Spam Detection on Emails Using Convolutional Neural Network Classifier with K nearest Neighbor Classifier

Abstract

Aim: The main aim of the research is to detect the spam emails using Convolutional Neural Network over KNN Algorithm and KNN algorithm belongs to Novel Cluster Based Method. Materials and Methods: Convolutional Neural Network and KNN are implemented in this research work. Sample size of n=20 is calculated using G power software. G power value is between 0.59 and 0.9  and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Result: CNN algorithm  provides a higher of 91.18% compared to KNN algorithm with 87.05% to classifise. There is a significant difference between two groups with a significance value of 0.003 (p<0.05). Conclusion: These results show that the performance of the Convolutional Neural Network algorithm (91.18%) detects spam emails  better than KNN(87.05%) algorithm in terms of accuracy.