Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.


Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations


Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. 2nd Edition, Prentice Hall, Upper Saddle River.

has been cited by the following article:

  • TITLE: Optimizing Feedforward Neural Networks Using Biogeography Based Optimization for E-Mail Spam Identification

    AUTHORS: Ali Rodan, Hossam Faris, Ja’far Alqatawna

    KEYWORDS: Spam, BBO, Multilayer Perceptron, Optimization, Biogeography Based Optimization

    JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.9 No.1, January 25, 2016

    ABSTRACT: Spam e-mail has a significant negative impact on individuals and organizations, and is considered as a serious waste of resources, time and efforts. Spam detection is a complex and challenging task to solve. In literature, researchers and practitioners proposed numerous approaches for automatic e-mail spam detection. Learning-based filtering is one of the important approaches used for spam detection where a filter needs to be trained to extract the knowledge that can be used to detect the spam. In this context, Artificial Neural Networks is a widely used machine learning based filter. In this paper, we propose the use of a common type of Feedforward Neural Network called Multi-Layer Perceptron (MLP) for the purpose of e-mail spam identification, where the weights of this network model are found using a new nature-inspired metaheuristic algorithm called Biogeography Based Optimization (BBO). Experiments and results based on two different spam datasets show that the developed MLP model trained by BBO gets high generalization performance compared to other optimization methods used in the literature for e-mail spam detection.