Mapping the NFT revolution: market trends, trade networks, and visual features

AbstractNon Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

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A REVIEW ON GAS CONSUMPTION AND GAS PREDICTION IN ETHEREUM SMART CONTRACTS

EPRA International Journal of Multidisciplinary Research (IJMR) ◽

10.36713/epra8349 ◽

2021 ◽

pp. 54-58

Author(s):

Kuldeep Nageshawar ◽

Rupali chourey ◽

Dr. Ritu Shrivastava

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Smart Contracts ◽

Transaction Data ◽

Regression Algorithms ◽

Gas Consumption

This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction

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Forecasting stock market trend: a comparison of machine learning algorithms

Finance, Markets and Valuation ◽

10.46503/nluf8557 ◽

2020 ◽

Vol 6 (1) ◽

pp. 37-49

Author(s):

R. Cervelló-Royo ◽

F. Guijarro

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Research Topic ◽

Machine Learning Algorithms ◽

The Past ◽

Average Accuracy ◽

Mathematical Formulas ◽

Market Trends ◽

Chart Patterns ◽

Past Trend

Forecasting the direction of stocks markets has become a popular research topic in recent years. Differentapproaches have been applied by researchers to address the prediction of market trends by consideringtechnical indicators and chart patterns from technical analysis. This paper compares the performanceof four machine learning algorithms to validate the forecasting ability of popular technical indicators inthe technological NASDAQ index. Since the mathematical formulas used in the calculation of technicalindicators comprise historical prices they will be related to the past trend of the market. We assume thatforecasting performance increases when the trend is computed on a longer time horizon. Our resultssuggest that the random forest outperforms the other machine learning algorithms considered in ourresearch, being able to forecast the 10-days ahead market trend, with an average accuracy of 80%.

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Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis

Soft Computing ◽

10.1007/s00500-019-04347-y ◽

2019 ◽

Vol 24 (15) ◽

pp. 11019-11043 ◽

Cited By ~ 4

Author(s):

Wasiat Khan ◽

Usman Malik ◽

Mustansar Ali Ghazanfar ◽

Muhammad Awais Azam ◽

Khaled H. Alyoubi ◽

...

Keyword(s):

Machine Learning ◽

Stock Market ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Situation Analysis ◽

Political Situation ◽

Public Sentiment ◽

Market Trends

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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