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Information Fusion and Machine Learning in Spatial Prediction for Local Agricultural Markets

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 887)

Abstract

This research explores information fusion and data mining techniques and proposes a methodology to improve predictions based on strong associations among agricultural products, which allows prediction for future consumption in local markets in the Andean region of Ecuador using spatial prediction techniques. This commercial activity is performed using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups.

Keywords

Data fusion Alternative circuits of commercialization Associations mining Predictive analysis 

Notes

Acknowledgements

This work was supported in part by Project MINECO TEC2017-88048-C2-2-R and by Commercial Coordination Network, Ministry of Agriculture, Livestock, Aquaculture and Fisheries Ecuador.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Group Ideia GeocaSalesian Polytechnic University of Quito-Ecuador Engineer SystemsQuitoEcuador
  2. 2.Applied Artificial Intelligence GroupCarlos III UniversityMadridSpain

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