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Deep Learning in Big Data and Internet of Things

  • Dimpal TomarEmail author
  • Pradeep TomarEmail author
  • Gurjit KaurEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)

Abstract

Due to constant escalation in the pace of information technology, the potential of Big Data and Internet of Things (IoT). Data induce high focus in data science field. As in IoT, large number of smart devices generate or collect enormous amount of data, over the period of time for various domain. However, IoT are basically one of the main source for the generation of big data. This massive volume of data contains valuable information upon which many organizations applying analytics for bang into future technology and favorable for business analysis and decision-making. Deep Learning is the high focus of advanced machine learning and facilitating analytics in various territories of Big Data and IoT by extracting complex feature abstraction or representation from different forms of data through hierarchical process. This review paper putting a focus on the overview of unique and modern technique of machine learning i.e. Deep Learning followed by a detailed consideration on models and algorithms. We also try to cover frameworks, opted for implementing Deep Learning and their use in various Big Data and IoT applications. We also investigate various Deep Learning applications in the realm of Big Data and IoT. Further try to incorporate challenges in several areas of Big Data and IoT. Finally, we conclude this work along with the future work.

Keywords

Big data Deep learning IoT 

References

  1. 1.
    Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., Marrs, A.: Disruptive technologies: advances that will transform life, business, and the global economy. McKinsey Global Institute San Francisco (2013)Google Scholar
  2. 2.
    Najafabadi, M.M., Villanustre, F., Khoshgoftaar, M.T., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015)Google Scholar
  3. 3.
    Gheisari, M., Wang, G., Bhuiyan, M.Z.A.: A survey on deep learning in big data. In: Proceedings of IEEE International Conference on Computational Science and Engineering and Embedded and Ubiquitous Computing (2017)Google Scholar
  4. 4.
    Fadlullah, Z., et al.: State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19(4), 2432–2455 (2017)CrossRefGoogle Scholar
  5. 5.
    Tsai, C.W., Lai, C.F., Chiang, M.C., Yang, L.T.: Data mining for internet of things: a survey. IEEE Commun. Surv. Tutorials 16(1), 77–97 (2014)CrossRefGoogle Scholar
  6. 6.
    Mohammadi, M., Al-F, A.: Enabling cognitive smart cities using big data and machine learning: approaches and challenges. IEEE Commun. Mag. 56(2), 1–8 (2017)CrossRefGoogle Scholar
  7. 7.
    Chen, X., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014)CrossRefGoogle Scholar
  8. 8.
    Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. ACM SIGKDD Explor. Newslett. 14(2), 1–5 (2013)CrossRefGoogle Scholar
  9. 9.
    Chen, M., Mao, S., Zhang, Y., Leung, V.C.: Big Data: Related Technologies, Challenges and Future Prospects. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-319-06245-7CrossRefGoogle Scholar
  10. 10.
    Hilbert, M.: Big data for development: a review of promises and challenges. Dev. Policy Rev. 34(1), 135–174 (2016)CrossRefGoogle Scholar
  11. 11.
    Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)CrossRefGoogle Scholar
  12. 12.
    Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks (2013)Google Scholar
  13. 13.
    Hermans, M., Schrauwen, B.: Training and analysing deep recurrent neural network. In: Advances in Neural Information Processing System, pp. 190–198 (2013)Google Scholar
  14. 14.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  15. 15.
    Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: ICML Unsupervised and Transfer Learning, pp. 37–50 (2012)Google Scholar
  16. 16.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends R Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Doersch, C.: Tutorial on variational autoencoders (2016)Google Scholar
  18. 18.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  19. 19.
    Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.: Semi-supervised learning with ladder networks. In: Advances in Neural Information Processing Systems, pp. 3546–3554 (2015)Google Scholar
  20. 20.
    Bahrampour, S., Ramakrishnan, N., Schott, L., Shah, M.: Comparative study of deep learning software frameworks (2016)Google Scholar
  21. 21.
    Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop (2011)Google Scholar
  22. 22.
    Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed system (2016)Google Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., Hinton G.E.: Image net classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  24. 24.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  25. 25.
    Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 6, 1–11 (2016)CrossRefGoogle Scholar
  26. 26.
    Song, X., Kanasugi, H., Shibasaki, R.: Deep transport: prediction and simulation of human mobility and transportation mode at a citywide level. In: IJCAI (2016)Google Scholar
  27. 27.
    Tian, Y., Pan, L.: Predicting short-term traffic flow by long short-term memory recurrent neural network. In: Proceedings of IEEE International Conference, pp. 153–158 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Gautam Buddha UniversityGreater NoidaIndia
  2. 2.Delhi Technological UniversityNew DelhiIndia

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