P. A. Omosebi, A. P. Adewole, Oladipupo A. Sennaike
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Web Page Prediction Model using Machine Learning Approaches: A Review
Webpage classification and prefetching are essential techniques to reduce internet access latency. However, insufficient server resources and network bandwidth can lead to inefficient usage when the majority of the prefetched web pages are not visited during subsequent visits. To address this issue, accurate prediction during prefetching is crucial. Existing research has shown that usage data and user navigation can be tracked using methods like Markov models to predict the most popular web paths, leading to more accurate classification and prefetching of web pages. However, accurately classifying web pages remains a challenging task due to the uncontrolled nature of web content, and the need to classify hypertext that is linked through HTML tags and URLs. This study presents several methods to improve the accuracy of machine learning algorithms for classifying web pages. By combining different results, we show how our approach can enable increase in the performance of classification of web page and prefetching. Our findings can lead to the development of more efficient and accurate webpage classification and prefetching algorithms, resulting in reduced internet access latency, which is of great relevance to internet users and providers alike.