Skip to main content

A Study on the Crisis Recognition Model Using Machine Learning-Based Bio-Signals

  • Chapter
  • First Online:
  • 470 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 936))

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Kim, T., Hong, J., Kang, P.: Box office forecasting using machine learning algorithms based on SNS data. Int. J. Forecast. 31(2), 364–390 (2015)

    Article  Google Scholar 

  2. Jung, D.K., Kim, K.N., Kim, G.R., Shim, D.H., Kim, M.H., Choi, B.C., Suh, D.J.: Biosignal monitoring system for mobile telemedicine. In: Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005, pp. 31–36. IEEE (2005)

    Google Scholar 

  3. Kelati, A., Dhaou, I.B., Tenhunen, H.: Biosignal monitoring platform using Wearable IoT. In: Proceedings of the 22st Conference of Open Innovations Association FRUCT, Petrozavodsk, Russia, pp. 9–13 (2018)

    Google Scholar 

  4. Halde, R.R.: Application of machine learning algorithms for betterment in education system. In: 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 1110–1114. IEEE (2016)

    Google Scholar 

  5. Bakare, J., Orji, C.T., Wogu, J.O., Ogbonna, C.A.: Effectiveness of teleconferencing in Nigerian universities: a descriptive approach. Int. J. u-and e-Service, Sci. Technol. 11(3), 27–38 (2018)

    Article  Google Scholar 

  6. Bellinger, C., Sharma, S., Japkowicz, N.: One-class versus binary classification: Which and when? In: 2012 11th International Conference on Machine Learning and Applications, vol. 2, pp. 102–106. IEEE (2012)

    Google Scholar 

  7. Lee, D.H., Fu, Y.: Cross-entropy optimization model for population synthesis in activity-based microsimulation models. Transp. Res. Rec. 2255(1), 20–27 (2011)

    Article  Google Scholar 

  8. Gómez, R.: Understanding categorical cross-entropy loss, binary cross-entropy loss, softmax loss, logistic loss, focal loss and all those confusing names. Retrieved 23 November, 2019 (2018)

    Google Scholar 

  9. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M.,Ghemawat, S.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

  10. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M.,Kudlur, M.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265–283 (2016)

    Google Scholar 

  11. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  12. Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111–122 (2011)

    Google Scholar 

  13. Schmidt-Hieber, J.: Nonparametric regression using deep neural networks with ReLU activation function. Ann. Stat. 48(4), 1875–1897 (2020)

    Google Scholar 

  14. Peng, H., Li, J., Song, Y., Liu, Y.: Incrementally learning the hierarchical softmax function for neural language models. In: AAAI, pp. 3267–3273 (2017)

    Google Scholar 

  15. He, Y.L., Zhang, X.L., Ao, W., Huang, J.Z.: Determining the optimal temperature parameter for Softmax function in reinforcement learning. Appl. Soft Comput. 70, 80–85 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Research Program funded by the Seoul Theological University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joon-Yong Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kim, JY. (2021). A Study on the Crisis Recognition Model Using Machine Learning-Based Bio-Signals. In: Tripathy, H.K., Mishra, S., Mallick, P.K., Panda, A.R. (eds) Technical Advancements of Machine Learning in Healthcare. Studies in Computational Intelligence, vol 936. Springer, Singapore. https://doi.org/10.1007/978-981-33-4698-7_18

Download citation

Publish with us

Policies and ethics