By Topic

Statistical analysis learning approach: The use of artificial intelligence in network data visualization system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wong, D.H.-T. ; Nat. Adv. IPv6 Centre (NAv6), Univ. Sains Malaysia, Minden, Malaysia ; Kok-Soon Chai

Many of the network data visualization tools or applications are designed and being applied in network data visualization system which are particularly for users with advanced network knowledge even though the tools are indispensable by diverse computer users. In this paper, we proposed and presented an adaptive statistical analysis learning approach that is able to adapt to the user feedback after viewing the network data visualization, preserving its capabilities of intelligently adjusting the network data visualization screens to diverse levels of computer users. The adaptive system is designed to response in conjunction with the feedback from initial testing which comprises of 200 computer users feedback. We implemented a rule-based decision scenario with automatic feedback to verify that our statistical analysis learning approach can improve the conventional network data visualization system and the display of screens. This short paper proposed a preliminary statistical analysis learning algorithm which is one of the main approaches in network data visualization research.

Published in:

Computer Applications and Industrial Electronics (ICCAIE), 2010 International Conference on

Date of Conference:

5-8 Dec. 2010