Food security prediction from heterogeneous data combining machine and deep learning methods
作者：
Highlights：
• The analysis of Food Security related phenomena poses several research challenges.
• We focus on the Food Consumption Score and Household Dietary Diversity Score indicators.
• We propose the FSPHD machine learning framework for the prediction of such indicators.
• We use a large set of input data heterogeneous in terms of format and domain.
• The results show promising performances that outperform competing methods.
摘要
•The analysis of Food Security related phenomena poses several research challenges.•We focus on the Food Consumption Score and Household Dietary Diversity Score indicators.•We propose the FSPHD machine learning framework for the prediction of such indicators.•We use a large set of input data heterogeneous in terms of format and domain.•The results show promising performances that outperform competing methods.
论文关键词：Food security,Machine learning,Deep learning,Heterogeneous data
论文评审过程：Received 12 March 2021, Revised 20 September 2021, Accepted 31 October 2021, Available online 20 November 2021, Version of Record 2 December 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.116189
