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.