1. W Awad,S Elseuofi (2011). Machine Learning methods for E-mail Classification. International Journal of Computer Applications, 16(1)
2. O Saad,A Darwish,R Faraj (2012). PERFORMANCE OF MACHINE LEARNING TECHNIQUES FOR EMAIL SPAM FILTERING. International Journal of Recent Trends in Engineering and Research, 12(2), 245-248.
3. Yingying Chen,Sourabh Jain,Vijay Adhikari,Zhi-Li Zhang,Kuai Xu (2011). A first look at inter-data center traffic characteristics via Yahoo! datasets. 2011 Proceedings IEEE INFOCOM, 1620-1628.
4. Kari Barlow,Jenny Lane (2007). Like technology from an advanced alien culture. Proceedings of the 35th annual ACM SIGUCCS fall conference, 8-10.
5. Danyel Fisher,A Brush,Eric Gleave,Marc Smith (2006). Revisiting Whittaker & Sidner's "email overload" ten years later. Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, 309-312.
6. Enrico Blanzieri,Anton Bryl (2008). A survey of learning-based techniques of email spam filtering. Artificial Intelligence Review, 29(1), 63-92.
7. P Cunningham,N Nowlan,S Delany,M Haahr (2003). A case-based approach to spam filtering that can track concept drift. The ICCBR, 3, 3-2003.
8. H Drucker,Donghui Wu,V Vapnik (1999). Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5), 1048-1054.
9. M Sahami,S Dumais,D Heckerman,E Horvitz (1998). A Bayesian approach to filtering junk e-mail. Learning for Text Categorization: Papers from the 1998 workshop, 62, 98-105.
10. Qiang Wang,Yi Guan,Xiaolong Wang (2006). SVM-Based Spam Filter with Active and Online Learning. TREC
11. S Mohammed,O Mohammed,J Fiaidhi,S Fong,T Kim (2013). Classifying Unsolicited Bulk Email (UBE) using Python Machine Learning Techniques. Classifying Unsolicited Bulk Email (UBE) using Python Machine Learning Techniques
12. A Harisinghaney,A Dixit,S Gupta,A Arora (2014). Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm. Optimization, Reliabilty, and Information Technology (ICROIT), 153-155.
13. M Scholar (2010). Supervised learning approach for spam classification analysis using data mining tools. organization, 2(8), 2760-2766.
14. V Christina,S Karpagavalli,G Suganya (2010). A Study on Email Spam Filtering Techniques. International Journal of Computer Applications, 12(1), 7-9.
15. V Metsis,I Androutsopoulos,G Paliouras (2006). Spam filtering with naive bayes-which naive bayes?. CEAS, 17, 28-69.
16. Ion Androutsopoulos,John Koutsias,Konstantinos Chandrinos,Constantine Spyropoulos (2000). An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 160-167.
17. J Hovold (2005). Naive Bayes Spam Filtering Using Word-Position-Based Attributes. CEAS, 41-48.
18. José Hidalgo (2002). Evaluating cost-sensitive Unsolicited Bulk Email categorization. Proceedings of the 2002 ACM symposium on Applied computing, 615-620.
19. I Androutsopoulos,G Paliouras,V Karkaletsis,G Sakkis,C Spyropoulos,P Stamatopoulos (2000). Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach. Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach
20. Tom Fawcett (2003). "In vivo" spam filtering. ACM SIGKDD Explorations Newsletter, 5(2), 140-148.
21. C Wu,K Cheng,Q Zhu,Y Wu (2005). Using visual features for anti-spam filtering. ICIP 2005. IEEE International Conference on, 3, 509.
22. G Cormack,J Gómez Hidalgo,E Sánz (2007). Spam filtering for short messages. Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 313-320.
23. T Subramaniam,H Jalab,A Taqa (2010). Overview of textual anti-spam filtering techniques. International Journal of Physical Sciences, 5(12), 1869-1882.
24. S Sharma,A Arora (2013). Adaptive approach for spam detection. International Journal of Computer Science Issues, 10(4), 23-26.
25. M Banday,T Jan (2009). Effectiveness and limitations of statistical spam filters. Effectiveness and limitations of statistical spam filters
26. P Chhabra,R Wadhvani,S Shukla (2010). Spam filtering using support vector machine. Special Issue IJCCT, 1(2), 3.
27. K Tretyakov (2004). Machine learning techniques in spam filtering. Data Mining Problem-oriented Seminar, 3, 60-79.
28. T Shahi,A Yadav (2013). Mobile SMS spam filtering for Nepali text using naïve bayesian and support vector machine. International Journal of Intelligence Science, 4(01), 24.
29. H Drucker,Donghui Wu,V Vapnik (1999). Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5), 1048-1054.
30. G Suganya,M Arulprakash,V Dhayalan,V Sridevi,M Kavitha,C Srinivasan (2025). Sustainable Solid Waste Segregation Through IoT and Linear Regression. 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), 883-888.
31. U Rahane,A Lande,O Bavikar,S Chavan,K Shedge International Journal of Engineering Sciences & Research Technology Advanced Filtering System to Protect OSN user Wall From Unwanted Messages. International Journal of Engineering Sciences & Research Technology Advanced Filtering System to Protect OSN user Wall From Unwanted Messages
32. J Moody,C Darken (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294.
33. Megha Rathi,Vikas Pareek (2013). Spam Mail Detection through Data Mining – A Comparative Performance Analysis. International Journal of Modern Education and Computer Science, 5(12), 31-39.
34. P Graham (2002). A plan for spam. A plan for spam
35. N Kang,C Domeniconi,D Barbará (2005). Categorization and keyword identification of unlabeled documents. Data Mining, Fifth IEEE International Conference on, 4.
36. V Singh,S Bhardwaj (2018). Spam Mail Detection Using Classification Techniques and Global Training Set. Intelligent Computing and Information and Communication, 623-632.
37. M Shafi'i Muhammad Abdulhamid,O Osho,I Ismaila,J Alhassan (2018). Comparative Analysis of Classification Algorithms for Email Spam Detection. Comparative Analysis of Classification Algorithms for Email Spam Detection
38. U Sah,N Parmar (2017). An approach for Malicious Spam Detection in Email with comparison of different classifiers. An approach for Malicious Spam Detection in Email with comparison of different classifiers
39. T Verma (2017). E-Mail Spam Detection and Classification Using SVM and Feature Extraction. E-Mail Spam Detection and Classification Using SVM and Feature Extraction
40. Nurul Rusland,Norfaradilla Wahid,Shahreen Kasim,Hanayanti Hafit (2017). Analysis of Naïve Bayes Algorithm for Email Spam Filtering across Multiple Datasets. IOP Conference Series: Materials Science and Engineering, 226(1), 012091.
41. A Yüksel,S Cankaya,İ Üncü (2017). Design of a Machine Learning Based Predictive Analytics System for Spam Problem. Acta Physica Polonica, A, 132(3)
42. Neelam Choudhary,Ankit Jain (2017). Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique. Communications in Computer and Information Science, 18-30.
43. Dave Debarr,Harry Wechsler (2009). Spam detection using Random Boost. Pattern Recognition Letters, 33(10), 1237-1244.
44. Thiago Guzella,Walmir Caminhas (2009). A review of machine learning approaches to Spam filtering. Expert Systems with Applications, 36(7), 10206-10222.
45. D Mavroeidis,K Chaidos,S Pirillos,D Christopoulos,M Vazirgiannis (2006). Using tri-training and support vector machines for addressing the ECML/PKDD 2006 discovery challenge. Proceedings of ECMLPKDD 2006 Discovery Challenge Workshop, 39-47.
46. Bryan Klimt,Yiming Yang (2004). The Enron Corpus: A New Dataset for Email Classification Research. Lecture Notes in Computer Science, 217-226.