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Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems
2021
Computational Intelligence and Neuroscience
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
doi:10.1155/2021/1194565
pmid:34804137
pmcid:PMC8598327
fatcat:jddvyv7qeje77klkj6uizc4k5i