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Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by Web crawler 期刊论文
IEEE Transactions on Computational Social Systems, 2019, 卷号: 6, 期号: 3, 页码: 547-553
作者:  Weng, Yuchen;  Wang, Xiujuan;  Kang, Mengzhen;  Hua, Jing;  Wang, Fei-yue
浏览  |  Adobe PDF(1374Kb)  |  收藏  |  浏览/下载：373/185  |  提交时间：2020/06/08
Web crawler  cucumber  agricultural CPSS framework  ARIMA model  neural network  price forecasting  
Parallel Crime Scene Analysis Based on ACP Approach 期刊论文
IEEE Transactions on Computational Social Systems, 2018, 卷号: 5, 期号: 1, 页码: 244-255
作者:  Wang, Shuai;  Wang, Xiao;  Ye, Peijun;  Yuan, Yong;  Liu, Shuo;  Wang, Feiyue
浏览  |  Adobe PDF(2590Kb)  |  收藏  |  浏览/下载：264/75  |  提交时间：2019/11/12
Artificial Societies  Computational Experiments  Parallel Execution  Crime Scene Analysis  Parallel Systems  
A General Cognitive Architecture for Agent-Based Modeling in Artificial Societies 期刊论文
IEEE Transactions on Computational Social Systems, 2018, 卷号: 5, 期号: 1, 页码: 176-185
作者:  Ye,Peijun
Adobe PDF(1191Kb)  |  收藏  |  浏览/下载：324/126  |  提交时间：2019/01/28
Agent-based Modeling  Artificial Society  Cognitive Architecture (Ca)  
Parallel Crime Scene Analysis Based on ACP Approach 期刊论文
IEEE Transactions on Computational Social Systems, 2018, 卷号: 5, 期号: 1, 页码: 244-255
作者:  Shuai Wang;  Xiao Wang;  Peijun Ye;  Yong Yuan;  Shuo Liu;  Fei-Yue Wang
Adobe PDF(2590Kb)  |  收藏  |  浏览/下载：443/176  |  提交时间：2018/10/09
Artificial Societies  Computational Experiments And Parallel Execution (Acp) Approach  Computational Experiments  Crime Scene Analysis  Parallel Systems.  
Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition 期刊论文
IEEE Transactions on Computational Social Systems, 2017, 卷号: PP, 期号: 99, 页码: 1-14
作者:  Yang Xing;  Chen Lv;  Zhaozhong Zhang;  Huaji Wang;  Xiaoxiang Na;  Dongpu Cao;  Efstathios Velenis;  Fei-Yue Wang
浏览  |  Adobe PDF(4412Kb)  |  收藏  |  浏览/下载：307/95  |  提交时间：2018/01/08
Driver Behavior  Driver Distraction  Feedforward Neural Network (Ffnn)  Kinect  Random Forest (Rf).