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Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning
by
Chen, Si
in
Algorithms
/ Artificial neural networks
/ Brain
/ Crypto-currencies
/ Currency
/ Data mining
/ Deep learning
/ Digital currencies
/ Economics
/ Electronic commerce
/ Electronic devices
/ Feature extraction
/ Financial risk
/ Foreign exchange rates
/ Fraud
/ Machine learning
/ Neural networks
/ Risk analysis
/ Risk assessment
/ Robbery
/ Theft
2022
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Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning
by
Chen, Si
in
Algorithms
/ Artificial neural networks
/ Brain
/ Crypto-currencies
/ Currency
/ Data mining
/ Deep learning
/ Digital currencies
/ Economics
/ Electronic commerce
/ Electronic devices
/ Feature extraction
/ Financial risk
/ Foreign exchange rates
/ Fraud
/ Machine learning
/ Neural networks
/ Risk analysis
/ Risk assessment
/ Robbery
/ Theft
2022
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Do you wish to request the book?
Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning
by
Chen, Si
in
Algorithms
/ Artificial neural networks
/ Brain
/ Crypto-currencies
/ Currency
/ Data mining
/ Deep learning
/ Digital currencies
/ Economics
/ Electronic commerce
/ Electronic devices
/ Feature extraction
/ Financial risk
/ Foreign exchange rates
/ Fraud
/ Machine learning
/ Neural networks
/ Risk analysis
/ Risk assessment
/ Robbery
/ Theft
2022
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Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning
Journal Article
Cryptocurrency Financial Risk Analysis Based on Deep Machine Learning
2022
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Overview
Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy (53.4%) and correct prediction (MSE 1.02) and offers the prospect of other research in this area.
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