@article { ISI:000231744300010,
	title = {Regression models using pattern search assisted least square support vector machines},
	journal = {Chemical Engineering Research and Design},
	volume = {83},
	number = {A8},
	year = {2005},
	month = {AUG},
	pages = {1030-1037},
	publisher = {INST CHEMICAL ENGINEERS},
	type = {Article},
	address = {165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND},
	abstract = {<p>Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.</p>},
	keywords = {equality constraints, LS-SVM, model selection, Optimization, pattern search},
	issn = {0263-8762},
	doi = {10.1205/cherd.03144},
	author = {Patil, N. S. and Shelokar, P. S. and Jayaraman, Valadi K. and Kulkarni, B. D.}
}
@article { ISI:000257228100019,
	title = {Optimal xylanase production using Penicilium janthinellum NCIM 1169: a model based approach},
	journal = {Biochemical Engineering Journal},
	volume = {40},
	number = {2},
	year = {2008},
	month = {JUN},
	pages = {348-356},
	publisher = {ELSEVIER SCIENCE SA},
	address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
	abstract = {<p>Xylanases are an industrially important class of hydrolytic enzymes that degrade xylans. Production of xylanase from a fungal culture by submerged fermentation and optimization of the operating conditions for maximum activity are the two aims of the present study. Penicillium janthinellum NCIM 1169 with Mandels-Weber medium, sugarcane bagassse (40\#) as a carbon source and beef extract as a nitrogen source were used in the experiments. We did 41 experiments to see the effect of variations in carbon, nitrogen source, pH, and inoculum on xylanase activity. This data was then used to build an input/output model using multiple linear regression, back propagation neural network and lazy learning algorithm. It was found that lazy learning model correlated well in mapping input/output data. This model was then utilized as an objective function in genetic algorithm to find the optimal combination of the operating conditions to get the maximum xylanase activity. It was observed that with carbon source, 1.63\%, nitrogen source, 0.16\%, pH, 4.1, and inoculum, 5.5\%, maximum xylanase activity of 28.98 +/- 1.73 U/ml was achieved. (C) 2008 Elsevier B.V. All rights reserved.</p>},
	keywords = {Artificial intelligence, Enzyme activity, Fermentation, Modeling, Optimization, Xylanase},
	issn = {1369-703X},
	doi = {10.1016/j.bej.2008.01.003},
	author = {Meshram, Mukesh and Kulkarni, Abhijit and Jayaraman, Valadi K. and Kulkarni, B. D. and Lele, S. S.}
}
