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Cluster Computing

, Volume 22, Supplement 2, pp 4397–4403 | Cite as

Enhanced perceptual psychopathology correlation mechanism model based on rough set and neural network

  • Xiaobing YangEmail author
  • Hongyun Liu
  • Yongmei Peng
Article
  • 29 Downloads

Abstract

Aimed at sensibility—psychopathology problem, undergraduates are selected to be research object and a correlative mechanism model for rough set and neural network of reinforced sensibility—psychopathology is proposed. Firstly, five characteristics are taken as example to analyze common psychological illnesses of undergraduates. These five characteristics include: behavior, emotional state, diet and sleep, character trait and physical illness, and four common psychological illnesses of undergraduates are selected as training to construct BP neural network evaluation model; then, upper and lower approximation of rough set and boundary region theory are adopted for noise removal of noise sample, which can solve sample noise interference of BP network pattern recognition effectively and reduce error recognition rate of BP network significantly. Finally, performance advantage of algorithm is verified through simulation experiment.

Keywords

Sensibility Psychopathology Rough set Neural network Correlative model 

Notes

Acknowledgements

Educational science plan of Hunan Province in 2014, Subject No.: XJK014BJD012.

References

  1. 1.
    Read, S.J., Monroe, B.M., Brownstein, A.L., et al.: A neural network model of the structure and dynamics of human personality. Psychol. Rev. 117(1), 61 (2010)Google Scholar
  2. 2.
    Sander, D., Grandjean, D., Scherer, K.R.: A systems approach to appraisal mechanisms in emotion. Neural Netw. 18(4), 317–352 (2005)Google Scholar
  3. 3.
    Schlesinger, M., McMurray, B.: The past, present, and future of computational models of cognitive development. Cogn. Dev. 27(4), 326–348 (2012)Google Scholar
  4. 4.
    Pitti, A., Kuniyoshi, Y., Quoy, M., et al.: Modeling the minimal newborn’s intersubjective mind: the visuotopic-somatotopic alignment hypothesis in the superior colliculus. PLoS ONE 8(7), e69474 (2013)Google Scholar
  5. 5.
    Lewis, M.D.: Bridging emotion theory and neurobiology through dynamic systems modeling. Behav. Brain Sci. 28(2), 169–194 (2005)Google Scholar
  6. 6.
    Miller, G.A., Rockstroh, B.: Endophenotypes in psychopathology research: where do we stand? Annu. Rev. Clin. Psychol. 9, 177–213 (2013)Google Scholar
  7. 7.
    Hood, S.B., Lovett, B.J.: Network models of psychopathology and comorbidity: philosophical and pragmatic considerations. Behav. Brain Sci. 33(2–3), 159–160 (2010)Google Scholar
  8. 8.
    Taylor, S.F., Stern, E.R., Gehring, W.J.: Neural systems for error monitoring: recent findings and theoretical perspectives. The Neuroscientist 13(2), 160–172 (2007)Google Scholar
  9. 9.
    Krueger, R.F., DeYoung, C.G., Markon, K.E.: Toward scientifically useful quantitative models of psychopathology: the importance of a comparative approach. Behav. Brain Sci. 33(2–3), 163–164 (2010)Google Scholar
  10. 10.
    Radulescu, A.: A multi-etiology model of systemic degeneration in schizophrenia. J. Theor. Biol. 259(2), 269–279 (2009)Google Scholar
  11. 11.
    Decety, J., Michalska, K.J., Akitsuki, Y., et al.: Atypical empathic responses in adolescents with aggressive conduct disorder: a functional MRI investigation. Biol. Psychol. 80(2), 203–211 (2009)Google Scholar
  12. 12.
    Liu, C., Li, Y., Zhang, Y., Yang, C., Hongbin, W., Qin, J., Cao, Y.: Solution-processed, undoped, deep-blue organic light-emitting diodes based on starburst oligofluorenes with a planar triphenylamine core. Chem. Eur. J. 18(22), 6928–6934 (2012)Google Scholar
  13. 13.
    Abdelhamid, D., Arslan, H., Zhang, Y., Uhrich, K.E.: Role of branching of hydrophilic domain on physicochemical properties of amphiphilic macromolecules. Polym. Chem. 5(4), 1457–1462 (2014)Google Scholar
  14. 14.
    Abdelhamid, D.S., Zhang, Y., Lewis, D.R., Moghe, P.V., Welsh, W.J., Uhrich, K.E.: Tartaric acid-based amphiphilic macromolecules with ether linkages exhibit enhanced repression of oxidized low density lipoprotein uptake. Biomaterials 53, 32–39 (2015)Google Scholar
  15. 15.
    Abdulhay, E., Mohammed, M.A., Ibrahim, D.A., Arunkumar, N., Venkatraman, V.: Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images. J. Med. Syst. (2018).  https://doi.org/10.1007/s10916-018-0912-y Google Scholar
  16. 16.
    Arunkumar, N., Ramkumar, K., Venkatraman, V.: Entropy features for focal EEG and non focal EEG. J. Comput. Sci. (2018).  https://doi.org/10.1016/j.jocs.2018.02.002 Google Scholar
  17. 17.
    Liu, C., Arunkumar, N.: Risk prediction and evaluation of transnational transmission of financial crisis based on complex network. Clust. Comput. (2018).  https://doi.org/10.1007/s10586-018-1870-3 Google Scholar
  18. 18.
    Meng, G., Arunkumar, N.: Construction of employee training program evaluation system of three exponential forecast based on sliding window. Clust. Comput. (2018).  https://doi.org/10.1007/s10586-017-1652-3 Google Scholar
  19. 19.
    Chen, X., Pang, L., Guo, P., Sun, X., Xue, Z., Arunkumar, N.: New upper degree of freedom in transmission system based on wireless G-MIMO communication channel. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1513-0 Google Scholar
  20. 20.
    Hamza, R., Muhammad, K., Nachiappan, A., González, G.R.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017).  https://doi.org/10.1109/ACCESS.2017.2762405 Google Scholar
  21. 21.
    Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recogn. Lett. (2017).  https://doi.org/10.1016/j.patrec.2017.07.002 Google Scholar
  22. 22.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)Google Scholar
  23. 23.
    Arunkumar, N., Ramkumar, K., Venkataraman, V.: A moving window approximate entropy in wavelet framework for automatic detection of the onset of epileptic seizures. Biomed. Res. (2017). Special Issue: ISSN 0970-938XGoogle Scholar
  24. 24.
    Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hunan Traditional Chinese Medical CollegeZhuzhouChina

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