Presenting a novel method to improve multi-layered perceptron artificial neural networks based on combination with frog leaping algorithm to detect spam emails

With the advancement of technology and the significant use of the Internet by
individuals and groups, advertisements in this field have expanded explosively,
so that all kinds of methods for mass distribution of these advertisements to
Internet users in the form of e-mail have been encountered. And this causes
problems for Internet users. One of these problems is the presence of spam
emails which Spam email is one of the most common negative features suffering
the owner of an email address. Although existing technologies cannot eliminate
unwanted spams, some existing methods can reduce their number. By definition,
spam is an electronic version of "useless mails." Spam refers to unwanted and
unsolicited e-mails. Such e-mails are not necessarily directly related to the virus,
meaning that messages sent from valid sources may also be included in this
class. This study proposes a new method based on improving the multilayer
perceptron artificial neural network using the shuffled frog leaping algorithm to
realize this objective. The shuffled frog leaping algorithm is used to find the best
features, and the artificial multilayer perceptron neural network is used to detect
spam emails based on the best features. The simulation results prove that the
shuffled frog leaping algorithm has significantly improved the multilayer
perceptron artificial neural network compared to the artificial neural network
based on the radial base function