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Computational Decision Support System for ADHD Identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 2, 页码: 233-255
作者:  Senuri De Silva
Adobe PDF(1937Kb)  |  收藏  |  浏览/下载:248/75  |  提交时间:2021/04/22
Attention deficit/hyperactivity disorder (ADHD)  functional magnetic resonance imaging (fMRI)  eye movement data  seed-based correlation  ensembled model  convolutional neural network (CNN)  default mode network (DMN)  saccades  fixations  ADHD-Care decision support system (DDS)  
Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 814-825
作者:  Lu-Jie Zhou
Adobe PDF(1208Kb)  |  收藏  |  浏览/下载:202/48  |  提交时间:2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
Computational Intelligence in Remote Sensing Image Registration: A survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 1-17
作者:  Yue Wu;  Jun-Wei Liu;  Chen-Zhuo Zhu;  Zhuang-Fei Bai;  Qi-Guang Miao;  Wen-Ping Ma;  Mao-Guo Gong
Adobe PDF(995Kb)  |  收藏  |  浏览/下载:224/62  |  提交时间:2021/02/23
Computational intelligence  evolutionary computation  neural network  deep learning  remote sensing image registration  
2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 377-409
作者:  Wei Jia;  Wei Xia;  Yang Zhao;  Hai Min;  Yan-Xiang Chen
Adobe PDF(15758Kb)  |  收藏  |  浏览/下载:261/40  |  提交时间:2021/05/24
Performance evaluation  neural architecture search  biometrics  palmprint  palm vein  deep learning  
Improved Network for Face Recognition Based on Feature Super Resolution Method 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 915-925
作者:  Ling-Yi Xu;  Zoran Gajic
Adobe PDF(1989Kb)  |  收藏  |  浏览/下载:150/39  |  提交时间:2021/11/26
Face recognition  feature super resolution  multiple-branch network  deep learning  convolutional neural networks  
Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 935-946
作者:  Lu-Jie Zhou
Adobe PDF(1263Kb)  |  收藏  |  浏览/下载:184/41  |  提交时间:2021/11/26
Train control system  Chinese named entity recognition (NER)  character feature  gating mechanism  bidirectional long short-term memory (BiLSTM)  
Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 981-992
作者:  Amlan Jyoti Baruah
Adobe PDF(1583Kb)  |  收藏  |  浏览/下载:180/48  |  提交时间:2021/11/26
Educational data mining (EDA)  MapReduce framework  deep neuro-fuzzy network  student performance  data augmentation  
Deep Audio-Visual Learning: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 351-376
作者:  Hao Zhu;  Man-Di Luo;  Rui Wang;  Ai-Hua Zheng;  Ran He
Adobe PDF(1864Kb)  |  收藏  |  浏览/下载:187/34  |  提交时间:2021/05/24
Deep audio-visual learning  audio-visual separation and localization  correspondence learning  generative models  representation learning  
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 311-333
作者:  Xiao-Qin Zhang;  Run-Hua Jiang;  Chen-Xiang Fan;  Tian-Yu Tong;  Tao Wang Peng-Cheng Huang
Adobe PDF(1787Kb)  |  收藏  |  浏览/下载:246/35  |  提交时间:2021/05/24
Deep learning  visual tracking  data-invariant  data-adaptive  general components  
Learning Deep RGBT Representations for Robust Person Re-identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 443-456
作者:  Ai-Hua Zheng;  Zi-Han Chen;  Cheng-Long Li;  Jin Tang;  Bin Luo
Adobe PDF(1832Kb)  |  收藏  |  浏览/下载:245/48  |  提交时间:2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning