We highlight some safety issues in commercial planes particulary focussing on hazards in the cockpit area. This chapter discusses a few methodologies to detect critical features and provide unambiguous information about the possible sources of hazards to the end user in near realtime. We explore the application of Bayesian probability, Iyengar—Krishnamachari method, Probabilistic Reasoning, Reasoning under Uncertainty, Dempster–Shafer Theory and analyze how these theories could help in the data analysis gathered from wireless sensor networks deployed in the cockpit area.
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Srivathsan, S., Balakrishnan, N., Iyengar, S.S. (2008). Critical Feature Detection in Cockpits – Application of AI in Sensor Networks. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_16
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