Improving Performance for Prediction of Hungarian Disease Dataset using Deep Learning

Abstract

Artificial intelligence is to create intelligent machines that can work like human brain. Deep learning is artificial intelligence methods that work like a human brain to store records and process the data. Machine learning algorithms have capacity to learn things that are required for particular application. Hence, they have the ability to learn. There are many algorithms available that work to increase the deep learning performance. The proposed system improves the performance for prediction of Hungarian disease dataset using improved RF HTMC FR deep learning algorithm. It uses Random Forest and HTM algorithm with feature reduction. According to experimental results the proposed algorithm performs better in terms of time, accuracy and also has very less percent of mean absolute error as compared to existing algorithm HTM and RFHTM.