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Publication Details

Applicant:
Central University of Tamil Nadu 
Author:
V. Adarsh , A. Martin 
Corresponding Authors:
V. Adarsh 
DOI #:
Not Applicable 
Title:
A Study on Deep Learning Techniques to Improve Bitcoin Price Prediction 
Journal:
International Journal of Computer Sciences and Engineering 
Year:
2019 
Volume:
Page:
126 
Keywords:
Activation Function, Bitcoin, Deep Learning,Prediction, Recurrent Neural Networks 
Abstract:
Bitcoin is so far is been the most versatile form of the cryptocurrency we came across in recent times, and the one which is widely accepted as well. Its values are varying like anything as we can see the frequent variation in the market value. We can say that this variation is dependent on various factors which a simple linear form of an equation or the method may fail to predict. In such a condition, it is very important that we apply a more efficient way of prediction. Several methods were employed having mathematical models which didn’t give out the expected results. Deep learning methods are widely known to solve such conditions, due to which the Recurrent Neural Networks come into the picture with its ability to learn the problem with the previous literature data. It can analyze the previous value and variations in the bitcoin pricing and using it as its base of knowledge, it can make the predictions more accurate. Even more by restructuring the activation function inside the Recurrent Neural Networks, its prediction accuracy can be further improved. 
Entered by:
Martin Aruldoss on 2020-09-03 
 
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