@article {24}, title = {Soft-sensor development for fed-batch bioreactors using support vector regression}, journal = {Biochemical Engineering Journal}, volume = {27}, number = {3}, year = {2006}, note = {Conference on Atomic Molecular and Optical Physics, Calcutta, INDIA, DEC 13-15, 2005}, month = {JAN}, pages = {225-239}, publisher = {Indian Assoc Cultivat Sci}, type = {Article}, address = {DR K S KRISHNAN MARG, NEW DELHI 110 012, INDIA}, abstract = {

In the present paper, a state-of-the-art machine learning based modeling formalism known as "support vector regression (SVR)", has been introduced for the soft-sensor applications in the fed-batch processes. The SVR method possesses a number of attractive properties such as a strong statistical basis, convergence to the unique global minimum and an improved generalization performance by the approximated function. Also, the structure and parameters of an SVR model can be interpreted in terms of the training data. The efficacy of the SVR formalism for the soft-sensor development task has been demonstrated by considering two simulated bio-processes namely, invertase and streptokinase. Additionally, the performance of the SVR based soft-sensors is rigorously compared with those developed using the multilayer perceptron and radial basis function neural networks. The results presented here clearly indicate that the SVR is an attractive alternative to artificial neural networks for the development of soft-sensors in bioprocesses.

}, keywords = {deprotection, Microwave, stannous chloride, tert-butyldimethylsilyl ethers}, isbn = {978-981-270-379-8}, issn = {0376-4699}, doi = {10.1016/j.bej.2005.08.002}, author = {Desai, Kiran M. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000252354300005, title = {Study of a laboratory-scale froth flotation process using artificial neural networks}, journal = {Mineral Processing and Extractive Metallurgy Review}, volume = {29}, number = {2}, year = {2008}, month = {DEC}, pages = {130-142}, publisher = {TAYLOR \& FRANCIS INC}, address = {325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USA}, abstract = {

A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

}, keywords = {back propagation algorithm, froth flotation, laboratory-scale, neural network}, issn = {0882-7508}, doi = {10.1080/08827500701421912}, author = {Kalyani, V. K. and Pallavika and Chaudhuri, Sanjay and Charan, T. Gouri and Haldar, D. D. and Kamal, K. P. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} } @article { ISI:000285027100017, title = {Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence}, journal = {Journal of Biosciences}, volume = {35}, number = {4}, year = {2010}, month = {DEC}, pages = {617-627}, publisher = {INDIAN ACAD SCIENCES}, address = {C V RAMAN AVENUE, SADASHIVANAGAR, P B \#8005, BANGALORE 560 080, INDIA}, abstract = {

Exploitation of microbial wealth, of which almost 95\% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90\% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

}, keywords = {Curvilinear component analysis, Principal component analysis, self-organizing maps}, issn = {0250-5991}, doi = {10.1007/s12038-010-0070-y}, author = {Raje, D. V. and Purohit, H. J. and Badhe, Yogesh P. and Tambe, S. S. and Kulkarni, B. D.} }