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Learning Markets: An AI Collaboration Framework Based on Blockchain and Smart Contracts 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2022, 卷号: 9, 期号: 16, 页码: 14273-14286
作者:  Ouyang, Liwei;  Yuan, Yong;  Wang, Fei-Yue
Adobe PDF(2684Kb)  |  收藏  |  浏览/下载:308/21  |  提交时间:2022/09/19
Collaboration  Artificial intelligence  Data models  Computational modeling  Smart contracts  Blockchain  Distributed databases  Artificial intelligence (AI) collaboration  blockchain  ensemble learning  federated learning (FL)  smart contracts  
A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts 期刊论文
INFORMATION SCIENCES, 2021, 卷号: 570, 页码: 124-143
作者:  Ouyang, Liwei;  Yuan, Yong;  Cao, Yumeng;  Wang, Fei-Yue
Adobe PDF(1322Kb)  |  收藏  |  浏览/下载:266/56  |  提交时间:2021/08/15
Blockchain  Smart contracts  Federated learning  Learning markets  Collaborative early warning  
Analyzing Bitcoin transaction fees using a queueing game model 期刊论文
ELECTRONIC COMMERCE RESEARCH, 2020, 期号: 3, 页码: 21
作者:  Li, Juanjuan;  Yuan, Yong;  Wang, Fei-Yue
浏览  |  Adobe PDF(1741Kb)  |  收藏  |  浏览/下载:331/87  |  提交时间:2020/08/31
Bitcoin  Blockchain  Transaction fees  Transaction confirmation queueing game  
Parallel End-to-End Autonomous Mining: An IoT-Oriented Approach 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2020, 卷号: 7, 期号: 2, 页码: 1011-1023
作者:  Gao, Yu;  Ai, Yunfeng;  Tian, Bin;  Chen, Long;  Wang, Jian;  Cao, Dongpu;  Wang, Fei-Yue
收藏  |  浏览/下载:180/0  |  提交时间:2020/06/02
Data mining  Transportation  Internet of Things  Mining equipment  Safety  Decision making  Artificial societies (A) for modeling  computational experiments (C) for analysis  and parallel execution (P) for control (ACP) approach  autonomous truck  decision making  Internet of Things (IoT)  management and control  parallel mines  
Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2020, 卷号: 21, 期号: 10, 页码: 4063 - 4071
作者:  Fenghua Zhu;  Yisheng Lv;  Yuanyuan Chen;  Xiao Wang;  Gang Xiong;  Fei-Yue Wang
浏览  |  Adobe PDF(2879Kb)  |  收藏  |  浏览/下载:424/222  |  提交时间:2020/10/15
Intelligent transportation systems  
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
浏览  |  Adobe PDF(2273Kb)  |  收藏  |  浏览/下载:404/102  |  提交时间:2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
A Novel GSP Auction Mechanism for Ranking Bitcoin Transactions in Blockchain Mining 期刊论文
DECISION SUPPORT SYSTEMS, 2019, 卷号: 124, 期号: 2019, 页码: NA
作者:  Li, Juanjuan;  Yuan, Yong;  Wang, Fei-Yue
浏览  |  Adobe PDF(2794Kb)  |  收藏  |  浏览/下载:721/127  |  提交时间:2019/09/25
Blockchain  Generalized Second Price auction  Transaction fee  Quality score  Virtual fee  
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending 期刊论文
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 卷号: 103, 页码: 142-157
作者:  Dai, Xingyuan;  Fu, Rui;  Zhao, Enmin;  Zhang, Zuo;  Lin, Yilun;  Wang, Fei-Yue;  Li, Li
Adobe PDF(5109Kb)  |  收藏  |  浏览/下载:302/26  |  提交时间:2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction  
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 6, 页码: 2395-2400
作者:  Lin, Yilun;  Dai, Xingyuan;  Li, Li;  Wang, Fei-Yue
浏览  |  Adobe PDF(623Kb)  |  收藏  |  浏览/下载:351/151  |  提交时间:2019/05/07
Traflic flow prediction  deep learning  generative adversarial network  
A novel hybrid share reporting strategy for blockchain miners in PPLNS pools 期刊论文
DECISION SUPPORT SYSTEMS, 2019, 卷号: 118, 期号: NA, 页码: 91-101
作者:  Qin, Rui;  Yuan, Yong;  Wang, Fei-Yue
浏览  |  Adobe PDF(2213Kb)  |  收藏  |  浏览/下载:451/150  |  提交时间:2019/07/12
Share reporting strategy  Blockchain mining  PPLNS reward  Pool mining  Computational experiments approach