Keyphrases
Random Forest
100%
Binarization
100%
Binarized Convolutional Neural Networks
100%
Intelligent Fault Diagnosis
100%
Rotating Machines
100%
Edge Computing
44%
Edge Nodes
33%
Real-time Fault Diagnosis
33%
Fault Diagnosis
22%
Deep Neural Network
22%
Central Cloud
22%
Gini Coefficient
22%
Diagnostic Accuracy
22%
Forest-based
22%
DNN-based
22%
High Demand
11%
Fault Detection
11%
Storage Requirement
11%
Traditional Industries
11%
Diagnostic Process
11%
Training Process
11%
Computational Resources
11%
Industrial Sector
11%
Feature Extracting
11%
Feature Extraction
11%
Low Computational Complexity
11%
Classification Accuracy
11%
Number of Edges
11%
Physical Machine
11%
Experiment Results
11%
Separability
11%
Feature Extraction Methods
11%
Evaluation Measures
11%
Transmission Overhead
11%
Large-scale Deployment
11%
Random Forest Regression
11%
Model-based Diagnosis
11%
New Computing Paradigms
11%
System Response Time
11%
Storage Space
11%
Model Computation
11%
Near-end
11%
Rolling Element Bearing
11%
Identification Accuracy
11%
Fault Characteristics
11%
Feature Extractor
11%
Fault Identification
11%
Deep Neural Network Training
11%
Space Resources
11%
Attribute Evaluation
11%
Binary Activations
11%
Improved Random Forest
11%
Edge Computing Device
11%
DNN Model
11%
Easiness
11%
Rotating Machinery
11%
Novel Faults
11%
Fault Diagnosis Model
11%
Diagnostic Efficiency
11%
Binary Features
11%
Fault Classifier
11%
Large-scale Scenario
11%
Rotational Machine
11%
Diagnostic Mode
11%
Cloud-edge Collaborative Computing
11%
Binary Weights
11%
Fault Diagnostics
11%
Engineering
Deep Neural Network
100%
Fault Diagnosis
100%
Random Forest
100%
Edge Computing
71%
Nodes
28%
Network Model
28%
Rotational
28%
Transmissions
14%
Regularization
14%
Storage Requirement
14%
System Response
14%
Response Time
14%
Extracted Feature
14%
Feature Extraction
14%
Classification Accuracy
14%
Based Feature Extraction
14%
Computation Complexity
14%
Separability
14%
Extractor
14%
Rolling Element
14%
Computing Device
14%
Industrial Sector
14%
Model Diagnosis
14%
Closest Node
14%
Computer Supported Cooperative Work
14%
Neural Network Training
14%
Computer Science
Deep Neural Network
100%
Random Decision Forest
100%
Intelligent Fault Diagnosis
100%
Fault Diagnosis
50%
Edge Computing
41%
Neural Network Model
16%
Feature Extraction
16%
Diagnostic Accuracy
16%
Speed-up
8%
computing paradigm
8%
Regularization
8%
Training Process
8%
Computing Device
8%
Computation Complexity
8%
Storage Requirement
8%
Classification Accuracy
8%
Extracted Feature
8%
Physical Machine
8%
Information Loss
8%
Neural Network Training
8%
Evaluation Measure
8%
Fault Detection
8%
Transmission Overhead
8%
System Response Time
8%
Industrial Sector
8%
binarization
8%
fault identification
8%
Binary Feature
8%
Modern Industry
8%
Complex Calculation
8%
Collaborative Computing
8%