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This study introduces a hybrid deep learning model using Internet of Things (IoT) and blockchain data for enhanced food supply chain management. The advanced deep learning (ADL) model optimizes transparency and efficiency, aiding industry policy decisions.

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Supply Chain Management

Background:

  • Food safety and supply chain transparency are critical concerns.
  • Internet of Things (IoT) and blockchain technologies offer solutions for data management.
  • Advanced Deep Learning (ADL) can optimize large datasets generated by these technologies.

Purpose of the Study:

  • To propose a hybrid ADL model for secure IoT-blockchain data in the food industry.
  • To enhance visibility, provenance, and efficiency in the food supply chain.
  • To assist practitioners and policymakers in leveraging Industry 4.0 technologies.

Main Methods:

  • Utilized secure IoT-blockchain data from the food sector.
  • Developed a hybrid model combining Recurrent Neural Networks (RNN), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).
  • Employed Genetic Algorithm (GA) for optimizing the hybrid model's parameters and training.

Main Results:

  • The proposed hybrid LSTM-GRU model, optimized by GA, demonstrated effective prediction capabilities.
  • System performance was evaluated across various user scenarios.
  • The model successfully integrated and optimized data for improved supply chain insights.

Conclusions:

  • The hybrid ADL model offers a significant advancement for managing secure IoT-blockchain data in the food industry.
  • This approach can improve supply chain transparency, efficiency, and decision-making.
  • It provides a framework for leveraging Industry 4.0 technologies for practical applications.