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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM20518

Intelligent Crypto Currency Price Predictor

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM20518

Volume:

7 |

5 |

May 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MK

M.S. Khatib

FC

Farhan Chavhan

AS

Abdul Shakeeb

TH

Tohid Hanfi

JA

Jay Avchar

TB

Tanmay Belkhede

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Abstract

The digital currency in which encryption techniques are used to regulate the generation of units of currency is said to be called cryptocurrency. The technology used here is used to explores the next day change in the price of cryptocurrency. It is a challenge for a common person to achieve with varying degrees of success. But this is achieved through the implementation of a optimized recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. This paper presents a deep learning approach for predicting the prices of cryptocurrencies. The proposed method uses a combination of Long Short-Term Memory (LSTM) neural networks and Recurrent Neural Networks (RNN) to analyze historical data and make predictions. The model was trained and tested using data from the Bitcoin market. The results showed that the proposed method outperformed other state-of-the-art models in terms of accuracy and robustness.

KEYWORDS

Crypto currency LSTM (Long-Short-Term-Memory) RNN (Recurrent-Neural-Network).

Cite This Article

M.S. Khatib, Farhan Chavhan, Abdul Shakeeb, Tohid Hanfi, Jay Avchar, Tanmay Belkhede, "Intelligent Crypto Currency Price Predictor," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023. https://doi.org/10.55041/IJSREM20518

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Publication Date

7/4/2023

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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