# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
###### Abstract
The second method demonstrated superior accuracy over the first one, enabling reliable predictions of null motions for maneuvers outside the training dataset. There is considerable research on the applications of AI for enhancing the autonomy of unmanned aerial vehicles (UAVs). The paper [36] offers a comprehensive review of AI-based autonomous UAV networks, underlining their potential to enhance network performance. After examining over 100 articles, the study validates the feasibility and cost-effectiveness of AI-based UAVs for future network designs and identifies areas in need of further research. In [37], the application of digital twins (DTs) for autonomous aircraft is explored, with a particular focus on the importance of data management. The study examines data augmentation through synthetic data generation to overcome real-life data availability issues, particularly highlighting the application on hybrid turbo-shaft engines in drones and helicopters. It demonstrates the effectiveness of using rolling linear regression and Kalman filter algorithms to simulate real-life operational data. The design of a controller using an evolutionary neural network for autonomous quadrotor flight is discussed in [38]. This controller aims to navigate the quadcopter to a designated position while adhering to attitude limitations. The neurocontroller, developed and trained with a custom evolutionary algorithm, proved effective in simulation tests, showing efficient path-following with high precision and speed. The study [39] investigates the use of deep learning methods, particularly through acoustic signals, to detect UAVs. Convolutional Neural Networks were found to be highly effective, with ensemble techniques achieving notable accuracy. This suggests potential for future integration with radar and visual detection methods. In [40], a model for secure communication and classification in drone-enabled emergency monitoring is described. This AI-based system enhances data security through an encryption process and classifies emergency situations using deep learning. The model demonstrated improved performance when tested on a professional dataset. The paper [41] presents an anomaly detection model for UAVs that utilizes an enhanced graph neural network combined with a transformer, graph attention mechanism, and a multi-channel fusion mechanism. Experimental results showed that the Graph Deviation Network surpasses other methods in terms of accuracy and computational efficiency on two datasets from unmanned systems. Numerous key civil aviation organizations have published official documents emphasizing AI's expanding role in the industry and offering recommendations for its adoption. The International Civil Aviation Organization (ICAO) underscored the importance of preparing the next generation of aviation professionals for an AI-enabled future in their 2017 Training Report, stressing the augmentation of aviation training with AI technologies to enhance competencies in utilizing AI tools [42]. Furthermore, ICAO's 2019 Working Paper on artificial intelligence and digitalization in aviation provides guidelines for implementing AI in areas such as air traffic management, advocating for increased R&D, and the development of workforce expertise in AI [43]. The European Union Aviation Safety Agency (EASA) unveiled its Artificial Intelligence Roadmap in 2020 [44]. This strategic document advocates for expanded research into -aviation AI applications, addressing safety and societal risks. It analyzes AI use cases and proposes an AI oversight framework, supporting the development, testing, and approval of aviation AI systems. EUROCONTROL highlighted the need to accelerate AI adoption in air traffic management in its 2020 FLY AI report [45]. Recommendations include establishing a shared AI infrastructure, promoting an \"AI culture\", and enhancing expertise through training and educational programs. EUROCONTROL also formed a task force on AI in aviation to propel progress.