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Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications

Author

Listed:
  • Sebastian Bersch

    (School of Engineering, University of Portsmouth, Portsmouth, UK)

  • Djamel Azzi

    (School of Engineering, University of Portsmouth, Portsmouth, UK)

  • Rinat Khusainov

    (School of Engineering, University of Portsmouth, Portsmouth, UK)

  • Ifeyinwa E. Achumba

    (School of Engineering, University of Portsmouth, Portsmouth, UK)

Abstract

This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work.

Suggested Citation

  • Sebastian Bersch & Djamel Azzi & Rinat Khusainov & Ifeyinwa E. Achumba, 2013. "Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 5(3), pages 1-15, July.
    • Handle: RePEc:igg:jaci00:v:5:y:2013:i:3:p:1-15
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