ArticleComputational intelligence and neuroscience2021
Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- Article
- Sustainability in Global Agri-Food Supply Chains: Insights from a Comprehensive Literature Review and the ABCDE Framework.Foods (Basel, Switzerland) · 2024Review
- A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance.BMC sports science, medicine & rehabilitation · 2024Article
- A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation.International journal of environmental research and public health · 2023Article
- Miniature Noninvasive Sensor Based on Impedance-Change Detection in Branches for Measuring Branch Ice Content in Overwintering Woody Plants.Micromachines · 2023Article
- Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.Sensors (Basel, Switzerland) · 2023Article
- Smart Soil Water Sensor with Soil Impedance Detected via Edge Electromagnetic Field Induction.Micromachines · 2022Article
- Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer.Foods (Basel, Switzerland) · 2022Article
- Security Risk Level Prediction of Carbofuran Pesticide Residues in Chinese Vegetables Based on Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.Entropy (Basel, Switzerland) · 2022Article
- A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.Entropy (Basel, Switzerland) · 2022Article
- Blockchain-Based Information Supervision Model for Rice Supply Chains.Computational intelligence and neuroscience · 2022Article
- Online Small Sample Learner Modeling and Curriculum Recommendation with Healthy Emotional Factors of College Students.Computational intelligence and neuroscience · 2022Article
- An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.Computational intelligence and neuroscience · 2022Article
- Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction.Computational intelligence and neuroscience · 2022Article
- A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.Computational intelligence and neuroscience · 2022Article
- An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.Computational intelligence and neuroscience · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain. The aim of the proposed method is to help managers and operators in food enterprises to find accurate risk levels of food security in advance and to provide regulatory authorities and consumers with potential rules for better decision-making, thereby maintaining the safety and sustainability of food product supply. The verification experiments show that the proposed method has the best performance in terms of prediction accuracy up to 97.62%, meanwhile achieves the appropriate model parameters only up to 211.26 megabytes. Moreover, the case analysis is implemented to illustrate the outperforming performance of the proposed method in risk level identification. It can effectively enhance the RITS ability for assuring food supply chain security and attaining multiple cooperation between regulators, enterprises, and consumers.
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