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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | TO DETECT THE TERRORISM ACTIIVITY |
| Authors Name: | Avhad Janhavi , Burkul Ashwini , Shelke Tejal , Sanap Renuka , Prof. Rahul M. Dhokane |
| Unique Id: | IJSDR2305115 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | In the recent times, terrorism has grown in an exponential manner in certain parts of the world. This enormous growth in terrorist activities has made it important to stop terrorism and prevent its spread before it causes damage to human life or property. With development in technology, internet has become a medium of spreading terrorism through speeches and videos. Terrorist organizations use the medium of the internet to harm and defame individuals and also promote terrorist activities through web pages that force people to join terrorist organizations and commit crimes on the behalf of those organizations. Web mining and data mining are used simultaneously for the purpose of efficient system development. Web mining even consists of many different text mining methods that can be helpful to scan and extract relevant data from unstructured data. Text mining is very helpful in detecting various patterns, keywords, and significant information in unstructured texts. Data mining and web mining systems are used for mining from text widely. Data mining algorithms are used to manage organized data sets and web mining algorithms can be helpful in mining and extracting from unstructured web pages and text data that is available across the web. Websites built in different plat- forms have varying data structures and that makes it quite difficult to read for a single algorithm. |
| Keywords: | Terrorism, Machine Learning, Encryption, Detection, SVM |
| Cite Article: | "TO DETECT THE TERRORISM ACTIIVITY", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.766 - 769, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305115.pdf |
| Downloads: | 000223171 |
| Publication Details: | Published Paper ID: IJSDR2305115 Registration ID:206348 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 766 - 769 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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