Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Building an Expert System through Machine Learning for Predicting the Quality of a WEB Site Based on Its Completion

Sastry Kodanda Rama Jammalamadaka
* ORCID logo ,
Vishnu Priya B
,
Sasi Bhanu Jammalamadaka
,
Bhupati CH
,
Kamesh Balakrishna Duvvuri
,
Raja Rao Budaraju
ORCID logo
Version 1 : Received: 31 July 2023 / Approved: 1 August 2023 / Online: 2 August 2023 (07:31:10 CEST)

A peer-reviewed article of this Preprint also exists.

Biyyapu, V.P.; Jammalamadaka, S.K.R.; Jammalamadaka, S.B.; Chokara, B.; Duvvuri, B.K.K.; Budaraju, R.R. Building an Expert System through Machine Learning for Predicting the Quality of a Website Based on Its Completion. Computers 2023, 12, 181. Biyyapu, V.P.; Jammalamadaka, S.K.R.; Jammalamadaka, S.B.; Chokara, B.; Duvvuri, B.K.K.; Budaraju, R.R. Building an Expert System through Machine Learning for Predicting the Quality of a Website Based on Its Completion. Computers 2023, 12, 181.

Abstract

The main channel for disseminating information is now the Internet. Users have different expectations for the calibre of websites regarding the posted and presented content. The WEB site's quality is influenced by up to 120 factors, each represented by two to fifteen attributes. A major challenge is quantifying the features and evaluating the quality of a website based on the feature counts. One of the aspects that determines a website's quality is its completeness, which focuses on the existence of all the objects and their connections with one another. It isn’t easy to build an expert model based on feature counts to evaluate website quality, so this paper has focused on that challenge. Both a methodology for calculating a website's quality and a parser-based approach for measuring feature counts are offered. We provide a multi-layer perceptron model that is an expert model for forecasting website quality from the "Completeness" perspective. The accuracy of the predictions is 98%, whilst the accuracy of the nearest model is 87%.

Keywords

Quality of website; Completeness of a website; Model learning to predict; Expert System; predicting the Quality of a website

Subject

Computer Science and Mathematics, Computer Science

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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