E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks  

作　　者：Sanaa A.A.Ghaleb Mumtazimah Mohamad Syed Abdullah Fadzli Waheed A.H.M.Ghanem 

机构地区：[1]Faculty of Informatics and Computing,Universiti Sultan Zainal Abidin,Kuala Terengganu,22200,Malaysia [2]Faculty of Ocean Engineering Technology and Informatics,Universiti Malaysia Terengganu,Kuala Terengganu,21030,Malaysia [3]Faculty of Engineering,University of Aden,Aden,Yemen [4]Faculty of Education(Aden-Saber),University of Aden,Aden,Yemen

出　　处：《Computers, Materials & Continua》2022年第6期4749-4766,共18页计算机、材料和连续体（英文）

摘　　要：Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

关 键 词：Grasshopper optimization algorithm multilayer perceptron artificial neural network spam detection approach 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]