• 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