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A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 800-811
作者:  Snehasis Banerjee;  Tanushyam Chattopadhyay;  Utpal Garain
浏览  |  Adobe PDF(891Kb)  |  收藏  |  浏览/下载：182/55  |  提交时间：2021/02/22
Feature engineering  sensor data analysis  Internet of things (IoT) analytics  interpretable learning  automation.  
Deep Learning Based Single Image Super-resolution: A Survey 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 413-426
作者:  Viet Khanh Ha;  Jin-Chang Ren;  Xin-Ying Xu;  Sophia Zhao;  Gang Xie;  Valentin Masero;  Amir Hussain
浏览  |  Adobe PDF(1389Kb)  |  收藏  |  浏览/下载：121/31  |  提交时间：2021/02/22
Image super-resolution  convolutional neural network  high-resolution image  low-resolution image  deep learning.  
Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 3, 页码: 286-296
作者:  Bing-Tao Zhang;  Xiao-Peng Wang;  Yu Shen
浏览  |  Adobe PDF(847Kb)  |  收藏  |  浏览/下载：169/42  |  提交时间：2021/02/22
Feature fusion  mild difficulty in falling asleep (MDFA)  decision support tool  sleep issues  optimal feature set.  
Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 27-39
作者:  Yu Hao;  Zhi-Jie Xu;  Ying Liu;  Jing Wang;  Jiu-Lun Fan
浏览  |  Adobe PDF(1521Kb)  |  收藏  |  浏览/下载：211/55  |  提交时间：2021/02/22
Crowd behavior  spatial-temporal texture  gray level co-occurrence matrix  information entropy.  
Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 16-26
作者:  Tian-Xiang Zhang;  Jin-Ya Su;  Cun-Jia Liu;  Wen-Hua Chen
浏览  |  Adobe PDF(1123Kb)  |  收藏  |  浏览/下载：166/47  |  提交时间：2021/02/22
Sentinel-2A  remote sensing  image classification  supervised learning  precision agriculture.