Expertise level identification of air traffic controllers through visual measures and explainable AI

Expertise level identification of air traffic controllers through visual measures and explainable AI

This dissertation explores the integration of eye-tracking metrics, machine learning (ML), and explainable artificial intelligence (XAI) tools to improve air traffic management (ATM) systems. Analyzing eye-tracking metrics like fixation count and duration of air traffic controllers (ATCos), the stud...

Full description

Bibliographic Details
Main Author: Lin, Yen-Po
Other Authors: Chen Chun-Hsien
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2025
Subjects:
Computer and Information Science
Engineering
Air traffic management
Air traffic controllers
Eye-tracking metrics
Machine learning
Explainable artificial intelligence
Shapley additive explanations
Local interpretable model-agnostic explanations
Synthetic minority over-sampling technique
XGBoost model
Human factors in air traffic management
Online Access:https://hdl.handle.net/10356/182335

Internet

https://hdl.handle.net/10356/182335

Similar Items