Spam detection using web page content

Author(s):  
Marco Túlio Ribeiro ◽  
Pedro H. Calais Guerra ◽  
Leonardo Vilela ◽  
Adriano Veloso ◽  
Dorgival Guedes ◽  
...  
Keyword(s):  
2005 ◽  
Author(s):  
Aaron W. Bangor ◽  
James T. Miller
Keyword(s):  

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.


2020 ◽  
Vol 140 (12) ◽  
pp. 1393-1401
Author(s):  
Hiroki Chinen ◽  
Hidehiro Ohki ◽  
Keiji Gyohten ◽  
Toshiya Takami

Author(s):  
Gursimran Singh ◽  
Harpreet Kaur

With the growth of website content it is become difficult to manage relations between Individual webpage and keep track of their hyperlinks within a website. This causes some Hyperlink become dead or broken. A broken Link  is a  link on a web page that no longer works. It is difficult to find out the broken link manually by checking each hyperlink individually because it is time consuming and tedious work. So to eliminate this we can use the selenium web driver tool and java code to automate testing of each hyperlink individually. The objective of this thesis is to automate finding of broken links using selenium web driver tool.


1997 ◽  
Vol 1 (3) ◽  
pp. 15-25
Author(s):  
Michael F. Hull
Keyword(s):  

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