Quantifying quality of WEB sites based on content

Raghavarao, Y Venkata; Sasidhar, K; Sastry, J. K. R.; Prakash, V. Chandra

doi:10.14419/ijet.v7i2.7.10280

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Sastry JKR et al [30] assert that various factors determine a product's quality, including content, structure, navigation, multimedia, and usability. To provide new viewpoints for website evaluation, website quality is discussed from several angles [31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Related Workmentioning

confidence: 99%

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

Biyyapu,

Jammalamadaka,

Jammalamadaka

et al. 2023

Computers

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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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Section: Related Workmentioning

confidence: 99%

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

Biyyapu,

Jammalamadaka,

Jammalamadaka

et al. 2023

Computers

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“…The method is proven effective in their studies and experiments, but this method has not been used for the selection of cloud services. In addition, the author proposed in [ 26] the effective and efficient selection approach to the composition of the QoS cloud service. In this paper, the researcher adopted the cloud model for reliable services to calculate the value of the incertitude of the cloud services that are redundant.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Service Ranking Based Collaborative Filtering Model on Cloud Web Service Data

Pawar¹,

Yalla²

2021

EAI Endorsed Transactions on Collaborative Computing

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INTRODUCTION:Trust is an important indicator in the cloud computing environment for service selection and recommendation. It is a difficult task to create a composite value-added service from several candidate services for the desired objectives due to the dramatic growth in services that have similar functionalities. OBJECTIVES: This research aims to design a hybrid service feature ranking; cloud service ranking are computed using the advanced contextual service ranking measures. A hybrid collaborative approach is totally based on confidence to the QoS web service prediction. METHODS: A new service ranking similarity computation is optimized for the cloud-based service selection. This collaborative filtering measure is used to check the top k customer selection by performing the top-k customer selection estimation on the cloud service ranking RESULTS: The proposed method is useful in the prediction of QoS values and helps with optimal service ranking. As a result, similar/ relating cloud services are increasing, making it extremely complex to select the best cloud service among the relevant / similar services available CONCLUSION: The state-of the-art approaches are proposed and tested on a mathematical QoS-Aware assessment framework. The use of semantic matching technique and QoS for web service ranking satisfies user requirements for web service recommendations. In addition, users require a web service not only based on functionality, but also based on high quality.

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“…A framework has been proposed by Sastry et al, [1] that has been presented with 42 different quality factors and a structure using which the overall quality of a web site is computed. Venkata Raghva Rao et al, [2] have proposed a set of metrics using which the quality of content hosted on the WEB can be computed. Sastry e.…”

Section: Literature Surveymentioning

confidence: 99%

Structure based assessment of quality of WEB sites

Virajitha¹,

Sastry²,

Prakash³

et al. 2018

IJET

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WEB sites are playing very vital role in information dissemination. Most of the businesses are using their WEB sites to promote market and conduct business. The quality of the WEB sites has indirect relationship with quantum of business conduct by the industrial establishments. Quality of a web site is based on number of characteristics; computation of the same in quantitative terms is a complex process. Structure of a WEB site plays a vital role in hosting the content in most comprehensive manner.In this paper the subjecting of the WEB to data mining and determining the structures contained in the WEB site is presented. The structures are evaluated to find the quality of the same individually and also combined considering all the structures that are mined. A method is presented in this paper using which the quality of a web site is computed considering the structure of the WEB site alone.

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Quantifying quality of WEB sites based on content

Cited by 6 publications

References 3 publications

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

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

A Hybrid Service Ranking Based Collaborative Filtering Model on Cloud Web Service Data

Structure based assessment of quality of WEB sites

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