Open Access

ARTICLE

A Novel Cryptocurrency Prediction Method Using Optimum CNN

by Syed H. Hasan¹, Syeda Huyam Hasan², Mohammed Salih Ahmed³, Syed Hamid Hasan^4,*

1 Iowa State University, Ames, USA
2 University of Texas at Arlington, USA
3 Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
4 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

* Corresponding Author: Syed Hamid Hasan. Email:

(This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)

Computers, Materials & Continua 2022, 71(1), 1051-1063. https://doi.org/10.32604/cmc.2022.020823

Received 09 June 2021; Accepted 30 July 2021; Issue published 03 November 2021

Abstract

In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining the correlations between cryptocurrency movements and Tweet sentiments. This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction; this method is used to predict the prices of four cryptocurrencies, namely, Litecoin, Monero, Bitcoin, and Ethereum. The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%. The method is validated by comparison with existing methods using visualization tools.

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Citations