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An evolutionary ensemble learning for diagnosing COVID-19 via cough signals
Mohammad Tayarani
Centre for AI and Robotics Research
School of Physics, Engineering & Computer Science
Department of Computer Science
Biocomputation Research Group
Research output
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Contribution to journal
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Article
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peer-review
40
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Keyphrases
COVID-19
100%
Cough Signals
100%
Evolutionary Ensemble Learning
100%
Convolutional Neural Network
60%
COVID-19 Disease
40%
Convolutional Neural Network Architecture
40%
Raw Signal
20%
Wavelet
20%
Tuberculosis
20%
Existing Algorithms
20%
Asthma
20%
Search Algorithm
20%
Fourier
20%
Short-time Fourier Transform
20%
Space Coverage
20%
Dry Cough
20%
Evolutionary Algorithms
20%
Memetic Algorithm
20%
Global Search
20%
Feature Extractor
20%
Evolutionary Paradigm
20%
Search Space
20%
Weighted Voting
20%
Gradient Method
20%
Automatic Feature Extraction
20%
Machine Learning Algorithms
20%
Curb
20%
Voting System
20%
Ensemble Scheme
20%
Hilbert-Huang
20%
Computer Science
Ensemble Learning
100%
Convolutional Neural Network
100%
Neural Network Architecture
40%
Statistical Approach
20%
Machine Learning Algorithm
20%
Feature Extraction
20%
Gradient Descent
20%
Evolutionary Algorithms
20%
Search Space
20%
Short Term Fourier Transform
20%
Memetic Algorithm
20%