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

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers Pub Date : 2023-09-11 DOI:10.3390/computers12090181

Vishnu Priya Biyyapu, Sastry Kodanda Rama Jammalamadaka, Sasi Bhanu Jammalamadaka, Bhupati Chokara, Bala Krishna Kamesh Duvvuri, Raja Rao Budaraju

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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 website’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 is not 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%.

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基于完成度的网站质量预测的机器学习专家系统构建

现在传播信息的主要渠道是互联网。用户对网站发布和呈现内容的质量有不同的期望。网站的质量受到多达120个因素的影响，每个因素由2到15个属性表示。一个主要的挑战是量化特征，并根据特征数量评估网站的质量。决定网站质量的一个方面是它的完整性，它关注的是所有对象的存在以及它们彼此之间的联系。建立一个基于特征数的专家模型来评估网站质量是一件不容易的事情，因此本文重点研究了这一挑战。提供了一种计算网站质量的方法和一种基于解析器的测量功能计数的方法。我们提供了一个多层感知器模型，是一个从“完整性”角度预测网站质量的专家模型。预测的准确率为98%，而最接近的模型的准确率为87%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

5.40

自引率

3.60%

发文量

153

审稿时长

11 weeks