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Large Scale Deep Network Architecture of CNN for Unconstraint Visual Activity Analytics

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

Handling the issues of massive datasets for information retrieval, feature learning, is expected one of the most challenging problems in machine learning and computer vision research. The issues in this work, have been focused to maintain the data scalability problems for machine learning classifiers in social media activity analysis. The research highlights the machine learning performance techniques which can provide promising results against the large and unstructured complex data of social media activities. This work has been focused on the biologically inspired processing techniques by neural network and introduces the extension of this network to resolve the problems of complex data pertaining to human activity analysis. It is presented various architectures of CNN and several phases of visual data processing for detection and recognition problems. Some selected techniques are highlighted that create the interest for deep network learning in various domains of research under the consideration of complex data handlings. It has been introduced activation functions and sequence pooling methodology for fast training of convolutional network with massive data of unstructured human activity recognition. Overall, it is highlighted that fast training aspects of the network against large scale and complex data, can be improved by choosing activation function and pooling methodology at fully connected layers of the neural network. Moreover, the sounding techniques of deep learning and data analytics are highly applicable for human health, medicine, robotics, education and industrial applications.

N. Kumar—IEEE Member.

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Author information

Authors and Affiliations

  1. Computer Society of India (CSI), Delhi, India

    Naresh Kumar

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Naresh Kumar

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  1. Naresh Kumar
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Correspondence to Naresh Kumar .

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Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Kumar, N. (2018). Large Scale Deep Network Architecture of CNN for Unconstraint Visual Activity Analytics. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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