Airplanes are widely used as a model of convince by millions of people regularly but the immense cost of air crashes, the study inspects the causes of crashes of aircrafts and crash rate in last minute years. The aim of this proposed examination is to predict the rate and cause of aircraft accident. danger and safety are not always guaranteed within the field of aircraft. There are several reasons of the crashes but in this study, we will examine which is the most occurred reason for the aircraft crash across the globe. The dataset used for this research from For the recommended diagnostic' training dataset, the National Transportation Safety Board (NTSB), which maintains all records of aviation accidents, was employed. The most effective method for using the beneficial knowledge and skills from big data is machine learning. The proposed study aims to develop a prediction model utilising machine learning techniques such random forest and Bayesian classifications, which may be extremely beneficial in aviation safety system. The prediction rate is in the 80–90% range.


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    Titel :

    Aircraft Crash Rate and Cause Detection Using Machine Learning Techniques


    Beteiligte:
    Kumari, Kajal (Autor:in) / Bari, Rakesh Kumar (Autor:in) / Kumar, Sumit (Autor:in) / Chauhan, Alok Singh (Autor:in)


    Erscheinungsdatum :

    2023-09-14


    Format / Umfang :

    529010 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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