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Computational intelligence framework for context-aware decision making

Author

Listed:
  • Adithya Thaduri

    (Luleå University of Technology)

  • Uday Kumar

    (Luleå 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

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    1. Ignacy Kaliszewski, 2006. "Soft Computing For Complex Multiple Criteria Decision Making," International Series in Operations Research and Management Science, Springer, number 978-0-387-30177-8, September.
    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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    Cited by:

    1. Taushif Anwar & V. Uma, 2021. "Comparative study of recommender system approaches and movie recommendation using collaborative filtering," 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. 12(3), pages 426-436, June.

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