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.
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12 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00521-022-08153-w
References
Batty M (2013) Big data, smart cities and city planning. Dialogues Hum Geogr 3(3):274
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
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
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
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
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
Frith J (2017) Big data, technical communication, and the smart city. J Bus Tech Commun 31(2):168–187
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
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
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
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
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
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
Tiwari A (2014) Urban sciences, big data and India’s smart city initiative. Glob J Multidiscip Stud 3(12):14–25
Acknowledgements
This work is supported by National Natural Science Foundation under Grant No. 71620107002 and National Social Science Foundation under Grant No. 91538204.
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08153-w
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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
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DOI: https://doi.org/10.1007/s00521-018-3858-6