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An Optimized Deep Convolutional Neural Network Architecture for Concept Drifted Image Classification

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

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Abstract

Machine Learning (ML) is a branch of Artificial Intelligence, which is continuously evolving to overcome current technological challenges faced by industries. These technological changes are due to modernization in industries for Business Intelligence (BI) i.e., 4th Industrial Revolution. Among the other ML approaches, Image Classification plays a significant role for Business Intelligence and upfront several new challenges in online and non-stationary environment, such as Concept Drift. To overcome the CD issue, one of the fundamental requirements is optimization of classifier. Whereas, Convolutional Neural Network (CNN) is considered best classifier/model for Image Classification. Therefore, the aim of this study is to investigate the optimize architecture for CNN in Concept Drifted environment. This study examines the variety of CNN architectures (CNN1 to CNN4) through different configuration of CNN layers and tuning parameters under certain Concept Drift scenarios. Furthermore, a comparative analysis is performed among these CNN models by monitoring their classification accuracy, loss and computational complexity to validate the optimized CNN model experimentally. In future, proposed Optimize Deep Neural Network architecture will be further investigated for high dimensional Imagery data-streams, for example color and multispectral imagery.

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References

  1. Thoben, K.: Industrie 4.0 and smart manufacturing–a review of research issues and application examples. Int. J. Autom. Technol. 11(1), 4–16 (2017)

    Article  Google Scholar 

  2. Jordan, I., Tom, M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  3. Hoens, T.R., Polikar, R: Learning from streaming data with concept drift and imbalance: an overview. Prog Artif. Intell. (2012). https://doi.org/10.1007/s13748-011-0008-0

    Article  Google Scholar 

  4. Jameel, S.M., Hashmani, M.A., Alhussain, H., Budiman, A.: 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 (2019)

    Google Scholar 

  5. Krizhevsky, A. Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (2012)

    Google Scholar 

  6. LeCun, Y. Yoshua, B.: Convolutional networks for images, speech, and time series. In: The handbook of brain theory and neural networks, vol. 3361 no. 10 (1995)

    Google Scholar 

  7. Convolutional Neural Network. https://ww2.mathworks.cn/en/solutions/deep-learning/

  8. Budiman, A., Ivan Fanany, M.: Adaptive online sequential elm for Concept Drift tackling. In: Computational Intelligence and Neuroscience (2016)

    Google Scholar 

  9. Almeida, P., et al.: Adapting dynamic classifier selection for concept drift. Expert Syst. Appl. 104, 67–85 (2018)

    Article  Google Scholar 

  10. Dierckx, W., et al.: Validation of spectral continuity between PROBA-V and SPOT-VEGETATION global daily datasets. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci. 40(7), 1155 (2015)

    Article  Google Scholar 

  11. LeCun, Y., Bottou, L.: Gradient-based learning applied to document recognition. Proc. IEEE 8, 2278–2324 (1998)

    Article  Google Scholar 

  12. Srivastava, N., Hinton, G., et al.: Dropout: a simple way to prevent neural networks from overfitting. Journal of machine learning research 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  13. Zhaohui, L., et al.: CNN-based image analysis for malaria diagnosis. In: Bioinformatics and Biomedicine (BIBM) (2016)

    Google Scholar 

  14. Qiang, G., et al.: Hybrid CNN-HMM model for street view house number recognition. In: Jawahar, C., Shan, S. (eds.) Asian Conference on Computer Vision. Springer, Cham (2014)

    Google Scholar 

  15. Eitz, M., Hays, J., et al.: How do humans sketch objects? ACM Trans. Graph. 31(4), 44-1 (2014)

    Google Scholar 

  16. Yang, Y., Hospedales, T., et al: Deep neural networks for sketch recognition (2015)

    Google Scholar 

  17. Ballester, P., Araújo, R.: On the performance of GoogLeNet and AlexNet applied to sketches. In: AAAI (2016)

    Google Scholar 

  18. Srinivas, S., Sarvadevabhatla, R., et al: A taxonomy of deep convolutional neural nets for computer vision (2016). https://arxiv.org/abs/1601.06615

  19. LeCun, Y., Corinna, C., et al.: MNIST dataset (1998)

    Google Scholar 

  20. Saikat, B., Sangram, G., Robert, D., et al.: Learning sparse feature representations using probabilistic Quadtrees and Deep belief nets. In: European Symposium on Artificial Neural Networks, ESANN (2015)

    Google Scholar 

Download references

Acknowledgment

This research study is conducted in Universiti Teknologi PETRONAS (UTP), Malaysia as a part of research project “Correlation between Concept Drift Parameters and Performance of Deep Learning Models: Towards Fully Adaptive Deep Learning Models” under Fundamental Research Grant Scheme (FRGS) Ministry of Higher Education (MOHE) Malaysia.

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Correspondence to Syed Muslim Jameel , Manzoor Ahmed Hashmani , Hitham Alhussain , Mobashar Rehman or Arif Budiman .

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Jameel, S.M., Hashmani, M.A., Alhussain, H., Rehman, M., Budiman, A. (2020). An Optimized Deep Convolutional Neural Network Architecture for Concept Drifted Image Classification. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_70

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