Frank R. Dunshea

University of Melbourne, University of Leeds

论文数

14

总引用数

725

H-Index

11

关于

暂无简介。

研究焦点

Computer science9 · 540 次引用
Artificial intelligence9 · 540 次引用
Machine learning7 · 456 次引用
Mathematics6 · 393 次引用
Chemistry4 · 293 次引用
Mouthfeel4 · 277 次引用
Animal science7 · 237 次引用
Biology7 · 237 次引用
Quality (philosophy)3 · 220 次引用
Dairy cattle6 · 207 次引用
Psychology3 · 181 次引用
Biometrics3 · 180 次引用

主要成就

11
H 指数
14
论文
725
总引用数
52
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: 22
🏛 所属机构: University of Melbourne, University of Leeds

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
    Heat Stress Impacts on Lactating Cows Grazing Australian Summer Pastures on an Automatic Robotic Dairy
    89 次引用 · 2020
  5. 5
    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
  6. 6
    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
  7. 7
    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
  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
    Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence
    30 次引用 · 2021
  10. 10
    Livestock Identification Using Deep Learning for Traceability
    30 次引用 · 2022

主要合作者

SFSigfredo Fuentes10 篇合著论文CGClaudia Gonzalez Viejo10 篇合著论文DDDamir D. Torrico6 篇合著论文SSSurinder S. Chauhan5 篇合著论文ETEden Tongson4 篇合著论文RORichard Osei-Amponsah4 篇合著论文AAArchana Abhijith3 篇合著论文BJB. J. Leury3 篇合著论文NLNir Lipovetzky3 篇合著论文KHKate Howell3 篇合著论文BCBrendan Cullen2 篇合著论文HHHai Ho Dac2 篇合著论文DCDeli Chen1 篇合著论文MBMei Bai1 篇合著论文PPPragna Prathap1 篇合著论文MJMohammad Javed Ashar1 篇合著论文MHMichael H. Zhang1 篇合著论文RERichard Eckard1 篇合著论文MFMatthew Flavel1 篇合著论文LCLong Cheng1 篇合著论文
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