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Computational intelligence framework for context-aware decision making


  • Adithya Thaduri

    () (LuleƄ University of Technology)

  • Uday Kumar

    () (LuleƄ University of Technology)

  • Ajit Kumar Verma

    () (Haugesund University College)


Abstract Learning of context-aware systems is necessary in building up knowledge on the characteristics of the environment to provide efficient decision making within multi-objective requirements. As the industrial systems becomes complex day-by-day, intelligent machine learning techniques need to be implemented at respective context-aware situations to facilitate recommendations using soft computing methods based on dynamic user specifications. In this paper, a framework is designed for a meta-database that is generated by contextual information of several peers with what-if conditions and rule-based approaches and thus by provide decision making utilizing several existing soft computing algorithms.

Suggested Citation

  • Adithya Thaduri & Uday Kumar & Ajit Kumar Verma, 2017. "Computational intelligence framework for context-aware decision making," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 2146-2157, December.
  • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:4:d:10.1007_s13198-014-0320-8
    DOI: 10.1007/s13198-014-0320-8

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    References listed on IDEAS

    1. repec:spr:isorms:978-0-387-30177-8 is not listed on IDEAS
    2. Pawlak, Zdzislaw, 2002. "Rough sets, decision algorithms and Bayes' theorem," European Journal of Operational Research, Elsevier, vol. 136(1), pages 181-189, January.
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