An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
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...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.unisza.edu.my/4749/1/FH03-FIK-21-51444.pdf http://eprints.unisza.edu.my/4749/ |
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| 要約: | 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%. |
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