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Web Data Segmentation for Terrorism Detection using Named Entity Recognition Technique

Authors(2) :-Pooja S. Kade, Prof. N. M. Dhande

Terrorism has grown day by day, its roots quite deep in some parts of the world. With increasing terrorist activities it has become very important to control terrorism and stop its spread before certain time period. So as identified that internet is a major source of spreading terrorism through speeches, images and videos. Terrorist organizations use internet to brain wash individuals and younger’s and also promote terrorist activities through provocative web pages that inspire helpless people and college student to join terrorist organizations. So here we propose an efficient web data mining system and segmentation technique to detect such web properties and mark them automatically for human review. Websites created in various platforms have different data structures and are difficult to read for a single algorithm so we use DOM Tree concept to extract the web data and SIFT feature for edge extraction that organized web data. Also we use Kmeans algorithm for segmentation and KNN for classification. In this way we may judge web pages and check if they may be promoting terrorism or not. This system proves useful in anti-terrorism sectors and even search engines to classify web pages into the different category.
Pooja S. Kade, Prof. N. M. Dhande
Data Mining, Web Mining, Patterns, DOMTree Technique, Object Recognition, Segmentation
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Publication Details
  Published in : Volume 3 | Issue 3 | March-April 2017
  Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 217-222
Manuscript Number : IJSRST173181
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Pooja S. Kade, Prof. N. M. Dhande, "Web Data Segmentation for Terrorism Detection using Named Entity Recognition Technique", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 3, pp.217-222 , March-April-2017.
Journal URL : http://ijsrst.com/IJSRST173181

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