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
Link To Document :
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