Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent

Jianlei Kong^1,2, Chengcai Yang¹, Jianli Wang¹

¹School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Computational Intelligence and Neuroscience

|November 22, 2021

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AttSCNs: A Bayesian-Optimized Hybrid Model with Attention-Guided Stochastic Configuration Networks for Robust GPS Trajectory Prediction.

Entropy (Basel, Switzerland)·2025

Same author

Long intergenic non-protein coding RNA 1949 suppresses rituximab resistance in diffuse large B-cell lymphoma via H3K27me3-mediated ONECUT2 silencing.

American journal of translational research·2025

Same author

Synergistic atmospheric influence on the co-pyrolysis of antibiotic sludge and waste bicycle tires: Optimal drivers, products, and pathways.

Journal of hazardous materials·2024

Same author

Comparative Analysis of Stent-Assisted Versus Non-Stent-Assisted Coiling in the Management of Ruptured Intracranial Aneurysms: A Systematic Review and Meta-Analysis.

Translational stroke research·2024

Same author

Bimetallic CuMn nanozyme-enzyme microsystem for efficient dimethyl phthalate degradation.

International journal of biological macromolecules·2024

Same author

Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells.

Scientific reports·2024

Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

See all related articles

This summary is machine-generated.

This study introduces a deep-stacking network for identifying food hazards in the supply chain. The method enhances real-time risk identification and traceability systems (RITSs), improving food safety and sustainability.

Area of Science:

Food Science and Technology
Computer Science and Engineering
Supply Chain Management

Background:

Increasing global concerns regarding food quality and safety necessitate advanced solutions.
Traditional food supply chain risk management faces challenges with real-time data integration.
Existing real-time risk identification and traceability systems (RITSs) require enhanced predictive capabilities.

Purpose of the Study:

To develop an innovative deep-stacking network for accurate hazardous risk identification in food supply chains.
To improve the predictive accuracy and efficiency of real-time risk identification and traceability systems (RITSs).
To support decision-making for food enterprises, regulatory authorities, and consumers, ensuring food safety and sustainability.

Main Methods:

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Oct 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Utilized a deep-stacking network approach for hazardous risk identification.
Integrated massive multisource data from the Internet of Things (IoT) across the entire food supply chain.
Conducted verification experiments and case analysis to evaluate the method's performance.

Main Results:

Achieved a prediction accuracy of up to 97.62% for hazardous risk identification.
Model parameters were optimized to an appropriate size of 211.26 megabytes.
Demonstrated superior performance in risk level identification compared to existing methods.

Conclusions:

The proposed deep-stacking network method significantly enhances the capabilities of RITSs for food supply chain security.
The approach provides accurate, real-time risk assessment, aiding proactive decision-making.
It fosters collaboration among regulators, enterprises, and consumers for improved food safety and sustainability.