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Fuzzy rule-base optimisation using genetic algorithm for mobile web page adaptation

Author

Listed:
  • Neetu Narwal
  • Sanjay Kumar Sharma
  • Amit Prakash Singh

Abstract

There is a global rise in use of mobile devices like mobile phones, PDA, palmtop, etc. for web browsing. Web page usually includes the scrolling that makes web browsing time-consuming. In this work, we used genetic algorithm based fuzzy inference system and utilised the power of genetic algorithm to optimise the fuzzy rules base web content classification. The content of the web page is partitioned into blocks and applies the genetic-based fuzzy inference system to discriminate the main block. The filtered main blocks are then reorganised on the device. As a result of our approach, the mobile web user is presented with the filtered web page content without noise which results in persistent content, fast accessing, and better utilisation of limited space. We implemented the system and result shows that the hybrid genetic-based fuzzy inference system provides better classification accuracy (93.57%) as compared with fuzzy inference system (78.55%) accuracy of classification.

Suggested Citation

  • Neetu Narwal & Sanjay Kumar Sharma & Amit Prakash Singh, 2018. "Fuzzy rule-base optimisation using genetic algorithm for mobile web page adaptation," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 10(4), pages 345-364.
    • Handle: RePEc:ids:ijidsc:v:10:y:2018:i:4:p:345-364
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