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Full-Text Articles in Engineering
Classifıcation Of Survivor/Non-Survivor Passengers In Fatal Aviation Accidents: A Machine Learning Approach, Tüzün Tolga İnan Dr.
Classifıcation Of Survivor/Non-Survivor Passengers In Fatal Aviation Accidents: A Machine Learning Approach, Tüzün Tolga İnan Dr.
International Journal of Aviation, Aeronautics, and Aerospace
The safety concept primarily examines the most fatal (resulting in dead passengers) accidents of aviation history in this study. The primary causes of most fatal accidents are; human, technical, and sabotage/terrorism factors. Although the aviation industry started with the first engine flight in 1903, the safety concept has been examined since the 1950s. The safety concept firstly examined the technical factors, and in the late 1970s, human factors started to analyze. Despite these primary causes, there have different factors that affect accidents. So, the study aims to determine the affecting factors of the most fatal accidents to classify the survivor/non-survivor …
Implementing Artificial Intelligence And Machine Learning Into Advanced Qualification Programs, Jennifer R. Herr
Implementing Artificial Intelligence And Machine Learning Into Advanced Qualification Programs, Jennifer R. Herr
Journal of Aviation/Aerospace Education & Research
Since its start, the Advanced Qualification Program (AQP) has encouraged new and innovative strategies for training airline crewmembers. The foundation of AQP is to train crew the way they fly and to find new and innovative ways to increase safety through training. By using data collected through the AQP process, training methods can be refined and improved. New technologies, such as artificial intelligence (AI) and machine learning can make data analysis and training more effective and efficient. This paper will explore these concepts and how AI and machine learning could be implemented in the AQP process to make training more …
Predictability Improvement Of Scheduled Flights Departure Time Variation Using Supervised Machine Learning, Deepudev Sahadevan, Palanisamy Ponnusamy Dr, Manjunath K. Nelli Mr, Varun P. Gopi Dr
Predictability Improvement Of Scheduled Flights Departure Time Variation Using Supervised Machine Learning, Deepudev Sahadevan, Palanisamy Ponnusamy Dr, Manjunath K. Nelli Mr, Varun P. Gopi Dr
International Journal of Aviation, Aeronautics, and Aerospace
The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air …