Claudia Gonzalez Viejo

University of Melbourne, Agriculture and Food

论文数

16

总引用数

806

H-Index

14

关于

暂无简介。

研究焦点

Computer science15 · 724 次引用
Artificial intelligence14 · 722 次引用
Machine learning11 · 633 次引用
Mathematics7 · 484 次引用
Chemistry6 · 319 次引用
Quality (philosophy)7 · 309 次引用
Mouthfeel4 · 277 次引用
Food science5 · 230 次引用
Psychology3 · 181 次引用
Biometrics3 · 180 次引用
Robot4 · 179 次引用
Robotics4 · 179 次引用

主要成就

14
H 指数
16
论文
806
总引用数
50
篇均引用
🏆 最高被引论文
Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System
96 次引用 · 2019
📈 最高产年份: 2019 (3 论文)
🤝 主要合作者: 13
🏛 所属机构: University of Melbourne, Agriculture and Food

代表论文

  1. 1
    Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System
    96 次引用 · 2019
  2. 2
    Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages
    92 次引用 · 2019
  3. 3
    Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms
    89 次引用 · 2017
  4. 4
    Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters
    82 次引用 · 2020
  5. 5
    Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications
    66 次引用 · 2018
  6. 6
    Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control
    62 次引用 · 2019
  7. 7
    Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER
    62 次引用 · 2016
  8. 8
    Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers
    53 次引用 · 2018
  9. 9
    Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning
    46 次引用 · 2020
  10. 10
    Beer Aroma and Quality Traits Assessment Using Artificial Intelligence
    36 次引用 · 2020

主要合作者

SFSigfredo Fuentes16 篇合著论文FRFrank R. Dunshea10 篇合著论文DDDamir D. Torrico7 篇合著论文ETEden Tongson4 篇合著论文NLNir Lipovetzky3 篇合著论文KHKate Howell3 篇合著论文HHHai Ho Dac2 篇合著论文CHCarmen Hernández‐Brenes1 篇合著论文BCBrendan Cullen1 篇合著论文GLGuangjun Li1 篇合著论文SSSurinder S. Chauhan1 篇合著论文BCBruna Condé1 篇合著论文RDR. D. COLLMANN1 篇合著论文
尚未生成