Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC

Saima Haseeb, Mahak Motwani, Amit Saxena
TIEIT, Bhopal
International Journal of Computer Applications, Volume 74, No.17, 2013

   author={Saima Haseeb and Mahak Motwani and Amit Saxena},

   title={Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC},

   journal={International Journal of Computer Applications},






   note={Published by Foundation of Computer Science, New York, USA}


Download Download (PDF)   View View   Source Source   
With the rapid growth of Internet, E-mail, with its convenient and efficient characteristics, has become an important means of communication in people’s life. It reduces the cost of communication. It comes with Spam. Spam emails, also known as "junk e-mails", are unsolicited one’s sent in bulk with hidden or forged identity of the sender, address, and header information. It is vital to pursue more effective spam filtering approaches to maintain normal operations of e-mail systems and to protect the interests of email users. In this paper we developed a Spam filter based on Bayesian filtering method using Aho-corasick and PFAC string matching algorithm. This filter developed an improved version of spam filter based on traditional Bayesian spam filtering to improve spam filtering efficiency, and to reduce chances of misjudgement of malignant spam. For further improvement of Spam filtering process we are transform the filter in to parallel spam filter on GPGPU’s by using PFAC Algorithm.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

You must be logged in to post a comment.

* * *

* * *

* * *

Free GPU computing nodes at

Registered users can now run their OpenCL application at We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to will be treated according to our Privacy Policy

HGPU group © 2010-2014

All rights belong to the respective authors

Contact us: