# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
###### Abstract
The paper [12] explores the growing complexities within air traffic management and the potential of explainable artificial intelligence to enhance the safety and user acceptance of AI systems in the aviation sector. An article [13] suggests the use of long short-term memory neural networks for analyzing aircraft surveillance data. This approach aims to detect conflicts and provide air traffic controllers with improved situational awareness, thereby facilitating the transition to a more automated, efficient, and safer next-generation air traffic management system. The study [14] delves into explainable AI techniques to boost transparency and trust in automated air traffic management systems. It involves training machine learning models on aviation and meteorological data to facilitate real-time risk prediction and generate explanations for air traffic controllers to assess. The paper [15] discusses the necessary requirements such as verification, certification, and user acceptance for the operational deployment of AI in air traffic management. It argues that research into explainable AI and verification should progress concurrently and that certification will become crucial as ground-based systems increasingly automate. Another paper [16] assesses AI's relevance in ATM, introduces the descriptive-predictive-prescriptive model of explainable AI and highlights the need for further research and validation to foster AI systems' acceptance in ATM. The methodology for applying machine learning systems in air traffic control, particularly in a conflict detection tool, is evaluated in [17]. Findings indicate that the methodology lacks time-dependent analyses, suggesting that standard classification and regression metrics may not be universally applicable across systems due to their variability over time. The robust data-driven method for aircraft trajectory prediction using a neural network based on the Generative Adversarial Network framework is presented in [18]. This system is enhanced with Blockchain Ledger Technology for secure storage of predictions, effectively resisting adversarial attacks, as shown in performance evaluations using proposed simulations. Numerous publications focus on the application of AI and machine learning in enhancing aviation safety. A systematic review [19] covers the use of natural language processing (NLP) in aviation safety from 2010 to 2022, detailing specific NLP techniques, their effectiveness, existing challenges, and future potential to boost safety and efficiency. Another study [20] employs NLP and machine learning methods, such as label spreading and support vector machines, to categorize human factor categories from aviation incident reports. Despite the challenges of limited labeled data, the methodology achieves high predictive accuracy. Research [21] utilizes machine learning and data analytics, specifically the specific clustering algorithm, to analyze go-arounds at international airports. It distinguishes between typical and anomalous patterns, noting that anomalous go-arounds often deviate from standard procedures and exhibit higher energy states during the initial approach. The approach introduced in [22] analyzes text-based aviation safety reports using NLP and categorizes them through special tools of clustering and visualization. Applied to the Aviation Safety Reporting System, it identifies 10 major and 31 sub-clusters, providing new insights into aviation safety incidents that are not apparent from existing labels. The paper [23] presents the Safety Analysis of Flight Events methodology, which combines data cleaning, correlation analyses, supervised learning, and visualization to understand flight risk factors better. Applied to commercial airline data, the methodology identifies crucial parameters for specific safety events and underscores the importance of aligning machine learning insights with human interpretations of incidents.