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Web content classification techniques for a DARK WEB SMART CRAWLER
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shirsath, Ashlesha Gore, Deipali V. |
| Copyright Year | 2017 |
| Abstract | The internet is a huge collection of billions and trillions of web pages containing varied information on or related to almost all areas of human research and development. The large volume of web and its continuously changing dynamic nature is one of the main hindrances in retrieving necessary and relevant information. A web crawler in any search engine plays a very important role in achieving a wide coverage and high efficiency. A web crawler design emphasizes on an efficient classifier, may it be site or form. A classifier categorizes a site to be relevant or irrelevant and this crawling process is launched on classified sites which depends on the extracted home page contents, thus here are the efforts of identifying best classifier which would enhance and improvise the performance of a Deep Web crawler. General TermsClassification techniques, Website classification, Crawler efficiency. KeywordsSupport Vector Machine (SVM), kNN Nearest Neighbor (kNN), Naive Bayes classifier (NB), Web Classification, Form classification, Dark Web Crawler. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijrter.com/papers/volume-2/issue-12/web-content-classification-techniques-for-a-dark-web-smart-crawler.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
