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A Hybrid Approach for the Prevention of Railway Accidents Based on Artificial Intelligence

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 866)

Abstract

The modes of reasoning which are used in the context of safety analysis and the very nature of knowledge about safety mean that a conventional computing solution is unsuitable and the utilization of artificial intelligence techniques would seem to be more appropriate. Our research has involved three specific aspects of artificial intelligence: knowledge acquisition, machine learning and knowledge based systems (KBS). Development of the knowledge base in a KBS requires the use of knowledge acquisition techniques in order to collect, structure and formalizes knowledge. It has not been possible with knowledge acquisition to extract effectively some types of expert knowledge. Therefore, the use of knowledge acquisition in combination with machine learning appears to be a very promising solution. This paper presents the result of these two research activities which are involved in the methodology of safety analysis of guided rail transport systems.

Keywords

  • Rail transport
  • Safety
  • Accident scenarios
  • Knowledge acquisition
  • Machine learning
  • Expert system

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Correspondence to Habib Hadj-Mabrouk .

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Hadj-Mabrouk, H. (2019). A Hybrid Approach for the Prevention of Railway Accidents Based on Artificial Intelligence. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-00979-3_41

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  • DOI: https://doi.org/10.1007/978-3-030-00979-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00978-6

  • Online ISBN: 978-3-030-00979-3

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