Volume 11 - Volume 11
Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network
Abstract
Aim: The main aim of the study is to predict metro water fraud accurately by Recurrent Neural
Network Algorithms and compare the prediction accuracy with Convolutional Neural Network.
Materials and Methods: In the existing system Convolutional Neural Network algorithm is used and
in the proposed system Recurrent Neural Network algorithm is used. CNN with sample size =20 and
RNN with sample size =20 was iterated forty times for predicting the accuracy. The algorithms have
been implemented and tested over a dataset which consists of 8002 records. Result: After performing
the experiment we get mean accuracy of 94.5210 by using Recurrent Neural Network algorithm and
we get accuracy of 93.4950 by using Convolutional Neural Network algorithm for metro water
fraudulent prediction. There is a statistical significant difference in accuracy for two algorithms is
p<0.05 by performing independent samples t-tests. Conclusion: The comparison results show that
the Recurrent Neural Network algorithm appears to be better performance than Convolutional
Neural Network algorithms.
Paper Details
PaperID: 2177
Author's Name: D. Sreekanth and Dr.K. Thinakaran
Volume: Volume 11
Issues: Volume 11
Keywords: Metro Water, Convolutional Neural Network Algorithm, Recurrent Neural Network Algorithm, Machine Learning, Statistical Analysis, Novel Detection.
Year: 2021
Month: June
Pages: 1177-1187