DocumentCode
2926508
Title
Applied Data Mining Approach in Ubiquitous World of Air Transportation
Author
Reza, Saybani Mahmoud ; Wah, Teh Ying ; Lahsasna, Adel
Author_Institution
Fac. of Comput. Sci. & Inf. Technol., Univ. of Malaya, Kuala Lumpur, Malaysia
fYear
2009
fDate
24-26 Nov. 2009
Firstpage
1218
Lastpage
1222
Abstract
Noise is a big problem for people living near airports, therefore the public, airport authorities and pilots are looking for ways to reduce the noise in the vicinity of populated areas. Optimal solution would be flight paths that are farthest from those areas, and worst paths are those, that just go above them. There are two classes of paths, namely optimal and non-optimal ones. This paper is going to use one of successfully used data mining techniques, namely neural network, which is capable of recognizing patterns. We used some coordinates of various flight paths as input for learning purposes of Neural Network, and defined two classes representing the optimal and non-optimal flight paths. The results have shown that this technique is well capable of recognizing the optimal and non-optimal flight paths. This technique can be used to reduce the noise.
Keywords
aerospace computing; data mining; neural nets; ubiquitous computing; air transportation; airport authorities; applied data mining approach; learning purposes; neural network; non-optimal flight paths; optimal flight paths; ubiquitous world; Acoustic noise; Air transportation; Aircraft; Airports; Data mining; Low-frequency noise; Noise measurement; Noise reduction; Pervasive computing; Ubiquitous computing; Data Mining; Neural Networks; Noise Reduction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Sciences and Convergence Information Technology, 2009. ICCIT '09. Fourth International Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4244-5244-6
Electronic_ISBN
978-0-7695-3896-9
Type
conf
DOI
10.1109/ICCIT.2009.255
Filename
5369950
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