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Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation 期刊论文
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2018, 卷号: 25, 期号: 1, 页码: 72-80
作者:  Xie, Jiaheng;  Liu, Xiao;  Zeng, Daniel Dajun
收藏  |  浏览/下载：216/0  |  提交时间：2018/10/10
E-cigarette Adverse Event  Bi-lstm  Recurrent Neural Network  Word Embedding  Deep Neural Network  
Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 卷号: 40, 期号: 2, 页码: 332-351
作者:  Sun, Yunlian;  Zhang, Man;  Sun, Zhenan;  Tan, Tieniu
浏览  |  Adobe PDF(2995Kb)  |  收藏  |  浏览/下载：856/304  |  提交时间：2017/09/16
Demographic Estimation  Biometrics  Human Age Estimation  Gender Classification  Race Recognition  
Who Are Your "Real" Friends: Analyzing and Distinguishing Between Offline and Online Friendships From Social Multimedia Data 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2017, 卷号: 19, 期号: 6, 页码: 1299-1313
作者:  Lu, Dongyuan;  Sang, Jitao;  Chen, Zhineng;  Xu, Min;  Mei, Tao
浏览  |  Adobe PDF(614Kb)  |  收藏  |  浏览/下载：229/0  |  提交时间：2017/09/12
Photo Sharing Behavior Analysis  Offline Friendship Identification  Social Multimedia Mining  
Deep Reinforcement Learning With Visual Attention for Vehicle Classification 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2017, 卷号: 9, 期号: 4, 页码: 356-367
作者:  Zhao, Dongbin;  Chen, Yaran;  Lv, Le
浏览  |  Adobe PDF(3192Kb)  |  收藏  |  浏览/下载：1051/541  |  提交时间：2017/05/08
Convolutional Neural Network (Cnn)  Reinforcement Learning  Vehicle Classification  Visual Attention  
A multi-level text representation model within background knowledge based on human cognitive process for big data analysis 期刊论文
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2016, 卷号: 19, 期号: 3, 页码: 1475-1487
作者:  Wei, Xiao;  Zhang, Jun;  Zeng, Daniel Dajun;  Li, Qing
浏览  |  Adobe PDF(4930Kb)  |  收藏  |  浏览/下载：400/157  |  提交时间：2016/12/26
Text Comprehension  Background Knowledge  Text Representation  Human Cognitive Process  Surface Code  Text Base  Situational Model  Semantics  
Analysis of symptoms and their potential associations with e-liquids' components: a social media study 期刊论文
BMC PUBLIC HEALTH, 2016, 卷号: 16, 期号: 674, 页码: 1-12
作者:  Li, Qiudan;  Zhan, Yongcheng;  Wang, Lei;  Leischow, Scott J.;  Zeng, Daniel Dajun;  Daniel Dajun Zeng
浏览  |  Adobe PDF(820Kb)  |  收藏  |  浏览/下载：287/13  |  提交时间：2016/10/23
E-cigarette  E-juice  E-liquid  Flavors  Vg  Pg  Nicotine  Symptoms  Reddit  Social Media  
A Commonsense Knowledge-Enabled Textual Analysis Approach for Financial Market Surveillance 期刊论文
INFORMS JOURNAL ON COMPUTING, 2016, 卷号: 28, 期号: 2, 页码: 278-294
作者:  Li, Xin;  Chen, Kun;  Sun, Sherry X.;  Fung, Terrance;  Wang, Huaiqing;  Zeng, Daniel D.
收藏  |  浏览/下载：221/0  |  提交时间：2016/10/20
Market Surveillance  Text Mining  Commonsense Knowledge  Business Intelligence  Intelligent Financial Systems  
Top–down visual attention integrated particle filter for robust object tracking 期刊论文
Signal Processing: Image Communication, 2016, 卷号: 43, 期号: 无, 页码: 28-41
作者:  Li, Wanyi;  Wang, Peng;  Qiao, Hong
浏览  |  Adobe PDF(9813Kb)  |  收藏  |  浏览/下载：380/95  |  提交时间：2016/10/13
Object Tracking  Visual Attention  Saliency  Particle Filter  Abrupt Motion  Occlusion  
Enhanced HMAX model with feedforward feature learning for multiclass categorization 期刊论文
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2015, 卷号: 9, 页码: 1-14
作者:  Li, Yinlin;  Wu, Wei;  Zhang, Bo;  Li, Fengfu
浏览  |  Adobe PDF(3664Kb)  |  收藏  |  浏览/下载：382/85  |  提交时间：2016/03/30
Hmax  Biologically Inspired  Feedforward  Saliency Map  Middle Level Patch Learning  Feature Encoding  Multiclass Categorization  
Enhanced Biologically Inspired Model for Object Recognition 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 卷号: 41, 期号: 6, 页码: 1668-1680
作者:  Huang, Yongzhen;  Huang, Kaiqi;  Tao, Dacheng;  Tan, Tieniu;  Li, Xuelong
收藏  |  浏览/下载：187/0  |  提交时间：2015/08/12
Biologically Inspired Model (Bim)  Feedback  Object Recognition  Sparseness