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Augmentations for selective multi-species quantification from infrared spectroscopic data
Emad Al Ibrahim,
Aamir Farooq
Mechanical Engineering
Physical Sciences and Engineering
Research output
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Contribution to journal
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Article
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peer-review
4
Scopus citations
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Dive into the research topics of 'Augmentations for selective multi-species quantification from infrared spectroscopic data'. Together they form a unique fingerprint.
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Keyphrases
Species Determination
100%
Multispecies
100%
Corruption
100%
Infrared Spectroscopic Data
100%
High Sensitivity
50%
High Selectivity
50%
Machine Learning Techniques
50%
Real-world Application
50%
New Sensors
50%
Realistic Scenario
50%
Sensing Platform
50%
Sensor Design
50%
Augmentation Strategy
50%
Gas Sensing Applications
50%
Infrared Spectroscopy
50%
Volatile Organic Compounds
50%
Unknown Interference
50%
Spectroscopic Sensor
50%
Interfering Species
50%
Engineering
Real World Application
100%
Sensing Application
100%
Machine Learning Method
100%
Demonstrates
100%
Computer Science
Machine Learning
100%
World Application
100%
Chemical Engineering
Learning System
100%