scholarly journals DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1991-2005 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Learning Approach ◽  
Unsupervised Anomaly Detection

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 120043-120065
Author(s):  
Kukjin Choi ◽  
Jihun Yi ◽  
Changhwa Park ◽  
Sungroh Yoon
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Review Analysis

scholarly journals A Deep Learning Approach to Anomaly Detection in the Gaia Space Mission Data

2019 ◽  
pp. 390-401
Author(s):  
Alessandro Druetto ◽  
Marco Roberti ◽  
Rossella Cancelliere ◽  
Davide Cavagnino ◽  
Mario Gai
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Space Mission ◽  
Learning Approach

scholarly journals Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Extreme Points ◽  
Learning Approach ◽  
Load Estimation ◽  
Learning Method ◽  
Network Computing ◽  
Noise Interference

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach

2021 ◽  
Author(s):  
Julio Galvan ◽  
Ashok Raja ◽  
Yanyan Li ◽  
Jiawei Yuan
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Data Driven ◽  
Sensor Data ◽  
Learning Approach
Sign in / Sign up

Export Citation Format

Share Document