An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network
| Autor: | Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, Waheed Ali H. M. Ghanem, Sanaa Abduljabbar Ahmed Ghaleb |
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| Rok vydání: | 2021 |
| Předmět: |
Optimization algorithm
Computer science business.industry Email spam InformationSystems_INFORMATIONSYSTEMSAPPLICATIONS media_common.quotation_subject Detector Machine learning computer.software_genre Adaptability Transmission (telecommunications) Search algorithm Multilayer perceptron The Internet Artificial intelligence business computer media_common |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789813368347 ACeS |
| DOI: | 10.1007/978-981-33-6835-4_27 |
| Popis: | Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%. |
| Databáze: | OpenAIRE |
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