Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing

IEEE Sensors Journal - United States
doi 10.1109/jsen.2019.2949528
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Related search

Towards an Integrative Approach for Automated Literature Reviews Using Machine Learning

2020English

Non-Invasive Hypoglycemia Monitoring System Using Extreme Learning Machine for Type 1 Diabetes

ISA Transactions
ControlSystems EngineeringElectronic EngineeringInstrumentationApplied MathematicsComputer Science ApplicationsElectrical
2016English

Quality for Fresh Consumption and Processing of Some Non-Traditional Tropical Fruits From Brazil

Fruits
HorticultureAgronomyCrop ScienceFood Science
2009English

Credit Risk Assessment Using Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering
Mechanics of MaterialsElectronic EngineeringCivilStructural EngineeringElectricalComputer Science
2019English

The Machine-Learning Approach

2020English

Reservoir Assessment Using Non-Invasive Geophysical Techniques

Environmental Earth Sciences
Planetary ChangeSoil ScienceGeologyGlobalEnvironmental ChemistryEarth-Surface ProcessesTechnologyWater SciencePollution
2018English

Towards Trustable Machine Learning

Nature Biomedical Engineering
MedicineBioengineeringComputer Science ApplicationsBiomedical EngineeringBiotechnology
2018English

Risk Assessment of Atmospheric Emissions Using Machine Learning

Natural Hazards and Earth System Sciences
EarthPlanetary Sciences
2008English

In Vivo Glucose Sensing for Diabetes Management: Progress Towards Non-Invasive Monitoring

BMJ
1999English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy