The rapid expansion of the non-fungible token(NFT)market has attracted many investors.However,studies on the NFT price fluctuations have been relatively limited.To date,the machine learning approach has not been used ...The rapid expansion of the non-fungible token(NFT)market has attracted many investors.However,studies on the NFT price fluctuations have been relatively limited.To date,the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction.The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea,the world’s largest NFT marketplace.We used Python programs to collect data and summarized them as:NFT information,collection information,and related account information.AdaBoost and Random Forest(RF)algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models,respectively.We found that the NFT related account information,especially the number of favorites and activity status of creators,confer a good predictive power to both the models.AdaBoost in the regression model had more accurate predictions,the root mean square error(RMSE)in predicting NFT sale price was 0.047.In predicting NFT sale price fluctuations,RF performed better,which the area under the curve(AUC)reached 0.956.We suggest that investors should pay more attention to the information of NFT creators.We anticipate that these prediction models will reduce the number of investment failures for the investors.展开更多
Tongue-coating microbiome,as a part of oral microbiome,is an important part of commensal microbes in human body.Despite technical difficulties in gaining functional insights using metagenomic approaches due to high le...Tongue-coating microbiome,as a part of oral microbiome,is an important part of commensal microbes in human body.Despite technical difficulties in gaining functional insights using metagenomic approaches due to high levels of host DNA contaminations,a number of diseases have been found to be closely related to the alteration of oral microbial communities,including periodontal and caries diseases,cardiovascular disease,diabetes,rheumatoid arthritis.展开更多
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the Innovative Human Resource Development for Local Intellectualization support program(IITP-2022-RS-2022-00156287)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation)supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-01203。
文摘The rapid expansion of the non-fungible token(NFT)market has attracted many investors.However,studies on the NFT price fluctuations have been relatively limited.To date,the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction.The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea,the world’s largest NFT marketplace.We used Python programs to collect data and summarized them as:NFT information,collection information,and related account information.AdaBoost and Random Forest(RF)algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models,respectively.We found that the NFT related account information,especially the number of favorites and activity status of creators,confer a good predictive power to both the models.AdaBoost in the regression model had more accurate predictions,the root mean square error(RMSE)in predicting NFT sale price was 0.047.In predicting NFT sale price fluctuations,RF performed better,which the area under the curve(AUC)reached 0.956.We suggest that investors should pay more attention to the information of NFT creators.We anticipate that these prediction models will reduce the number of investment failures for the investors.
基金supported by the Key Research Program of the Chinese Academy of Sciences under Grant KJZD-SW-L05 to Yixin Zeng and KJZD-SW-L05-04 to Hairong Chenthe Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(Nos.ZYYCXTD-D-202001)to Jun Wang。
文摘Tongue-coating microbiome,as a part of oral microbiome,is an important part of commensal microbes in human body.Despite technical difficulties in gaining functional insights using metagenomic approaches due to high levels of host DNA contaminations,a number of diseases have been found to be closely related to the alteration of oral microbial communities,including periodontal and caries diseases,cardiovascular disease,diabetes,rheumatoid arthritis.