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Reseach Article

Accuracy Analysis of Neural Networks in removal of unsolicited e-mails

by P.Mohan Kumar, P.Kumaresan, S.Yokesh Babu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 16 - Number 3
Year of Publication: 2011
Authors: P.Mohan Kumar, P.Kumaresan, S.Yokesh Babu
10.5120/1990-2682

P.Mohan Kumar, P.Kumaresan, S.Yokesh Babu . Accuracy Analysis of Neural Networks in removal of unsolicited e-mails. International Journal of Computer Applications. 16, 3 ( February 2011), 39-44. DOI=10.5120/1990-2682

@article{ 10.5120/1990-2682,
author = { P.Mohan Kumar, P.Kumaresan, S.Yokesh Babu },
title = { Accuracy Analysis of Neural Networks in removal of unsolicited e-mails },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 16 },
number = { 3 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume16/number3/1990-2682/ },
doi = { 10.5120/1990-2682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:03:54.901546+05:30
%A P.Mohan Kumar
%A P.Kumaresan
%A S.Yokesh Babu
%T Accuracy Analysis of Neural Networks in removal of unsolicited e-mails
%J International Journal of Computer Applications
%@ 0975-8887
%V 16
%N 3
%P 39-44
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today communication has been revolutionized with email and other online communication systems. However, some computer users have abused the technology used to drive these communications, by sending out thousands and thousands of spam emails with little or no purpose other than to increase traffic or decrease bandwidth. With the electronic mail emerging as the primary means of communication, sorting of electronic mails is of prime importance. Most current sorting techniques are rule based, in which the user is supposed to give a set of rules, according to which mails are sorted. But configuring these rules is a tedious and often impossible task due to the variety of emails. In this paper a technique using neural network is deployed which automatically removes unwanted incoming mails, without the need for constant user intervention as well as its accuracy is analyzed in parallel.

References
  1. Sivanadyan, Thiagarajan, Detecting Spam emails using neural networks, www.cae.wisc.edu/~ece539/project/f03/sivanandyan.pdf, June-July 1999
  2. D. Puniškis, R. Laurutis, R. Dirmeikis, An Artificial Neural Nets for Spam e-mail Recognition, Kaunas University of Technology, 2006
  3. Yue Yang and Sherif Elfayoumy, Anti-Spam Filtering Using Neural Networks and Baysian Classifiers, www.student.cse.buffalo.edu/~pejusdas/document/p2report.pdf
  4. Ponnapalli A formal selection and pruning algorithm for feed forward ANN optimization.IEEE Conference transaction on NN 1999 vol 10.
  5. Amit Ramesh and M.J.Matrix.Learning moment sequences from demonstration IEEE proceedings ICDL-2000 MIT.
  6. F.L Chung and T.LEE network growth approach to design feed forward NN.IEE
  7. Md.Yosuf, javeria iqubal Punjab University Pakistan. Hash table based feed forward NN 2009 International conference on emerging trends.
  8. M.Shami. S.Dumars 1998 A Bayesian Network approach for filtering Junk e-mail. In learning for text categorization papers from AAAI workshop.
  9. M.Shami Application of machine learning to information access In AAAI 1997-Proceedings of 14th national conference on AI.
  10. .M.Gori and A.Tesi on the problem of local minima in back probagation .IEEE Transaction pattern analysis and machine learning VOL 14 Jan-1992.
  11. M.Bianchini,P.Frasconi and M.Gori Learning in Multilayered NN used as auto associaters. IEEE transactions on NN Vol 6 March 1995.
  12. H.Dundar and k rose .the effect of quantization on multilayr NN.IEEE transaction on NN Nov-1995
Index Terms

Computer Science
Information Sciences

Keywords

unsolicited neural network accuracy