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ISSN 2063-5346
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AN ENHANCED JUNK EMAIL SPAM DETECTION USING MACHINE LEARNING BY SUPPORT VECTOR MACHINES OVER RANDOM FOREST

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C. Gnanendhra Reddy, S. Magesh Kumar
» doi: 10.31838/ecb/2023.12.sa1.450

Abstract

Aim: The main aim of the research is to enhance Junk Email Spam Detection using Machine Learning by Novel Support vector machines over Random Forest. Materials and Methods: Novel Support vector machine and Random Forest are implemented in this research work. Sample size is calculated using G power software and determined as 10 per group with pretest power 2, threshold 50 and Confidence Intervals 95%. Results: Novel Support vector machine provides a higher of 93.52 % compared to the Random Forest algorithm with 91.41 % in email spam detection. There is a significant difference between two groups with significance value of p=0.019 (p<0.05). Conclusion: Novel Support vector machine algorithm detects spam emails better than Random forest algorithm.

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