Semi-Supervised Outlier Detection and Deep Feature Extraction for Detecting Cyber-Attacks in Smart Grids Using PMU Data

2020 ◽  
pp. 509-515
Author(s):  
Ruobin Qi ◽  
Craig Rasband ◽  
Jun Zheng ◽  
Raul Longoria
Keyword(s):  
Feature Extraction ◽  
Outlier Detection ◽  
Smart Grids ◽  
Cyber Attacks ◽  
Deep Feature ◽  
Deep Feature Extraction

Integration of deep feature extraction and ensemble learning for outlier detection

2019 ◽  
Vol 89 ◽  
pp. 161-171 ◽  
Author(s):  
Debasrita Chakraborty ◽  
Vaasudev Narayanan ◽  
Ashish Ghosh
Keyword(s):  
Feature Extraction ◽  
Outlier Detection ◽  
Ensemble Learning ◽  
Deep Feature ◽  
Deep Feature Extraction

Towards Robust Localization Deep Feature Extraction by CNN

2020 ◽  
Author(s):  
Erik Carlbaum ◽  
Sina Sharif Mansouri ◽  
Christoforos Kanellakis ◽  
Anton Koval ◽  
George Nikolakopoulos
Keyword(s):  
Feature Extraction ◽  
Deep Feature ◽  
Robust Localization ◽  
Deep Feature Extraction

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

Data‐driven wind speed forecasting using deep feature extraction and LSTM

2019 ◽  
Vol 13 (12) ◽  
pp. 2062-2069 ◽  
Author(s):  
Yu‐Xi Wu ◽  
Qing‐Biao Wu ◽  
Jia‐Qi Zhu
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Data Driven ◽  
Wind Speed Forecasting ◽  
Deep Feature ◽  
Deep Feature Extraction

Video super-resolution network via enhanced deep feature extraction and residual up-down block

2021 ◽  
Vol 29 (06) ◽  
Author(s):  
Jiajia Lei ◽  
Xiaohai He ◽  
Chao Ren ◽  
Xiaohong Wu ◽  
Yi Wang
Keyword(s):  
Feature Extraction ◽  
Super Resolution ◽  
Deep Feature ◽  
Deep Feature Extraction
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