Advanced deep learning approaches to predict supply chain risks under COVID-19 restrictions

作者：

Highlights：

• Proposes a wide range of DL approaches to predict supply chain risks.

• Introducing a temporal convolutional network (TCN) by combining RNN and CNN models.

• Identification of the most promising DL approach in terms of performance.

• Performance demonstration of the selected classifiers on the advanced DL networks.

摘要

•Proposes a wide range of DL approaches to predict supply chain risks.•Introducing a temporal convolutional network (TCN) by combining RNN and CNN models.•Identification of the most promising DL approach in terms of performance.•Performance demonstration of the selected classifiers on the advanced DL networks.

论文关键词：Supply chain risk,COVID-19,Deep learning,Convolutional network,Temporal convolutional network,Classifiers

论文评审过程：Received 7 May 2022, Revised 4 August 2022, Accepted 14 August 2022, Available online 19 August 2022, Version of Record 26 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118604