An Enhanced Deep Learning Technique to Measure the Impact of Cryptocurrency on the World Payment system using Random Forest

Fatma M. Talaat

Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt

Emails: fatma.nada@ai.kfs.edu.eg

Abstract

Cryptocurrency is a technology that uses an encrypted peer-to-peer network to facilitate digital barter. Bitcoin, the first and most popular cryptocurrency, is paving the way as a disruptive technology to long-standing and unchanging financial payment systems. While cryptocurrencies are unlikely to replace traditional fiat currency, they have the potential to alter how Internet-connected global markets interact with one another, removing the restrictions that exist around traditional national currencies and exchange rates. Technology advances at a breakneck pace, and a technology's success is almost entirely determined by the market it tries to improve. Cryptocurrencies have the potential to change digital trade marketplaces by enabling a fee-free trading mechanism. A SWOT analysis of Bitcoin is offered, which highlights some of the recent events and movements that may have an impact on whether Bitcoin contributes to a paradigm change in economics. Cryptocurrency is a relatively new payment option, and users are naturally drawn to it because it offers privacy. To measure the impact of cryptocurrency on the world payment system, we use a Cryptocurrency extra data – Bitcoin. The proposed algorithm uses Random Forest Algorithm for prediction. The RFPA has achieved a 0.073 MSE. The RFPA has achieved the best results as it can handle huge datasets with a lot of dimensionality. It improves the model's accuracy and eliminates the problem of overfitting. When compared to other algorithms, it takes less time to train.

Keywords: Cryptocurrency; Bitcoin; Exchange Rates; Random Forest; Machine Learning