A Study over Supervised and Unsupervised learning in predicting the impact of bird strike in Aviation Industry

Venugopal, Srivathsav (2022) A Study over Supervised and Unsupervised learning in predicting the impact of bird strike in Aviation Industry. Masters thesis, Dublin, National College of Ireland.

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Abstract

Various reasons urge people to plan journeys all across the world. Air travel is by far the most popular mode of transportation is because of its numerous advantages. Airlines collisions may occur for various reasons despite their popularity as a form of transportation. One hazard that has constantly concerned the aviation business is bird hits. Large open areas like airport runways and other locations are occupied by birds, resulting in an accident or other emergency actions by the airlines. Predicting damage to aircraft caused by bird hits by using machine learning models and comparing the results of the two subsets of Machine learning, such as Supervised and Unsupervised learning, will be the aim of the research in the end.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Aviation Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	14 Mar 2023 12:51
Last Modified:	14 Mar 2023 12:51
URI:	https://norma.ncirl.ie/id/eprint/6334

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