Skip to main content
SpringerLink
Log in

A Fully Adaptive Image Classification Approach for Industrial Revolution 4.0

  • Conference paper
  • First Online:
Recent Trends in Data Science and Soft Computing (IRICT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 843))

Abstract

Industrial Revolution (IR) improves the way we live, work and interact with each other by using state of the art technologies. IR-4.0 describes a future state of industry which is characterized through the digitization of economic and production flows. The nine pillars of IR-4.0 are dependent on Big Data Analytics, Artificial Intelligence, Cloud Computing Technologies and Internet of Things (IoT). Image datasets are most valuable among other types of Big Data. Image Classification Models (ICM) are considered as an appropriate solution for Business Intelligence. However, due to complex image characteristics, one of the most critical issues encountered by the ICM is the Concept Drift (CD). Due to CD, ICM are not able to adapt and result in performance degradation in terms of accuracy. Therefore, ICM need better adaptability to avoid performance degradation during CD. Adaptive Convolutional ELM (ACNNELM) is one of the best existing ICM for handling multiple types of CD. However, ACNNELM does not have sufficient adaptability. This paper proposes a more autonomous adaptability module, based on Meta-Cognitive principles, for ACNNELM to further improve its performance accuracy during CD. The Meta-Cognitive module will dynamically select different CD handling strategies, activation functions, number of neurons and restructure ACNNELM as per changes in the data.

This research contribution will be helpful for improvement in various practical applications areas of Business Intelligence which are relevant to IR-4.0 and TN50 (e.g., Automation Industry, Autonomous Vehicle, Expert Agriculture Systems, Intelligent Education System, and Healthcare etc.).

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Abbreviations

ACNNELM:

Adaptive Convolutional Neural Network Extreme Learning Machine

MOSELM:

Meta-Cognitive Online Sequential Extreme Learning Machine

OSELM:

Online Sequential Extreme Learning Machine

RD:

Real Drift

VD:

Virtual Drift

HD:

Hybrid Drift

N-Ad:

Non-Adaptive

S-Ad:

Semi-Adaptive

F-Ad:

Fully Adaptive

B:

Binary

M:

Multi

NI:

Non Imaging

SI:

Synthetic Image

RI:

Real Image

References

  1. Sun, Y., Tang, K., Zhu, Z., Yao, X.: Concept drift adaptation by exploiting historical knowledge. In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11 (2018)

    Google Scholar 

  2. Kotsiantis, S.B.: Emerging Artificial Intelligence Applications in Computer Engineering, 1st edn. IOS Press, Amsterdam Netherlands (2007)

    Google Scholar 

  3. Nelson, T.O., Narens, L.: Meta-memory: a theoretical framework and new findings. Psychol. Learn. Motiv. 26(1), 125–173 (1990)

    Article  Google Scholar 

  4. Budiman, A., Fanany, M.I., Basaruddin, C.: Adaptive online sequential ELM for concept drift tackling. Comput. Intell. Neurosci. 20, 2016 (2016)

    Google Scholar 

  5. Zliobaite, I., et al.: Next challenges for adaptive learning systems. ACM SIGKDD Explor. Newsl. 14(1), 48 (2012)

    Article  Google Scholar 

  6. Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. 42(2), 513–529 (2012)

    Article  Google Scholar 

  7. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in Big Data analytics. J. Big Data 2(1), 1 (2015)

    Article  Google Scholar 

  8. Hussain S., Hashmani M.: Image retrieval based on color and texture feature using artificial neural network. In: Chowdhry B.S., Shaikh F.K., Hussain D.M.A., Uqaili M.A. (eds.) IMTIC 2012, vol 281. Springer, Berlin

    Google Scholar 

  9. Budiman, A., Fanany, M.I., Basaruddin, C.: Adaptive Parallel ELM with Convolutional Features for Big Stream Data. Thesis Dissertation, Faculty of Computer Science, University of Indonesia (2017). https://doi.org/10.13140/rg.2.2.18500.22404

  10. Huang, H.K., Chiu, C.F., Kuo, C.H., Wu, Y.C., Chu, N.Y.Y., Chang, P.C.: Mixture of deep CNN-based ensemble model for image retrieval. In: 5th Global Conference on Consumer Electronics, pp. 1–2. IEEE, Kyoto (2016)

    Google Scholar 

  11. Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli F., Kittler J., Windeatt T. (eds.) Multiple Classifier Systems. MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Berlin (2004)

    Google Scholar 

  12. Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling Concept Drift. Inf. Fusion Arch. 9(1), 56–68 (2008)

    Article  Google Scholar 

  13. Mirza, B., Lin, Z.: Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Netw. 80, 79–94 (2016)

    Article  Google Scholar 

  14. Zliobaite, I.: Learning under Concept Drift: An Overview, pp. 1–36. Cornell University Library (2010). arxiv.org/abs/1010.4784

    Google Scholar 

  15. van Schaik, A., Tapson, J.: Online and adaptive pseudoinverse solutions for ELM weights. Neurocomputing 149(Part A), 233–238 (2015)

    Article  Google Scholar 

  16. Xu, S., Wang, J.: Dynamic extreme learning machine for data stream classification. Neurocomputing 238(A), 433–449 (2017)

    Article  Google Scholar 

  17. Xu, S., Wang, J.: A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst. Appl. 65, 332–344 (2016)

    Article  Google Scholar 

  18. Krawczyk, B.: Reacting to different types of Concept Drift one-class classifiers. In: 2nd International Conference on Cybernetics, pp. 30–35, IEEE, Gdynia (2015)

    Google Scholar 

  19. Brzezinski, D., Stefanowski, J.: Reacting to different types of Concept Drift: the accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 81–94 (2014)

    Article  Google Scholar 

  20. Cao, K., Wang, G., Han, D., Ning, J., Zhang, X.: Classification of uncertain data streams based on extreme learning machine. Cogn. Comput. 7(1), 150–160 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universiti Teknologi PETRONAS, Sri Iskandar, Perak, Malaysia

    Syed Muslim Jameel, Manzoor Ahmed Hashmani & Hitham Alhussain

  2. High Performance Cloud Computing Center (HPC3), Universiti Teknologi PETRONAS, Sri Iskandar, Perak, Malaysia

    Manzoor Ahmed Hashmani & Hitham Alhussain

  3. University of Indonesia, Jakarta, Indonesia

    Arif Budiman

Authors
  1. Syed Muslim Jameel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manzoor Ahmed Hashmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hitham Alhussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arif Budiman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Muslim Jameel .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Nadhmi Gazem

  3. Information Technology Department, Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen

    Fathey Mohammed

  4. Information Systems Department, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Malaysia

    Abdelsalam Busalim

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

Jameel, S.M., Hashmani, M.A., Alhussain, H., Budiman, A. (2019). A Fully Adaptive Image Classification Approach for Industrial Revolution 4.0. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_30

Download citation

Publish with us

Policies and ethics

Search

Navigation