Skip to main content
SpringerLink
Log in

Pattern Recognition in Convolutional Neural Network (CNN)

  • Conference paper
  • First Online:
Application of Intelligent Systems in Multi-modal Information Analytics (ICMMIA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 138))

Included in the following conference series:

Abstract

One of the main topics of human science research is to explain the mechanism of brain activity and the essence of human intelligence, create intelligent machines similar to human intelligent activities, and develop the technology and intelligent application of human intelligence. The research on pattern recognition using CNN technology is a hot topic. Therefore, this paper studies the problems faced by pattern recognition and explores it. Firstly, this paper discusses the research background and significance of CNN, and briefly introduces the characteristics of CNN. Then it summarizes the relevant articles at home and abroad. Then it briefly describes the BP neural model used in eep and its existing reasons, and explains the better effects they may bring. Finally, a series of experimental tests are carried out on pattern recognition. It is concluded that the network with scaling parameter set to 4 and depth of about 26 has the best effect, which can effectively solve the problem of pattern recognition.

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

Access this chapter

Log in via an institution
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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li, D., Zhao, X., Yuan, G., Liu, Y., Liu, G.: Robustness comparison between the capsule network and the convolutional network for facial expression recognition. Appl. Intell. 51(4), 2269–2278 (2020). https://doi.org/10.1007/s10489-020-01895-x

    Article  Google Scholar 

  2. Kong, Q., Cao, Y., Iqbal, T., et al.: PANNs: large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 28, 2880–2894 (2020)

    Article  Google Scholar 

  3. Dai, Y., Qiu, D., Wang, Y., et al.: Research on computer-aided diagnosis of Alzheimer’s disease based on heterogeneous medical data fusion. Int. J. Pattern Recogn. Artif. Intell. 33(5), 1957001.1–1957001.17 (2019)

    Google Scholar 

  4. Zhang, Q., Zhang, M., Chen, T., et al.: Recent advances in Convolutional Neural Network acceleration. Neurocomputing 323, 37–51 (2019)

    Article  Google Scholar 

  5. Ahmed, T., Parvin, M.S., Haque, M.R., et al.: Lung cancer detection using CT image based on 3D CNN. Comput. Commun. (English) 3, 35–42 (2020)

    Article  Google Scholar 

  6. Afridi, M.J., et al.: On automated source selection for transfer learning in CNNs. Pattern Recogn. J. Pattern Recogn. Soc. 73, 65–75 (2018)

    Article  Google Scholar 

  7. Nguyen, H.D., Yeom, S., Lee, G.S., et al.: Facial emotion recognition using an ensemble of multi-level CNNs. Int. J. Pattern Recogn. Artif. Intell. 33(11), 139–149 (2018)

    Google Scholar 

  8. Banharnsakun, A.: Towards improving the Convolutional Neural Networks for deep learning using the distributed artificial bee colony method. Int. J. Mach. Learn. Cybern. 10(6), 1301–1311 (2019)

    Article  Google Scholar 

  9. Liu, W., Wang, Z., Liu, X., et al.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  10. Zahangir, A.M., Mahmudul, H., Chris, Y., et al.: Improved inception-residual convolutional neural network for object recognition. Neural Comput. Appl. 32, 1–15 (2017)

    Google Scholar 

  11. Wu, Z., Jiang, S., Zhou, X., et al.: Application of image retrieval based on Convolutional Neural Networks and Hu invariant moment algorithm in computer telecommunications. Comput. Commun. 150, 729–738 (2020)

    Article  Google Scholar 

  12. Ashiquzzaman, A., Tushar, A.K.: Handwritten Arabic numeral recognition using deep learning neural networks. In: IEEE 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR) - Dhaka, Bangladesh, 13–14 February 2017, pp. 1–4 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Surrey, Guildford, UK

    Zhengyu Sun

Authors
  1. Zhengyu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengyu Sun .

Editor information

Editors and Affiliations

  1. School of Business Administration, Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  2. Amrita School of Engineering, Chennai, Tamil Nadu, India

    A. G. Sreedevi

  3. Shanghai Polytechnic University, Shanghai, China

    Zheng Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, Z. (2022). Pattern Recognition in Convolutional Neural Network (CNN). In: Sugumaran, V., Sreedevi, A.G., Xu , Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-031-05484-6_37

Download citation

Publish with us

Policies and ethics

Search

Navigation