Skip to main navigation
Skip to search
Skip to main content
University of Haifa Home
Update your profile
Home
Researchers
Research units
Research output
Search by expertise, name or affiliation
Improving fluoroprobe sensor performance through machine learning
D. Lafer
, A. Sukenik
, T. Zohary
, O. Tal
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Improving fluoroprobe sensor performance through machine learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Accessory pigments
11%
Accurate Assessment
11%
Aquatic Ecosystems
11%
Chlorophyll a (Chl a)
11%
Community Structure
22%
Community-based
11%
Dinoflagellate
11%
Dynamic Quality
11%
Ecosystem Dynamics
11%
Extreme Gradient Boosting
11%
Fluorescent Probe
11%
Fluorescent Sensor
11%
FluoroProbe
100%
High-throughput
11%
Inverted Microscope
11%
Israel
11%
Lake Ecosystem
11%
Lake Kinneret
33%
Least Squares Support Vector Regression (LSSVR)
22%
Machine Learning
100%
Machine Learning Models
11%
Mean Square Error
11%
Multi-excitation
11%
Phytoplankton
22%
Phytoplankton Abundance
22%
Phytoplankton Biomass
22%
Phytoplankton Community
22%
Phytoplankton Composition
11%
Phytoplankton Dynamics
11%
Phytoplankton Species
11%
Random Forest Algorithm
11%
Regression Forest
11%
Sea of Galilee
11%
Sensor Limitations
11%
Sensor Performance
100%
Species Biomass
11%
Specific Sensors
11%
Support Vector Regression Algorithm
11%
Taxonomic Groups
22%
Taxonomic Structure
11%
Water Quality
11%
Engineering
Fundamental Component
33%
Learning System
100%
Limitations
66%
Mean Square Error
33%
Random Forest
33%
Raw Data
33%
Support Vector Machine
100%
Earth and Planetary Sciences
Aquatic Environment
11%
Chlorophyll
11%
Ecosystem Dynamics
11%
Israel
11%
Lake Ecosystem
11%
Machine Learning
100%
Phytoplankton
100%
Support Vector Machine
33%
Immunology and Microbiology
Dinoflagellate
11%
Dynamics
22%
Phytoplankton
100%
Random Forest
11%
Support Vector Machine
33%
Taxon
11%