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Noise prediction using machine learning with measurements analysis
Po Jiun Wen,
Chihpin Huang
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環境工程研究所
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引文 斯高帕斯（Scopus）
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Keyphrases
Machine Learning
100%
Noise Prediction
100%
Measurement Analysis
100%
Laborers
60%
Noise Measurement
40%
Prediction Model
20%
Learning Model
20%
Synchrotron Radiation
20%
Time Duration
20%
Root Mean Square Error
20%
Performance Prediction
20%
Pollution
20%
Noise Pollution
20%
Noise Sensor
20%
Time-frequency
20%
Workplace
20%
Noise Frequency
20%
Radiation Research
20%
R2 Value
20%
Coefficient of Determination
20%
Long-term Exposure
20%
Machine Learning Models
20%
Noise Equivalent
20%
Training Impact
20%
Gradient Boosting Model
20%
High-noise Environments
20%
Noise Exposure
20%
Training Data Selection
20%
Measurement Record
20%
Computer Science
Machine Learning
100%
Learning System
100%
Measurement Noise
66%
Training Data
33%
Prediction Model
33%
Prediction Performance
33%
Gradient Boosting
33%
Engineering
Learning System
100%
Measurement Noise
66%
Root Mean Square Error
33%
Frequency Noise
33%
Duration Time
33%
Sensor Noise
33%
Prediction Performance
33%
Equivalent Level (Leq)
33%
Physics
Machine Learning
100%
Noise Measurement
66%
Synchrotron Radiation
33%