Skip to main content

A Framework Design of National Healthy Diet Monitoring System

  • Conference paper
  • First Online:
Smart Health (ICSH 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11924))

Included in the following conference series:

  • 813 Accesses

Abstract

Proper diet is one of the most important prerequisites for people’s health. Resident nutrition and diet building are crucial to the success of national health program of China in its new stage of development. Although there has been numerous research on dietary and its relation of health, there is a lack of overall vision of the current dietary situations in China. In this paper, a national health diet monitoring system is proposed based on the national nutrition survey and the Internet nutrition diet related data, using the text knowledge discovery method. The purpose of the proposed framework is to find out the current situation of diet and nutrition of national residents, monitor the dietary purchase, dietary habits and changes of residents, and provide decision-making support for the formulation of relevant policies and the dietary guidance and improvement of residents.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lynch, C.: Big data: how do your data grow? Nature 455(7209), 28 (2008)

    Article  Google Scholar 

  2. Zhu, H., Bin, H.: Impact of information on public opinion reversal – an agent based model. Phys. A 512, 578–587 (2018)

    Article  MathSciNet  Google Scholar 

  3. Young, C., et al.: Supporting engagement, adherence, and behavior change in online dietary interventions. J. Nutr. Educ. Behav. 51(6), 719–739 (2019)

    Article  Google Scholar 

  4. Koronakos, G., et al.: Assessment of the OECD’s better life index by incorporating the public opinion. Soc. Econ. Plann. Sci. (2019, in press). https://doi.org/10.1016/j.seps.2019.03.005

  5. Guo, Q.Y., et al.: Comparative analysis of 1959, 1982, 1992, 2002 and 2010–2013 Chinese residents’ nutrition and health status survey/monitoring. Health Res. 45(4), 542–547 (2016). (in Chinese)

    Google Scholar 

  6. Holovaty, A., kaplin-moss, J.: The definitive guide to Django: Web development done right. Apress (2009)

    Google Scholar 

  7. Pu, W., Cao, L., Xia, B.: Design of keyword ranking monitoring system based on Django framework. Microcomput. Appl. 20(2017), 97–100. (in Chinese)

    Google Scholar 

  8. Feng, C., Yang, B.: Design and implementation of JD data analysis system based on scrapy framework. Value Eng. 28, 111 (2018). (in Chinese)

    Google Scholar 

  9. Xu, L., et al.: Construction of affective lexical ontology. Acta Intell. 27(2), 180–185 (2008)

    Google Scholar 

  10. Rong, X.: Word2vec parameter learning explained. arXiv preprint arXiv:1411.2738 (2014)

  11. Pauls, A., Klein, D.: Faster and smaller n-gram language models. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics (2011)

    Google Scholar 

  12. Bengio, Y., et al.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)

    Google Scholar 

  13. Ma, L., Zhang, Y.: Using Word2Vec to process big text data. In: 2015 IEEE International Conference on Big Data (Big Data). IEEE (2015)

    Google Scholar 

  14. Zhang, D., et al.: Chinese comments sentiment classification based on word2vec and SVMperf. Expert Syst. Appl. 42(4), 1857–1863 (2015)

    Article  Google Scholar 

  15. Trstenjak, B., Mikac, S., Donko, D.: KNN with TF-IDF based framework for text categorization. Procedia Eng. 69, 1356–1364 (2014)

    Article  Google Scholar 

  16. Huang, C.-H., Yin, J., Hou, F.: A text similarity measurement combining word semantic information with TF-IDF method. Jisuanji Xuebao (Chin. J. Comput.) 34(5), 856–864 (2011)

    Article  Google Scholar 

  17. Leys, C., et al.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49(4), 764–766 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This project is supported by the Key Project of Chinese Academy of Sciences (No. KJZD-EW-G20), Projects of Social Science Program of Beijing Education Commission (No. SM201810037001 and SM201910037004) and Social Beijing Social Science Foundation (No. 18GLB022).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Shang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, L., Ma, XQ., Shang, W. (2019). A Framework Design of National Healthy Diet Monitoring System. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34482-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34481-8

  • Online ISBN: 978-3-030-34482-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics