Features Extraction of Growth Trend in Social Websites Using Non-linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Umer Khayam and Durre Nayab and Gul Muhammad Khan and 
                 Sahibzada Ali Mahmud",
  title =        "Features Extraction of Growth Trend in Social Websites
                 Using Non-linear Genetic Programming",
  bibdate =      "2014-09-16",
  bibsource =    "DBLP,
  booktitle =    "Artificial Intelligence Applications and Innovations -
                 10th {IFIP} {WG} 12.5 International Conference, {AIAI}
                 2014, Rhodes, Greece, September 19-21, 2014.
  publisher =    "Springer",
  year =         "2014",
  volume =       "436",
  editor =       "Lazaros S. Iliadis and Ilias Maglogiannis and 
                 Harris Papadopoulos",
  isbn13 =       "978-3-662-44653-9",
  pages =        "414--423",
  series =       "IFIP Advances in Information and Communication
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  DOI =          "doi:10.1007/978-3-662-44654-6_41",
  abstract =     "Nonlinear Cartesian Genetic Programming is explored
                 for extraction of features in the growth curve of
                 social web portals and establishment of a prediction
                 model. Daily hit rates of web portals provide the
                 measure of the growth and social establishment
                 behaviour over time. Non-linear Cartesian Genetic
                 Programming approach also termed as CGPANN has unique
                 ability of dealing with the nonlinear data as it
                 provides the flexibility in feature selection, network
                 architecture, topology and other necessary parameters
                 selection to establish the desired prediction model. A
                 number of socially established web portals are used to
                 evaluate the performance of the model over a span of
                 two years. Efficient performance is shown by the system
                 keeping the fact in consideration that only single
                 independent web portal data is used for training the
                 network and the same network was used for the other web
                 portals for their performance evaluation. The system
                 performance is significantly good as the system selects
                 only the desired features from the features presented
                 as input and achieves an optimal network and topology
                 that produce the best possible results.",

Genetic Programming entries for Umer Khayam Durre Nayab Gul Muhammad Khan Sahibzada Ali Mahmud