Research on performance Evaluation method to promote sustainable development of opening government affairs

Mingle Zhou; Shengli Cao; Ran Wang; Yu Wang

doi:10.1051/e3sconf/202125102097

All issues

Volume 251 (2021)

E3S Web Conf., 251 (2021) 02097

References

Open Access

Issue		E3S Web Conf. Volume 251, 2021 2021 International Conference on Tourism, Economy and Environmental Sustainability (TEES 2021)


Article Number		02097
Number of page(s)		6
Section		Environmental Ecological Analysis and Sustainable Development Research
DOI		https://doi.org/10.1051/e3sconf/202125102097
Published online		15 April 2021

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Jianli, Gao. (2020) Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set. Neural Computing & Applications, PP. 1–16. [Google Scholar]
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[1] Alaouie, A.M. (2019) Assessing whether the design of a state forensic portal of test results complies with requirements of open government data. Forensic Science International, 1. [Google Scholar]

[2] Wang, X.; Dang, D.; Guo, Z. (2020) Evaluating the crowd quality for subjective questions based on a Spark computing environment. Future Generation Computer Systems, 106: 426–437. [Google Scholar]

[3] Fang, Yong; Mingming, Wang. (2010) Research on the Third-party Evaluation Consultation Model of National S&T Planned Projects Based on Government Functions Transformation. In: 6th International Conference on Public Administration. Canberra. pp.136–141. [Google Scholar]

[4] Mrazgua, J.; Tisssir, E.H.; Ouahi, M. (2019) Fuzzy Fault-Tolerant H Control Approach for Nonlinear Active Suspension Systems with Actuator Failure. In: The Second International Conference on Intelligent Computing in Data Sciences. Indonesia. 148:465–474. [Google Scholar]

[5] Collins, E.G. (2003) Fuzzy control systems design and analysis: a linear matrix inequality approach: Kazuo Tanaka and Hua O. Wang. Automatica, 39:2011–2013. [Google Scholar]

[6] Lyu, Y.; Jiang, Y. (2020) Examination on avionics system fault prediction technology based on ashy neural network and fuzzy recognition. Journal of Intelligent & Fuzzy Systems, 38:3939–3947. [Google Scholar]

[7] Elavarasan, D.; Vincent, D.R.; Sharma, V.; Zomaya, A.Y.; Srinivasan, K. (2018) Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers And Electronics In Agriculture, 155:257–282. [Google Scholar]

[8] Jianli, Gao. (2020) Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set. Neural Computing & Applications, PP. 1–16. [Google Scholar]

[9] Lima-Junior, F.R.; Carpinetti, L.C.R. (2020) An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCORÔ metrics. Computers & Industrial Engineering, 139. [Google Scholar]

[10] Souza, P.; Ponce, H.; Lughofer, E. (2020) Evolving fuzzy neural hydrocarbon networks: A model based on organic compounds. Knowledge-based Systems, 203. [Google Scholar]