Improving Soybean Disease Prediction by Performing Late-Stage Re-Training Using Fireworks Algorithm

Angad Sandhu, Rajashree Krishna, Ishita Gaur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a novel method of training and optimizing a model to get as optimal a result as possible. This study aims to shed light on alternative ways of using multiple optimizers with different properties to get better results, achieving an incremental increase in accuracy. This is done by switching the optimizer after training once, hence performing Late-Stage Re-Training (LSRT). The present study shows the use of 2 different optimizers, Adam and Fireworks, for their unique properties to gain better results. Adam is used first for most of the training process and is later switched out to Fireworks for re-training this model. This methodology is tested on the Soybean dataset. The above process leads to an average of 2-3% increase, thus obtaining a training accuracy of 98.9%.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-23
Number of pages5
ISBN (Electronic)9781665487160
DOIs
Publication statusPublished - 2022
Event6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Shivamogga, India
Duration: 14-10-202215-10-2022

Publication series

Name2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings

Conference

Conference6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Country/TerritoryIndia
CityShivamogga
Period14-10-2215-10-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Control and Optimization

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