A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Author:

Bala Bisandu Desmond1,Salih Homaid Mohammed2,Moulitsas irene2,Filippone Salvatore3

Affiliation:

1. Department of Computational Engineering Sciences, Cranfield University, UK and Machine Learning and Data Analytics Laboratory, Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, UK

2. Department of Computational Engineering Sciences, Cranfield University, UK and Machine Learning and Data Analytics Laboratory, Digital Aviation Research and Technology Centre(DARTeC), Cranfield University, UK

3. Department of Civil and Computer Engineering, Universita di Roma, Italy and Department of Computational Engineering Sciences, Cranfield Univeristy, United Kingdom

Publisher

ACM

Reference45 articles.

1. Modelling Flight Delays in the Presence of Class Imbalance

2. European Union Aviation Safety Agency , “ Artificial Intelligence Roadmap: A Human-Centric Approach to AI in Aviation,” no . February , pp. 1– 33 , 2020 . European Union Aviation Safety Agency, “Artificial Intelligence Roadmap: A Human-Centric Approach to AI in Aviation,” no. February, pp. 1–33, 2020.

3. X. Zhu and L. Li , “ Improved Flight Time Predictions for Fuel Loading Decisions of Scheduled Flights with a Deep Learning Approach ,” no. 11215119, pp. 1– 12 , 2020 . X. Zhu and L. Li, “Improved Flight Time Predictions for Fuel Loading Decisions of Scheduled Flights with a Deep Learning Approach,” no. 11215119, pp. 1–12, 2020.

4. M. T. Akpinar and M. E. Karabacak , “ Data mining applications in civil aviation sector: State-of-art review ,” CEUR Workshop Proc. , vol. 1852 , pp. 18– 25 , 2017 . M. T. Akpinar and M. E. Karabacak, “Data mining applications in civil aviation sector: State-of-art review,” CEUR Workshop Proc., vol. 1852, pp. 18–25, 2017.

5. Aviation Delay Estimation using Deep Learning

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