References
- Gaurav, D., S. Tiwari, A. Goyal, N. Gandhi, A. Abraham. Machine Intelligence-Based Algorithms for Spam Filtering on Document Labeling. – Soft Comput., Vol. 24, 2020, pp. 9625-9638.
- Dedeturk, B., B. Akay. Spam Filtering Using a Logistic Regression Model Trained by an Artificial Bee Colony Algorithm. – Applied Soft Computing, Vol. 91, 2020, 106229. ISSN 1568-4946.
- Pelletier, L., J. Almhana, V. Choulakian. Adaptive Filtering of SPAM. – White Paper, University of Moncton E1A 3E9, 2018. www.umoncton.ca/greti/papers/Adaptive Filtering of Spam.pdf
- Mirjalili, S., S. M. Mirjalili, A. Lewis. Grey Wolf Optimizer. – Advances in Engineering Software, Vol. 69, 2014, pp. 46-61.
- Dada, E. G., J. S. Bassi, H. Chiroma, S. M. Abdulhamid, A. O. Adetunmbi, O. EAjibuwa. Machine Learning for Email Spam Filtering: Review, Approaches and Open Research Problems. – Heliyon, Vol. 5, 2019, Issue 6, e01802. ISSN 2405-8440.
- Rajamohana, S., K. Umamaheswari, B. Abirami. Adaptive Binary Flower Pollination Algorithm for Feature Selection in Review Spam Detection. – In: Proc. of IEEE International Conference on Innovations in Green Energy and Healthcare Technologies, 2017, pp. 1-4.
- Tyagi, A. Content-Based Spam Classification – A Deep Learning Approach. A Thesis Submitted to the Faculty of Graduate Studies. University of Calgary, Alberta, Canada, 2016.
- Zavvar, M., M. Rezaei, S. Garavand. E-mail Spam Detection Using Combination of Particle Swarm Optimization and Artificial Neural Network and Support Vector Machine. – Int. J. Mod. Educ. Comput. Sci., 2016, pp. 68-74.
- Awad, M., M. Foqaha. E-mail Spam Classification Using Hybrid Approach of RBF Neural Network and Particle Swarm Optimization. – Int. J. Netw. Secur. Appl., Vol. 8, 2016, No 4.
- Alkaht, I., B. Al-Khatib. Filtering SPAM Using Several Stages Neural Networks. – Int. Rev. Comp. Software, Vol. 11, 2016, No 2.
- Sharma, A., A. Suryawansi. A Novel Method for Detecting Spam E-mail Using KNN Classification with Spearman Correlation as Distance Measure. – Int. J. Comput. Appl., Vol. 136, 2016, No 6, pp. 28-34.
- Palanisamy, C., T. Kumaresan, S. E. Varalakshmi. Combined Techniques for Detecting E-mail Spam Using Negative Selection and Particle Swarm Optimization. – Int. J. Adv. Res. Trends Eng. Technol., 2016. ISSN: 2394-3777.
- Karthika, D., P. Visalakshi, T. Sankar. Improving E-mail Spam Classification Using Ant Colony Optimization Algorithm. – Int. J. Comput. Appl., 2015, No ICICT, pp. 975-8887.
- Goetschi, R. SPAM-Filtering Using Artificial Neural Networks. Semester Thesis, Berne University of Applied Sciences, July 2004. www.hta-bi.bfh.ch/~goetr/nn_spam_goetschi2004.pdf
- Smita, M., S. Kumar. Survey on Types of Bug Reports and General Classification Techniques in Data Mining. – International Journal of Computer Science and Information Technologies, Vol. 6, 2015, No 4, pp. 1578-1583.
- UCI Machine Learning Repository Spambase Dataset. The University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml/datasets
- Idris, I., et. al. A Combined Negative Selection Algorithm-Particle Swarm Optimization for an E-mail Spam Detection System. – Eng. Appl. Artificial Intelligence, Vol. 39, 2015, pp. 33-44.
- Lamiaa, M., E. Bakrawy. Grey Wolf Optimization and Naive Bayes Classifier Incorporation for Heart Disease Diagnosis. – Aust. J. Basic & Appl. Sci., Vol. 11, 2017, No 7, pp. 64-70.
- Uysal, A. K., S. Gunal. A Novel Probabilistic Feature Selection Method for Text Classification. Knowledge-Based Syst., Vol. 36, 2012, pp. 226-235.