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Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

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    This study introduces a deep learning method for aviation passenger profiling using a novel Pythagorean fuzzy deep Boltzmann machine (PFDBM). This advanced approach significantly improves the accuracy of identifying potential threats in large electronic datasets.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Aviation Security

    Background:

    • Classical passenger profiling methods struggle with large electronic datasets.
    • Need for efficient and accurate passenger profiling in commercial aviation.

    Purpose of the Study:

    • To propose a deep learning approach for enhanced passenger profiling.
    • To develop a novel Pythagorean fuzzy deep Boltzmann machine (PFDBM) for improved feature analysis.
    • To create an integrated deep neural network (DNN) for identifying individual and group attackers.

    Main Methods:

    • Utilizing a Pythagorean fuzzy deep Boltzmann machine (PFDBM) with Pythagorean fuzzy numbers.
    • Employing a hybrid algorithm combining gradient-based and evolutionary methods for PFDBM training.
    • Developing a deep neural network (DNN) for passenger classification and group attacker identification.

    Main Results:

    • The proposed PFDBM approach demonstrated superior learning ability and classification accuracy on Air China datasets.
    • The integrated DNN effectively identified group attackers, overcoming individual feature limitations.
    • Achieved significantly higher accuracy compared to existing passenger profiling methods.

    Conclusions:

    • The fuzzy deep learning approach offers a powerful solution for complex pattern analysis in aviation security.
    • This method can be adapted for various challenging data analysis tasks.
    • The PFDBM-based DNN significantly enhances the efficiency and accuracy of passenger profiling.