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Classification of Web Logs Using Hybrid Functional Link Artificial Neural Networks

  • Conference paper
Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 327))

Abstract

Over the decades, researchers are striving to understand the web usage pattern of a user and are also extremely important for the owners of a website. In this paper, a hybrid analyzer is proposed to find out the browsing patterns of a user. Moreover, the pattern which is revealed from this surge of web access logs must be useful, motivating, and logical. A smooth functional link artificial neural network has been used to classify the web pages based on access time and region. The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of functional link neural network using differential evolution. In specific, the differential evolution is used to fine tune the weight vector of this hybrid network and some trigonometric functions are used in functional expansion unit. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer Application, Department of Computer Science and Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar, 751024, India

    Ajit Kumar Behera

  2. Department of Computer Science and Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar, 751024, India

    Ch. Sanjeev Kumar Dash

  3. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, 756019, Odisha, India

    Satchidananda Dehuri

Authors
  1. Ajit Kumar Behera
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  2. Ch. Sanjeev Kumar Dash
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  3. Satchidananda Dehuri
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Corresponding author

Correspondence to Ajit Kumar Behera .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Bhubaneswar Engineering College, Bhubaneswar, Odisha, India

    Bhabendra Narayan Biswal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Department of Computer Science and Engineering, University of Kalyanai, Faculty of Engg., Tech. & Management, Kalyanai, West Bengal, India

    J.K. Mandal

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Behera, A.K., Dash, C.S.K., Dehuri, S. (2015). Classification of Web Logs Using Hybrid Functional Link Artificial Neural Networks. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_28

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  • DOI: https://doi.org/10.1007/978-3-319-11933-5_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11932-8

  • Online ISBN: 978-3-319-11933-5

  • eBook Packages: EngineeringEngineering (R0)

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