Machine learning in/for blockchain: Future and challenges

Chen, Fang; Wan, Hong; Cai, Hua; Cheng, Guang

doi:10.1002/cjs.11623

Cited by 39 publications

(37 citation statements)

References 56 publications

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“…One is the theoretical research on blockchain crowdfunding, bionic algorithms, cryptocurrency, consensus mechanism and the Internet of Things [32][33][34]. The second is the role of blockchain in equity registration, letter of credit and international exchange; applied research in the field of cross-border payment; and the construction of basic platform based on blockchain [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

et al. 2021

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Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

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Section: Discussionmentioning

confidence: 99%

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

et al. 2021

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“…In Ref. [75], the application of machine learning in blockchains is summarized, which has great application prospects in illegal behavior detection and cryptocurrency price prediction, among others. For example, using machine learning methods to collect addresses leaked or voluntarily displayed by users from the network (to accept donations or rewards) or addresses leaked by exchanges, these data are used as a training set to find the best separation space in the given data and classify them.…”

Section: Behavior-based Methodsmentioning

confidence: 99%

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Song

Zhang

Wang

et al. 2023

Tsinghua Sci. Technol.

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Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in, and several users have participated in the accumulation of massive transaction data. Complete transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in blockchains. Network modeling and analysis of cryptocurrency transaction records are common methods in blockchain data analysis. The analysis of attribute graphs can provide insights into various economic indicators, illegal activities, and general Internet security, among others. Accordingly, this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex networks. To provide systematic guidance for researchers, we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects: blockchain data model, data acquisition on blockchains, existing analysis tools, available insights, and common analysis methods. For each aspect, we introduce the research problems, summarize the methods, and discuss the results and findings. Finally, we present future research points and several open questions in the study of cryptocurrency transaction networks.

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“…where X * implies the last implication of X (1) . X * represents the executed as hidden layer outcome to evaluate the last weighted β * and it can be rewritten as:…”

Section: Process Involved In Elmae Modelmentioning

confidence: 99%

“…The advancement of artificial intelligence (AI) experiences massive variations for years in which numerous effective applications have been provided to the general public to offer a very comfortable life at present [1]. One such significant research branch of AI technologies is known as machine learning (ML), the 3 demonstrative learning technologies: semi-supervised, supervised, and unsupervised.…”

Section: Introductionmentioning

confidence: 99%

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

Kalyani¹,

Velmurugan²,

Abdeljaber³

et al. 2023

Computers, Materials &Amp; Continua

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In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

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Machine learning in/for blockchain: Future and challenges

Cited by 39 publications

References 56 publications

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

Blockchain Data Analysis from the Perspective of Complex Networks: Overview

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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