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Improving fluoroprobe sensor performance through machine learning
D. Lafer
, A. Sukenik
, T. Zohary
, O. Tal
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Article
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peer-review
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Dive into the research topics of 'Improving fluoroprobe sensor performance through machine learning'. Together they form a unique fingerprint.
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Keyphrases
Machine Learning
100%
Sensor Performance
100%
FluoroProbe
100%
Lake Kinneret
33%
Phytoplankton
22%
Phytoplankton Biomass
22%
Community Structure
22%
Phytoplankton Abundance
22%
Phytoplankton Community
22%
Taxonomic Groups
22%
Least Squares Support Vector Regression (LSSVR)
22%
Israel
11%
Water Quality
11%
Chlorophyll a (Chl a)
11%
Dinoflagellate
11%
Sea of Galilee
11%
Community-based
11%
High-throughput
11%
Fluorescent Probe
11%
Accurate Assessment
11%
Machine Learning Models
11%
Aquatic Ecosystems
11%
Lake Ecosystem
11%
Ecosystem Dynamics
11%
Phytoplankton Species
11%
Regression Forest
11%
Phytoplankton Composition
11%
Species Biomass
11%
Mean Square Error
11%
Taxonomic Structure
11%
Phytoplankton Dynamics
11%
Extreme Gradient Boosting
11%
Dynamic Quality
11%
Specific Sensors
11%
Random Forest Algorithm
11%
Multi-excitation
11%
Sensor Limitations
11%
Inverted Microscope
11%
Fluorescent Sensor
11%
Support Vector Regression Algorithm
11%
Accessory pigments
11%
Engineering
Learning System
100%
Support Vector Machine
100%
Limitations
66%
Raw Data
33%
Mean Square Error
33%
Random Forest
33%
Fundamental Component
33%
Earth and Planetary Sciences
Machine Learning
100%
Phytoplankton
100%
Support Vector Machine
33%
Israel
11%
Lake Ecosystem
11%
Ecosystem Dynamics
11%
Aquatic Environment
11%
Chlorophyll
11%
Immunology and Microbiology
Phytoplankton
100%
Support Vector Machine
33%
Dynamics
22%
Dinoflagellate
11%
Taxon
11%
Random Forest
11%