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Deep learning Performance with Random Forest HTM Cortical Learning and TenzorFlow Library


Betty Mary Jacob , Mount Zion Institute of Science and Technology; Deepthy S, Mount Zion College of Engineering; Simi Elizabeth Jacob, Mount Zion College of Engineering; Ajeesh S, Mount Zion College of Engineering; Nisha Mohan P.M, Mount Zion College of Engineering


HTM Cortical Learning, TenzorFlow Library


Merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep learning depends on the concept of minimizing the mean absolute percentage mistake which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent mistakes by using half of the value. And increase the percentage of the overlap duty cycle with 30%. Deep learning has achieved success in the field of Computer Vision, Speech and Audio Processing, and Natural Language Processing. It has the strong learning ability that can improve utilization of datasets for the feature extraction compared to traditional Machine learning Algorithm. Different is the essential building block for creating a deep Neural Different. The different model is the more general computational model. It analyzes the unsupervised data, making it a valuable tool for data analytics. A key task of this paper is to develop and analyze learning algorithm.

Other Details

Paper ID: IJSRDV8I90018
Published in: Volume : 8, Issue : 9
Publication Date: 01/12/2020
Page(s): 38-40

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