Fundamentals of AI in Aviation - Desklib

This report discusses the current practice and use of AI in the aviation industry, main applications, algorithms and technologies of AI used, UAE national initiative and strategies towards AI, benefits of using AI in the aviation industry, and future perspective towards AI in the aviation industry.

Running head: FUNDAMENTALS OF AI IN AVIATION
Fundamentals of AI in Aviation
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Table of Contents
1. Introduction..................................................................................................................................2
2. Discussion....................................................................................................................................2
2.1 Current Practice and Use of AI in Aviation Industry............................................................2
2.2 Main Applications, Algorithms and Technologies of AI Used.............................................3
2.3 UAE National Initiative and Strategies towards AI..............................................................4
2.4 Benefits of using AI in Aviation Industry.............................................................................4
2.5 Future Perspective towards AI in Aviation Industry.............................................................5
3. Conclusion...................................................................................................................................5
References........................................................................................................................................6

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1. Introduction
The use of AI in the aviation industry is disrupting the ways in which aviation companies
would make use of the huge amount of data, perform their operation and generate their revenue
stream. In recent times, the leading aviation industries in the world are making broad use of the
technological aspects for the purpose of improving the efficiency of operations (Ku et al. 2014).
The other purpose for which AI is mainly used within the industry would be discussed in the
following sections of the report. The report would also discuss the different aspects in relation to
AI in the aviation industry.
2. Discussion
2.1 Current Practice and Use of AI in the Aviation Industry
AI within the aviation industry is currently been used for changing the ways in which
operations are being performed within the sector. The various current practices that are used
within the aviation industry are described as follows:
1. Revenue Management (RM) – It can be defined as the application based on data and
analytics that would be mainly aimed for the purpose of selling a product to those people who
would need it. This facility can be provided at low costs and by making use of the proper form of
the channel (Abdelghany and Abdelghany 2016). The specialists in the fields of AI would make
use of the technology for adjusting prices and defining destinations in specific markets.
2. Aeroplane Maintenance and Air Safety – The aeroplane carriers would deploy
different maintenance solutions that would be able to manage the gathered data in a better
manner. This data would be captured from the aircraft health monitoring systems.

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3. Analysis of Feedback – The airline carriers would be able to learn the pain points and
gather flight experience with the help of data analysis techniques. With the help of using AI, it
could be used for performing feedback analysis and doing research over the market (Čokorilo,
De Luca and Dell’Acqua 2014). This would also allow the airlines for making informed
decisions and thus meet with the expectations of the customers.
4. Optimization of Fuel Efficiency – The airline carrier makes use of AI systems that
are accompanied by machine learning algorithms in order to collect and analyse the data
collected from flight recorders.
2.2 Main Applications, Algorithms and Technologies of AI Used
The primary applications of AI within the airline carrier can be described as follows:
1. To enhance the experience of the airport. Kiosks within the airport are facilitated with
face recognition technology for confirming the identity of traveller based on matching their faces
with the photos in their passport.
2. The airline carriers would be making use of smart and predictive analytics software for
the purpose of maintaining their assets. The airline carrier would be collecting and analyzing the
data based on performing engineering, discussing the market trends and understanding the
preferences of customers.
The AI technology makes use of machine learning algorithms for the purpose of
collecting and analysing data about the flight routes, measuring distances, determining altitudes
and calculating the number of passengers (Ding et al. 2015). With the help of such kind of
technologies, the algorithms would be trained for identifying the patterns and performing
changes.

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2.3 UAE National Initiative and Strategies towards AI
In October 2017, the Government of UAE had launched ‘UAE Strategy for Artificial
Intelligence’. This kind of development of strategy would mark the phase of post-mobile
government. This strategy would highly depend on various forms of future sectors, services and
infrastructural projects (UAE National Program for Artificial Intelligence 2019). The use of AI
as defined by the UAR Government would be affecting the following sectors. These include –
space, health, education, environment, renewable energy, transport, water and technology.
2.4 Benefits of using AI in the Aviation Industry
With the impact of AI made over the aviation industry, the real benefits that are made by
the technology in the industry are:
1. Virtual Assistants – The AI supported virtual assistants would be helping airline
companies to improve their level of productivity and thus the amount of efficiency for the pilots
(Strong 2016). This form of technology is also used by companies to help in improving the
services offered to customers.
2. Aircraft Monitoring – Advanced form of analytics would help companies to monitor
the efficiency and health of aircraft at every point of time (Strong 2016). The AI technology uses
Natural Language Processing (NLP) for scanning millions of logs based on maintenance for
predicting component failures and thus also recommending fixes within the deficit areas.
3. Traveller Experience – These technologies would be calculating the patterns of
weather based on certain routes. This technology could be able to predict the likelihood that
passengers would not swap top another flight carrier. This technology would also predict the
preferences of travellers and thus help arrange a beautiful experience for them.

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2.5 Future Perspective towards AI in the Aviation Industry
The future perspective for the use of AI within the aviation industry is that technology
would solve different critical problems that might arise within the sector. In the future, AI
technology would help the airline industry to identify the passengers at the airport with efficient
use of kiosk service (Freeman et al. 2015). The screening of baggage would also be improved.
Customer service would be another sector in which the industry would be putting more amount
of focus. a high amount of support would also be provided in the area of providing
confidentiality of traveller data.
3. Conclusion
Based on the discussion over the research, it could be concluded that the AI
implementation within the aviation industry would prove to be highly helpful for the sector.
There is a wide form of concern that would be focused on the implementation of technology
within the sector. With the use of AI supported technologies, it would bring a different kind of
opportunities for the sector. Based on providing such kind of benefits, it could be concluded that
society would be highly benefited with the use of such kind of technology. This kind of
technology would also be helpful in developing a sustainable culture among the industry and
bring better services for the people.

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References
Abdelghany, A. and Abdelghany, K., 2016. Modeling applications in the airline industry.
Routledge.
Čokorilo, O., De Luca, M. and Dell’Acqua, G., 2014. Aircraft safety analysis using clustering
algorithms. Journal of Risk Research, 17(10), pp.1325-1340.
Ding, S., Zhao, H., Zhang, Y., Xu, X. and Nie, R., 2015. Extreme learning machine: algorithm,
theory and applications. Artificial Intelligence Review, 44(1), pp.103-115.
Freeman, A.I., Surridge, B.W., Matthews, M., Stewart, M. and Haygarth, P.M., 2015.
Understanding and managing de-icer contamination of airport surface waters: A synthesis and
future perspectives. Environmental Technology & Innovation, 3, pp.46-62.
Ku, A.Y., Dosch, C., Grossman, T.R., Herzog, J.L., Maricocchi, A.F., Polli, D. and Lipkin,
D.M., 2014. Addressing rare-earth element criticality: an example from the aviation
industry. JOM, 66(11), pp.2355-2359.
Strong, A.I., 2016. Applications of artificial intelligence & associated technologies. Science
[ETEBMS-2016], 5(6).
Strong, A.I., 2016. Applications of artificial intelligence & associated technologies. Science
[ETEBMS-2016], 5(6).
UAE National Program for Artificial Intelligence. (2019). UAE National Program for Artificial
Intelligence - AI Strategy 2031. [online] Available at: https://ai.gov.ae/ [Accessed 24 May 2019].