Efficiency Analysis of Web Quality using Artificial Neural Networks

N. Manoharan1 and R. Balasubramanian2

1Department of Computer Science (P.G), SRM Arts and Science College, Kattankulathur, Chennai, Tamil Nadu.

2Faculty of Computer Applications,Erode Builder Educational Trust’s Group of Institutions, E.B.E.T. Knowledge Park, Kangeyam, Tiruppur District-638108.


Metrics are units of measurement. The term “metrics” is also frequently used to mean a set of specific measurements taken on a particular item or process. The objective of the paper is to apply the Web Metrics to Artificial Neural Networks systems. The Web metrics characterizing length using Length Metric, complexity using Complexity Metric and functionality using Functionality Metric were obtained and used to generate effort using Effort Metric to prediction models for Web authoring and design and a great impact on quality of the Website product.

As size and other impacts in the website or webpage to increases complexity also increases and hence the effort for understanding, debugging, maintenance etc also increases. Various size measures have been introduced both for procedural and Artificial Neural Network system to measure the size of the Website and WebPages. The application of ANN system has demanded the acceptance of metrics to assess and ensure quality in the Web applications. The quality of Artificial Neural Network (ANN) simulation is one of the important factors that decide the usefulness of the neural network. In this paper is made by choosing a Websites and WebPages and applying Web metrics as tool to indicate quality of the Websites and WebPages.A Artificial Neural Network tool has to develop for generation of the Website size and counts and incidence. By means of the resultant of the experiment, various Web Metrics constrains can find.

Keywords: Web metrics, Length metrics, Complexity metrics, Functionality metrics, ANN quality measurement.

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