The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions
Abstract
Abstract A cryptocurrency is a network-based digital exchange medium, where the records are secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). It uses blockchain technology to make the transactions secure, transparent, traceable, and immutable. Due to these properties, the cryptocurrencies have gained popularity in almost all the sectors especially in financial sectors. Though, cryptocurrencies are getting recognition form the approval bodies, but still, the uncertainty and dynamism in their prices risk the investments substantially. Cryptocurrency price prediction has become a trending research topic globally. Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Neural Networks (NN), and Long short-term memory (LSTM) have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. In this paper, a LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero. The results depict that the proposed scheme accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.
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Cites
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Learning long-term dependencies with gradient descent is difficult
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A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting
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Self-citationA Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks
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Self-citationPeng, Y., Albuquerque, P. H. M., Camboim de Sá, J. M., Padula, A. J. A., & Montenegro, M. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.12.004 · Cited by 257
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Self-citationPrediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system
M., P., Sharma, A., V., V., Bhardwaj, V., Sharma, A. P., Iqbal, R., & Kumar, R. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106527 · Cited by 246
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Machine Learning Models for Secure Data Analytics: A taxonomy and threat model
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Cited by
Hamayel, M. J., & Owda, A. Y. (2021) · AI · doi:10.3390/ai2040030 · Cited by 294
Metadata
- Authors
- Patel, M. M., Tanwar, S., Gupta, R., & Kumar, N.
- Venue
- Journal of Information Security and Applications
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 55
- Pages
- 102583
- ISSN
- 2214-2126 (ISSN-L)
- OpenAlex
- W3081572486
- Cited by
- 282
- References
- 36
- FWCI
- 24.86
- Type
- journal article
- Language
- en
- Open access
- closed
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