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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/ifip12/KhayamNKM14,

• 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, http://dblp.uni-trier.de/db/conf/ifip12/aiai2014.html#KhayamNKM14",
• booktitle = "Artificial Intelligence Applications and Innovations - 10th {IFIP} {WG} 12.5 International Conference, {AIAI} 2014, Rhodes, Greece, September 19-21, 2014. Proceedings",
• 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 Technology",
• 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

Citations