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A 57-year-old female, born in Laos and who had lived in Thailand prior to immigrating to Canada in 1989, was seen by her physician with a chief complaint of cough and dyspnea. Her chest X-ray showed bilateral pulmonary air fluid levels. A fungus, with a diffusible red pigment, tentatively identified asPenicillium marneffei, was isolated from the patient's(More)
Our group participated in the categorization task of the TREC Genomics Track. We introduced and investigated a cluster-based approach for classifying documents. We first clustered the abstracts of the negative training examples based on their term distribution, then built a classifier to distinguish between each cluster and the set of positive examples. The(More)
Genetic programming (GP) and its variants have been extensively applied for modeling of the stock markets. To improve the generalization ability of the model, GP have been hybridized with its own variants (gene expression programming (GEP), multi expression programming (MEP)) or with the other methods such as neural networks and boosting. The generalization(More)
Due to the complexity and uncertainty in the process, the soft computingmethods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic andmulti-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP(More)
Finishing processes are gaining importance over the last decades as manufacturers seek to improve process efficiency while meeting increasingly severe cost and product requirements. A number of researchers have employed conventional modeling techniques such as response surface methodology and linear programming but very few or none has paid attention to the(More)
  • A. Garg, K. Tai
  • 2013 IEEE Symposium on Computational Intelligence…
  • 2013
The evolutionary approach of Genetic Programming (GP) has been applied extensively to model various non-linear systems. The distinct advantage of using GP is that prior assumptions for the selection of a model structure are not required. The GP automatically evolves the optimal model structure and its parameters that best describe the system(More)
Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimizing the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by(More)