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Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions

  • Ioanna PolychronouEmail author
  • Panagis Katsivelis
  • Mihalis Papakonstantinou
  • Giannis Stoitsis
  • Nikos Manouselis
Conference paper
  • 92 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 554)

Abstract

It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.

Keywords

Machine learning Deep learning Data analytics Big data Experimentation method 

Notes

Acknowledgements

This work is funded with the support by European Commission, and more specifically project Big Data Grapes “Big Data to Enable Global Disruption of the Grapevine-powered industries” (Grant No. 780751) (http://www.bigdatagrapes.eu/), which is funded by the schema “Research and innovation actions (RIA)” under the work programme topic “ICT-16-2017 - Big data PPP: research addressing main technology challenges of the data economy”. This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use, which may be made of the information contained therein.

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Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  • Ioanna Polychronou
    • 1
    Email author
  • Panagis Katsivelis
    • 1
  • Mihalis Papakonstantinou
    • 1
  • Giannis Stoitsis
    • 1
  • Nikos Manouselis
    • 1
  1. 1.AgroknowMaroussiGreece

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