Skip to main content
Log in

RETRACTED ARTICLE: Machine learning in explaining nonprofit organizations’ participation: a driving factors analysis approach

  • Machine Learning - Applications & Techniques in Cyber Intelligence
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article was retracted on 12 December 2022

This article has been updated

Abstract

The construction of smart cities requires the participation of nonprofit organizations, but there are still some problems in the analysis of driving factors of participation. Based on this, using the structural equation model as the research method, a public satisfaction relationship model, based on the machine learning, for nonprofit organizations participating in the construction planning of smart cities was constructed in this study. At the same time, corresponding assumptions are set, and data are collected through questionnaires. Afterward, the Likert tenth scale was used to score questionnaire questions, and deep learning was conducted in conjunction with the model. The research shows that the model established in this study has good analytical results and has certain practical effects. It can provide suggestions for optimization and can provide theoretical references for subsequent research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

References

  1. Batty M (2013) Big data, smart cities and city planning. Dialogues Hum Geogr 3(3):274

    Article  Google Scholar 

  2. Hashem IAT, Chang V, Anuar NB et al (2016) The role of big data in smart city. Int J Inf Manag 36(5):748–758

    Article  Google Scholar 

  3. Girtelschmid S, Steinbauer M, Kumar V et al (2014) On the application of big data in future large-scale intelligent Smart City installations. Int J Pervasive Comput Commun 10(2):322–328

    Article  Google Scholar 

  4. Silva BN, Khan M, Han K (2017) Big data analytics embedded smart city architecture for performance enhancement through real-time data processing and decision-making. Wirel Commun Mob Comput 2017(7):1–12

    Article  Google Scholar 

  5. Rathore MM, Paul A, Ahmad A et al (2017) IoT-based big data: from smart city towards next generation super city planning. Int J Semant Web Inf Syst 13(1):28–47

    Article  Google Scholar 

  6. Zhu C, Shu L, Leung VCM et al (2017) Secure multimedia big data in trust-assisted sensor-cloud for smart city. IEEE Commun Mag 55(12):24–30

    Article  Google Scholar 

  7. Frith J (2017) Big data, technical communication, and the smart city. J Bus Tech Commun 31(2):168–187

    Article  Google Scholar 

  8. Liu Z (2017) Research on the Internet of Things and the development of smart city industry based on big data. Clust Comput 5:1–7

    Google Scholar 

  9. Souza A, Figueredo M, Cacho N et al (2016) Using big data and real-time analytics to support smart city initiatives. IFAC-PapersOnLine 49(30):257–262

    Article  Google Scholar 

  10. Bi Y, Lin C, Zhou H et al (2017) Time-constrained big data transfer for SDN-enabled smart City. IEEE Commun Mag 55(12):44–50

    Article  Google Scholar 

  11. Song YM, Kim SA, Shin D (2017) Forthcoming big data on smart buildings and cities: an experimental study on correlations among urban data. World Acad Sci Eng Technol Int J Civil Environ Struct Constr Architect Eng 11(4):501–510

    Google Scholar 

  12. Anuradha G, Babu KR (2017) Trends and impact of vehicular tailpipe emission using big data analytics under smart city environment. Int J Sci Eng Res 8(9):269–275

    Google Scholar 

  13. Chang HJ, Kim DN (2016) A study on big data utilization for implementation of the resident participation type safe community planning of the smart city. J Korea Inst Inf Electron Commun Technol 9(5):478–495

    Google Scholar 

  14. Tiwari A (2014) Urban sciences, big data and India’s smart city initiative. Glob J Multidiscip Stud 3(12):14–25

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation under Grant No. 71620107002 and National Social Science Foundation under Grant No. 91538204.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhanxue Gong.

Ethics declarations

Conflict of interest

The authors have no competing interests.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08153-w

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gong, Z., Li, X., Liu, J. et al. RETRACTED ARTICLE: Machine learning in explaining nonprofit organizations’ participation: a driving factors analysis approach. Neural Comput & Applic 31, 8267–8277 (2019). https://doi.org/10.1007/s00521-018-3858-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3858-6

Keywords

Navigation