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@article{192556,
        author = {Dr.Neelam Ruhil and Dr.Rajesh Kumar},
        title = {Using Cloudflare Model Approach to Forecast Web Page Caching},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1871-1876},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192556},
        abstract = {The World Wide Web is a large repository of information. There are lot of users who regularly accesses this information source, this is simple to invent certain patterns to access resources on web. Web assumption has been implemented in the past for static content. With the increasing Internet traffic and Web content, the Web assumption models are very famous. Data mining methodology categorizes the modules of clients based on their attributes and assumes future activity without allowing instant inferences and interactivity. Here some practices like information retrieval and assumption by partial matching can be used in combination with prediction modelling to increase validity and performance. There is always some scope to improve the web page access based on user requirement. One of the methods given by web is page pre-fetching which means to make available the web page to the user before the user request. In this current work we are presenting an intellectual method created on the history of web page visit used for web page prediction. A three-level approach is proposed in which we Cloudflare model is combined with AWS approach and association mining.},
        keywords = {},
        month = {February},
        }