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Assessment of Social Transmission of Food Preferences Behaviors
Published on: January 25, 2018
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Human-model interaction-based decision support system for optimizing food safety assessment
Canghong Jin1, Yuanhong Xiao2, Hao Wu3
1Hangzhou City University, Huzhou Street 51, Hangzhou 310015, Zhejiang Province, China.
Food Research International (Ottawa, Ont.)
|April 23, 2025
Summary
This study introduces the Model-Human-interaction Risk Assessment (MHRA) framework, enhancing decision support systems (DSS) with human input for better data quality and model optimization. It improves accuracy and food safety by integrating expert knowledge into the loop.
Area of Science:
- Decision Support Systems
- Machine Learning
- Food Safety
Background:
- Real-world decision support systems (DSS) require high-quality data for continuous operation, but data acquisition is often costly and resource-intensive, especially in specialized domains.
- Machine learning research increasingly explores integrating experimental data and expert knowledge to overcome data acquisition challenges.
- The 'Human in the Loop' (HITL) approach is gaining traction for its ability to ensure expert system knowledge bases remain up-to-date.
Purpose of the Study:
- To present a novel framework, Model-Human-interaction Risk Assessment (MHRA), designed to improve DSS performance through human interaction and collaborative scenario construction.
- To address the need for updateable expert system knowledge bases by integrating human input across various DSS phases.
- To demonstrate how human interactive simulation models can enhance decision-making accuracy and standardization while minimizing food safety risks.
Main Methods:
- Development of the Model-Human-interaction Risk Assessment (MHRA) framework.
- Integration of human interaction and collaborative scenario construction into the DSS workflow.
- Application of a human interactive simulation model to assist decision-makers.
Main Results:
- The MHRA framework leverages human interaction to enhance DSS performance and data quality.
- The 'Human in the Loop' (HITL) approach ensures continuous knowledge base updates.
- The human interactive simulation model aids in maximizing evaluation model accuracy and standardization.
Conclusions:
- The MHRA framework offers a practical solution for improving decision support systems by incorporating human expertise.
- The study highlights the effectiveness of human interactive simulation in minimizing food safety risks.
- The infant food assessment case study demonstrates the framework's applicability and provides insights into its strengths and limitations.
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