This paper is published in Volume-3, Issue-2, 2017
Area
Software Engineering
Author
Jayanthi .B, Dr. S. Suganya
Org/Univ
RVS College of Arts and Science, Sulur, India
Pub. Date
24 April, 2017
Paper ID
V3I2-1537
Publisher
Keywords
Extreme Learning Machine (ELM), Grey Wolf Optimizer (GWO), Support Vector Machine (SVM), Webpage Quality Improvement.

Citationsacebook

IEEE
Jayanthi .B, Dr. S. Suganya. A Method for Webpage Quality Improvement Using Improved Grey Wolf Optimization (IGWO) Based Extreme Learning Machine, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Jayanthi .B, Dr. S. Suganya (2017). A Method for Webpage Quality Improvement Using Improved Grey Wolf Optimization (IGWO) Based Extreme Learning Machine. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Jayanthi .B, Dr. S. Suganya. "A Method for Webpage Quality Improvement Using Improved Grey Wolf Optimization (IGWO) Based Extreme Learning Machine." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

In emerging websites, there is a need to measure and evaluate the websites quality and to make it better understanding. Several metrics based on the dimensions of the criteria are content quality, organization quality, design quality and user-friendly quality. These factors depend upon the word count, page size, average link text count and so on. This research investigates an Improved Grey Wolf Optimization (IGWO) and Extreme Learning Machine (ELM) is proposed to correspond with quality related items. The proposed approach IGWO is compared to verify the performance of Grey Wolf Optimization (GWO) Support Vector Machine (SVM) model, traditional Extreme Learning Machine (ELM) and Ivory based models are made to test the webpages. The performance metrics captured in IGWO based ELM model can be used to predict the website designs are good or bad. The simulation results have proven the superiority of the proposed method over the other competitive counterparts.
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