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An edge-driven multi-agent optimization model for infectious disease detection

Abstract

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.
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Category

Academic article

Language

English

Author(s)

  • Youcef Djenouri
  • Gautam Srivastava
  • Anis Yazidi
  • Jerry Chun-Wei Lin

Affiliation

  • SINTEF Digital / Mathematics and Cybernetics
  • Western Norway University of Applied Sciences
  • OsloMet - Oslo Metropolitan University
  • China Medical University
  • Brandon University

Year

2022

Published in

Applied intelligence (Boston)

ISSN

0924-669X

Volume

52

Issue

12

Page(s)

14362 - 14373

View this publication at Norwegian Research Information Repository