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DETECTING SPAM EMAILS USING MACHINE LEARNING AND HARRIS HAWKS’S OPTIMIZER (HHO) ALGORITHM

  • Unique Paper ID: 165120
  • Volume: 11
  • Issue: 1
  • PageNo: 371-375
  • Abstract:
  • Email spamming is an important issue in recent years. The number of spam emails increases as the number of internet users increases. Everyone is using technology for illegal and immoral acts like robbery and phishing. Spam phishing will make us mentally disturbed. As a result of it, the number of spam in our inbox will increase. It will affect our social life and make our life hell. One thing is sure we must control the spread of spam and use machine learning methods to trace the fraud spoofers. This paper discusses a new method for spam classification that combines a new metaheuristic technique called Harris Hawks Optimizer (HHO) and a machine learning technique called XG-Boost By using three sigmoid activation function and Logistic regression public database, the hybrid HHO-XG-boost models are proposed for spam classification. The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by a natural cooperative hunting phenomenon of Harris’s hawks and the HHO model that allows for an adaptive consideration of spatial and under-spatial shock search among neighbor spam-capturing datasets. From the experimental results, it is marked from figure tables that the proposed model is more accurate and attains better performance than others like noise removal. From the experimental procedures, the results are more accurate, novice, and fast than other antispam emails. From the experimental analysis, the proposed method's accuracy is very good since it reduced the spam drastically.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 1
  • PageNo: 371-375

DETECTING SPAM EMAILS USING MACHINE LEARNING AND HARRIS HAWKS’S OPTIMIZER (HHO) ALGORITHM

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Journal no 47859

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