Domain Identification and Classification of Web Pages Using Artificial Neural Network

Gupta, Shailender; Bhatia, Komal Kumar

doi:10.1007/978-3-642-36321-4_20

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…To predict the domain of the webpage, an instance of the system described in (Gupta & Bhatia, 2013a) has been used. The system is based on a Neural Network model classifier which works on a two-step framework: training the neural network (where it is given a set of web pages with known domains); testing (where the trained neural network is now used to predict the domain of a new webpage whose domain is unavailable).…”

Section: Page Classifier and Content Extractormentioning

confidence: 99%

Design of a Parallel and Scalable Crawler for the Hidden Web

Gupta¹,

Bhatia²

2021

International Journal of Information Retrieval Research

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The WWW contains huge amount of information from different areas. This information may be present virtually in the form of web pages, media, articles (research journals / magazine), blogs etc. A major portion of the information is present in web databases that can be retrieved by raising queries at the interface offered by the specific database and is thus called the Hidden Web. An important issue is to efficiently retrieve and provide access to this enormous amount of information through crawling. In this paper, we present the architecture of a parallel crawler for the Hidden Web that avoids download overlaps by following a domain-specific approach. The experimental results further show that the proposed parallel Hidden web crawler (PSHWC), not only effectively but also efficiently extracts and download the contents in the Hidden web databases

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Section: Page Classifier and Content Extractormentioning

confidence: 99%