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Related Concept Videos

Conditioned Taste Aversion01:14

Conditioned Taste Aversion

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Conditioned taste aversion, also known as sauce béarnaise syndrome, is a phenomenon in which an individual develops an aversion to a certain food taste following a negative experience, typically illness. This form of aversion is a type of classical conditioning in which the taste of the food (conditioned stimulus, CS) is associated with the experience of illness (unconditioned stimulus, UCS).
A notable characteristic of conditioned taste aversion is that it often requires only a single...
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In biological systems, most metabolic pathways are interconnected. The cellular respiration processes that convert glucose to ATP—such as glycolysis, pyruvate oxidation, and the citric acid cycle—tie into those that break down other organic compounds. As a result, various foods—from apples to cheese to guacamole—end up as ATP. In addition to carbohydrates, food also contains proteins and lipids—such as cholesterol and fats. All of these organic compounds are used...
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Related Experiment Video

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Deep Neural Networks for Image-Based Dietary Assessment
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An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

Min Zuo1, Baoyu Zhang1, Qingchuan Zhang1

  • 1Beijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.

Computational Intelligence and Neuroscience
|March 3, 2022
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Summary

This study introduces FHER, a novel entity relation extraction method for food health and safety. It enhances few-shot learning, enabling effective analysis of complex food-health relationships even with limited data.

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Area of Science:

  • Natural Language Processing
  • Food Science
  • Public Health

Background:

  • Entity relation extraction is vital for analyzing structured text.
  • Limited research exists for complex food-health relationships.
  • Advanced methods are needed for the food health and safety domain.

Purpose of the Study:

  • Propose an entity relation extraction method (FHER) for few-shot learning in food health and safety.
  • Address the challenge of analyzing complex food-health concepts with limited data.
  • Improve the performance of entity relationship extraction in this specialized domain.

Main Methods:

  • Developed a novel entity relation extraction method named FHER.
  • Implemented three distinct techniques to enhance few-shot learning performance.
  • Utilized self-built datasets (FH and MHD) for model training and evaluation.

Main Results:

  • The FHER method demonstrated effective extraction of domain-specific entities and relations.
  • Significant performance improvements were observed in a few-shot learning context.
  • The approach proved successful even with small sample sizes.

Conclusions:

  • The proposed FHER method is effective for entity relation extraction in the food health and safety domain.
  • The applied techniques successfully address few-shot learning challenges.
  • This research provides a valuable tool for analyzing complex food-health data.