementUniversity of TehranTehranIran
  3. 3.Alborz Campus, Department of ManagementUniversity of TehranTehranIran

Personalised recommendations