Blockchain: A Tale of Two Applications

Ferreira, Micael; Rodrigues, Sven; Reis, Catarina I.; Maximiano, Marisa

doi:10.3390/app8091506

Cited by 17 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This market is characterized by high volatility, no closed trading periods, relatively smaller capitalization, and high market data availability [ 1 ]. The financial feasibility of the cryptocurrency market in relation to other markets has been documented [ 2 , 3 , 4 ] and the algorithms upon which the cryptocurrencies operate have been validated in other fields as well [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

200

View full text Add to dashboard Cite

Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

show abstract

Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

200

View full text Add to dashboard Cite

show abstract

“…Today, block chain [72] is the main solution for multi-party sharing of files and data. We need to ensure that data ownership is always the data provider, so we also need to pay attention to the privacy protection of data fusion.…”

Section: Protection Of Data Property In Data Fusionmentioning

confidence: 99%

Information Fusion for Multi-Source Material Data: Progress and Challenges

2019

View full text Add to dashboard Cite

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

show abstract

“…A survey on systems of cryptocurrency is elaborated in [4]. The characteristics of this market, such as the high availability of market data, high volatility, smaller capitalization, and decentralized control, are studied in the literature [5]. Despite the fact that this market acts similarly to other stock markets in terms of intrinsic volatility, investor confidence has been reflected in it [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

Nayak

Das

2021

FinTech

View full text Add to dashboard Cite

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

show abstract

Blockchain: A Tale of Two Applications

Cited by 17 publications

References 10 publications

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Information Fusion for Multi-Source Material Data: Progress and Challenges

Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

Contact Info

Product

Resources

About