Detecting Spam Email with Machine Learning Optimized with Harris Hawks’s optimizer (HHO) Algorithm

Detecting Spam Email with Machine Learning Optimized with Harris Hawks’s optimizer (HHO) Algorithm

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Product Code: Python - Machine Learning
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Product Description

Aim:

         This study aims to improve the accuracy of spam email detection by leveraging a hybrid approach employing the Harris Hawks Optimizer (HHO) in conjunction with the powerful XGBoost algorithm for feature selection in machine learning.


Abstract:

          The persistent and evolving threat of spam emails in today's digital ecosystem necessitates advanced and adaptive mechanisms for their detection and filtration. In this pursuit, this research presents an innovative framework aimed at significantly augmenting the precision and efficacy of identifying spam emails. Leveraging machine learning paradigms, the study introduces a hybridized approach that integrates the Harris Hawks Optimizer (HHO) with the robust XGBoost algorithm, specifically targeting feature selection. The primary aim is to substantially elevate the accuracy and computational efficiency in the differentiation between spam and genuine emails.

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Proposed Method:

         This research proposes an enhancement by replacing KNN with XGBoost, a more powerful and flexible algorithm. The HHO algorithm is employed for feature selection to optimize the feature subset for XGBoost, improving the classification accuracy and computational efficiency.


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  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
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  7. Source Code
  8. Screen Shots
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  10. Reference Papers
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  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

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  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.