# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
###### Abstract
A study [24] develops a methodology employing learning methods and predictive modeling to detect failures due to human factors in the aviation industry. While the approach shows promise, the study calls for larger datasets to enhance predictive accuracy in future research. Research [25] evaluates the potential of generative language models, particularly ChatGPT, for improving aviation safety analyses. By generating incident summaries and identifying human factors from the Aviation Safety Reporting System dataset, the study advocates for a human-in-the-loop system to responsibly utilize these models, highlighting the importance of collaboration and continual enhancement in aviation safety applications. Maintenance is a key area where artificial intelligence is both highly sought after and extensively applicable. Research [26] investigates the use of Convolutional Neural Networks alongside autonomous drones to automate visual inspections for aircraft maintenance. This study builds on existing work, introducing methods that enhance defect detection accuracy, such as specific image augmentations and pre-classification techniques, improving the identification of issues like dents. A novel approach combining machine learning and IoT to predict the thermal performance of aircraft wing anti-icing systems is presented in [27]. Utilizing an artificial neural network, this method has proven more efficient and quicker than traditional computational fluid dynamics approaches, indicating significant potential for aviation applications. The paper [28] reviews the application of statistical and machine learning techniques to analyze aircraft environmental impacts, including fuel burn, emissions, and noise. It summarizes key research themes and representative works, discussing how these methods can further enhance the sustainability of aviation operations. In [29], the article introduces the use of deep neural networks and transfer learning to automatically detect corrosion in aircraft lap joints from images, achieving accuracy comparable to that of trained human operators. This could aid maintenance personnel and facilitate more automated condition-based maintenance. The study [30] explores active vibration control in helicopters, particularly focusing on individual blade control to reduce vibration loads at the hub. By employing various models and methods, such as fuzzy neural networks, the research indicates that this approach could significantly lessen hub vibration loads, providing valuable insights for developing helicopter vibration control laws. Four data-driven frameworks for predicting the exhaust gas temperature baseline of aeroengines are proposed in [31]. These frameworks use data from actual engines and apply machine learning methods, with the Generalized Regression Neural Network model displaying the highest accuracy and efficiency, making it suitable for practical airline use. The article [32] reviews machine learning applications in lithium-ion battery research, particularly for aviation batteries and green aviation technologies. It examines the strengthsand weaknesses of different machine learning approaches to enhance understanding and drive future advancements in the field. Paper [33] showcases the use of a multilayer perceptron neural network to model the transient performance of aero engines, particularly focusing on heat transfer during transient operations. This model, trained on finite element simulation data and refined with real engine measurements, accurately replicates engine thermal transients. A data-driven approach for predicting base pressure in suddenly expanded flows is introduced in [34]. This method, using machine learning models trained on response equations, accurately forecasts base pressure to help optimize base drag in rockets and missiles. The study [35] applies machine learning techniques, specifically a deep neural network and a random forest classifier, to predict null motions in a four-control moment gyroscope used in satellite attitude control.