Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

Tetiana Shmelova; Yuliya Sikirda; Nina Rizun; Vitaliy Lazorenko; Volodymyr Kharchenko

doi:10.4018/978-1-7998-5357-2.ch010

Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers

Abstract

This chapter presents the application of new information technology in education for the training of air traffic controllers (ATCs). Machine learning, multi-criteria decision analysis, and text analysis as the methods of artificial intelligence for ATCs training have been described. The authors have made an analysis of the International Civil Aviation Organization documents for modern principles of ATCs education. The prototype of the neural network for evaluating the timeliness and correctness of the decision making by ATCs has been developed. The new theoretical and practical tasks for simulation and pre-simulation training have been obtained using expert judgment method. The methodology for sentiment analyzing the airline customers' opinions has been proposed. In addition, the examples of artificial intelligence systems and expert systems by the authors, students and colleagues from National Aviation University, Ukraine and Gdansk University of Technology, Poland have been proposed.

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Authors (5)

Tetiana Shmelova Prof.
Yuliya Sikirda Dr.
Nina Rizun dr
Vitaliy Lazorenko Dr.
Volodymyr Kharchenko Prof.

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Category:: Monographic publication
Type:: rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Language:: English
Publication year:: 2020
Bibliographic description:: Shmelova T., Sikirda Y., Rizun N., Lazorenko V., Kharchenko V.: Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers// Research Anthology on Reliability and Safety in Aviation Systems, Spacecraft, and Air Transport/ : , 2020, s.237-286
DOI:: Digital Object Identifier (open in new tab) 10.4018/978-1-7998-5357-2.ch010
Verified by:: Gdańsk University of Technology