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

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 behavior 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.
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Umer Khayam, Durre Nayab, Gul Muhammad Khan, S. Ali Mahmud. Features Extraction of Growth Trend in Social Websites Using Non-linear Genetic Programming. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.414-423, ⟨10.1007/978-3-662-44654-6_41⟩. ⟨hal-01391342⟩



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