Open Access
ITM Web Conf.
Volume 42, 2022
1st International Conference on Applied Computing & Smart Cities (ICACS21)
Article Number 01001
Number of page(s) 9
Published online 24 February 2022
  1. T. Verma, N. S. J. I. J. o. I. T. Gill, and E. Engineering, “Email Spams via Text Mining using Machine Learning Techniques, ” 19, no. 4, pp. 2535-2539, (2020). [Google Scholar]
  2. N. Saidani, K. Adi, M. S. J. C. Allili, and Security, “A semantic-based classification approach for an enhanced spam detection, ” 94, p. 101716, (2020). [Google Scholar]
  3. H. Taylor, “Making Mass-Spamming Illegal Rises, ” Harris Interactive (2011). [Google Scholar]
  4. P. Heymann, et al., “Fighting spam on social web sites: A survey of approaches and future challenges, ” Internet Computing, IEEE, 11, pp. 36-45, (2007). [CrossRef] [Google Scholar]
  5. Clearbridge. What is the global cost of spam? Available: [Google Scholar]
  6. M. Fossi, et al., “Symantec global internet security threat report, ” White Paper, Symantec Enterprise Security, 1, (2013). [Google Scholar]
  7. X. Guo and Z. Xia, “Fighting spam, ” University of California Berkeley, (2012). [Google Scholar]
  8. S. Youn and D. McLeod, “A comparative study for email classification, ” in Advances and Innovations in Systems, Computing Sciences and Software Engineering, ed: Springer, pp. 387-391 (2007). [CrossRef] [Google Scholar]
  9. I. Firdausi, et al., “Analysis of Machine learning Techniques Used in BehaviorBased Malware Detection, ” in Advances in Computing, Control and Telecommunication Technologies (ACT), 2010 Second International Conference on, pp. 201-203, (2010). [Google Scholar]
  10. S. T. Maller, “Email filtering methods and systems, ” ed: Google Patents, (2006). [Google Scholar]
  11. D. Cook, et al., “Catching spam before it arrives: domain specific dynamic blacklists, ” in Proceedings of the 2006 Australasian workshops on Grid computing and e-research 54, pp. 193-202, (2006). [Google Scholar]
  12. P. Warkhede, et al., “Fast packet classification for two-dimensional conflict-free filters, ” in INFOCOM. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001, pp. 1434-1443 (2001). [Google Scholar]
  13. P. O. Boykin and V. Roychowdhury, “Personal email networks: An effective antispam tool, ” arXiv preprint cond-mat/0402143, (2004). [Google Scholar]
  14. A. W. Moore and D. Zuev, “Internet text classification using bayesian analysis techniques, ” in ACM SIGMETRICS Performance Evaluation Review, pp. 50-60, (2010). [Google Scholar]
  15. N. J. Kawale and S. Y. Sait, “A Review on Various Techniques for Spam Detection, ” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 1771-1775: IEEE, (2021). [Google Scholar]
  16. O. El Kouari, H. Benaboud, and S. Lazaar, “Using machine learning to deal with Phishing and Spam Detection: An overview, ” in Proceedings of the 3rd International Conference on Networking, Information Systems & Security, pp. 1-7, (2020). [Google Scholar]
  17. M. Sahami, et al., “A Bayesian approach to filtering junk e-mail, ” in Learning for Text Categorization: Papers from the 2008 workshop, pp. 98-105, (2008). [Google Scholar]
  18. B. Cui, et al., “On effective e-mail classification via neural networks, ” in Database and Expert Systems Applications, pp. 85-94, (2005). [Google Scholar]
  19. G. Leroy and T. C. Rindflesch, “Using symbolic knowledge in the UMLS to disambiguate words in small datasets with a naive Bayes classifier, ” Medinfo, 11, pp. 381-385, (2004). [Google Scholar]
  20. E. Byvatov, et al., “Comparison of support vector machine and artificial neural network systems for drug/nondrug classification, ” Journal of Chemical Information and Computer Sciences, 43, pp. 1882-1889, (2009). [Google Scholar]
  21. A. Lorenz, et al., “Comparison of different neuro-fuzzy classification systems for the detection of prostate cancer in ultrasonic images, ” in Ultrasonics Symposium, 2005. Proceedings., 2005 IEEE, pp. 1201-1204 (2005). [Google Scholar]
  22. N. Widiastuti, “Convolution neural network for text mining and natural language processing, ” in IOP Conference Series: Materials Science and Engineering, 662, no. 5, p. 052010: IOP Publishing, (2019). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.