@article { ISI:000224609100004, title = {SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples}, journal = {Pattern Recognition}, volume = {38}, number = {1}, year = {2005}, month = {JAN}, pages = {41-49}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

A hybrid technique involving symbolization of data to remove noise and use of conditional entropy minima to extract relevant and non-redundant features is proposed in conjunction with support vector machines to obtain more robust classification algorithm. The technique tested on three data sets shows improvements in classification efficiencies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

}, keywords = {classification, conditional entropy, SVM, symbolization}, issn = {0031-3203}, doi = {10.1016/j.patcog.2004.06.002}, author = {Kumar, R. and Jayaraman, Valadi K. and Kulkarni, B. D.} } @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:000244413600017, title = {Spinning wave motion in frontal polymerization}, journal = {Chemical Engineering Science}, volume = {62}, number = {5}, year = {2007}, month = {MAR}, pages = {1448-1455}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

We present a mathematical model describing dynamics of spinning waves which propagate during frontal polymerization reaction taking place in a cylindrical reactor tube. The self-organization of spatio-temporal solution of wave equations due to interplay between thermal diffusion and kinetics gives rise to pattern formation. We begin with a fundamental equation of motion of radial coordinate after defining an asymptotic phase for spinmode. The motion is analyzed near a critical (Hopf) point and a perturbation solution is used to obtain patterns for a case of preparation of poly(2-hydroxyethyl methacrylate) (PHEMA) via frontal polymerization reaction. The model uses the distance as seen in photograph taken using scanning electron microscope (SEM) from which motion begins around core of spiral and calculates pitch of spiral which matches closely with experimental observation in micrograph. Also the model predicts qualitatively the ramp wave and spiral wave motion as observed under SEM. Both these results are reported in open literature for the first time to our best knowledge. (c) 2006 Elsevier Ltd. All rights reserved.

}, keywords = {frontal polymerization, spinning wave, spiral motion, wave dynamics}, issn = {0009-2509}, doi = {10.1016/j.ces.2006.11.037}, author = {Inamdar, Satish R. and Pujari, N. S. and Karimi, I. A. and Ponrathnam, S. and Tayal, R. K. 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:000261281100001, title = {SVR-based prediction of point gas hold-up for bubble column reactor through recurrence quantification analysis of LDA time-series}, journal = {International Journal of Multiphase Flow}, volume = {34}, number = {12}, year = {2008}, month = {DEC}, pages = {1099-1107}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

Recurrence quantification analysis (RQA) has emerged as a useful tool for detecting singularities in nonstationary time-series data. In this paper, we use RQA to analyze the velocity-time data acquired using laser doppler anemometry (LDA) signals in a bubble column reactor for Single point and Multipoint point spargers. The recurring dynamical states within the velocity-time-series occurring due to the bubble and the liquid passage at the point of measurement, are quantified by RQA features (namely \% Recurrence, \% Determinism, \% Laminarity and Entropy), which in turn are regressed using support vector regression (SVR) to predict the point gas hold-up values. It has been shown that SVR-based model for the bubble column reactor can be potentially useful for online prediction and monitoring of the point gas hold-up for different sparging conditions. (C) 2008 Elsevier Ltd. All rights reserved.

}, keywords = {Bubble column, Gas hold-up, LDA, Recurrence quantification analysis (RQA), Support vector regression (SVR)}, issn = {0301-9322}, doi = {10.1016/j.ijmultiphaseflow.2008.07.001}, author = {Gandhi, Ankit B. and Joshi, J. B. and Kulkarni, A. A. and Jayaraman, Valadi K. and Kulkarni, B. D.} } @article { ISI:000254416300052, title = {Synthesis and magnetic properties of barium-calcium hexaferrite particles prepared by sol-gel and microemulsion techniques}, journal = {Journal of Magnetism and Magnetic Materials}, volume = {320}, number = {6}, year = {2008}, month = {MAR}, pages = {1095-1101}, publisher = {ELSEVIER SCIENCE BV}, type = {Article}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

The preparation of W-type hexaferrite particles with the composition BaCa2Fe16O27 by microemulsion and a stearic acid sol - gel method with and without surfactant has been investigated at various sintering temperatures. The structural and magnetic characteristics have been studied by X-ray diffraction ( XRD), a vibrating sample magnetometer ( VSM), scanning electron microscopy ( SEM), thermogravimetric analysis ( TGA), differential scanning calorimetric ( DSC) and Fourier transform infrared ( FTIR) techniques. The effect of sintering temperature on the properties of BaCa2Fe16O27 hexaferrites has been studied. The value of saturation magnetization ( M-s) depends on types of surfactant used. The sample prepared in the presence of polyoxyethylene ( 20) sorbitan monooleat ( Tween 80) shows low saturation magnetization ( M-s 15.10 emu/g), whereas the other sample prepared in the presence of a surfactant cetyltrimethylammonium bromide (CTAB) exhibits high saturation magnetization (M-s = 24.60 emu/g) compared to the normal sample. (C) 2007 Elsevier B. V. All rights reserved.

}, keywords = {BaCa2Fe16O27, hexaferrite, magnetic property, Microemulsion, Sol-gel method, surfactant}, issn = {0304-8853}, doi = {10.1016/j.jmmm.2007.10.032}, author = {Jotania, R. B. and Khomane, Ramdas B. and Chauhan, C. C. and Menon, Shamal K. 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.} } @article { ISI:000296871800013, title = {Sharp cut algorithm for optimization}, journal = {Computers \& Chemical Engineering}, volume = {35}, number = {12}, year = {2011}, month = {DEC}, pages = {2716-2728}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

In this paper, we introduce a new cutting plane algorithm which is computationally less expensive and more efficient than Kelley{\textquoteright}s algorithm. This new cutting plane algorithm uses an intersection cut of three types of cutting planes. We find from numerical results that the global search method formed using successive linear programming and a new intersection set is at least twice as fast as Kelley{\textquoteright}s cutting planes. The necessary mathematical analysis and convergence theorem are provided. The key findings are illustrated via optimization of a cascade of three CSTRs. (C) 2010 Elsevier Ltd. All rights reserved.

}, keywords = {Convergence theorem, Cutting plane, Sharp cut, Successive linear programming}, issn = {0098-1354}, doi = {10.1016/j.compchemeng.2010.11.010}, author = {Inamdar, Satish R. and Karimi, I. A. and Parulekar, S. J. and Kulkarni, B. D.} } @article {44518, title = {Sustainability assessment of brick work for low-cost housing: a comparison between waste based bricks and burnt clay bricks}, journal = {Sustainable Cities and Society}, volume = {37}, year = {2018}, month = {FEB }, pages = {396-406}, type = {Journal Article}, abstract = {Manufacturing of bricks, using clay or fly ash, is one of the major contributors to greenhouse gas emissions as their manufacturing involves utilization of coal and cement. To overcome this limitation, alternative construction materials are developed by author using industrial and agro wastes like cotton mill waste, recycled paper mill waste, and rice husk ash. This work aims at performing a sustainability assessment of burnt clay bricks and bricks made of industrial and agro wastes used for brickwork in a low-cost house. The criteria considered for the assessment are economic, environmental, social, and technical aspects for manufacture of bricks and use of different bricks for brickwork. For the evaluation of environmental criterion, a life cycle assessment (LCA) tool is used. Overall sustainability index (SI) is calculated for alternatives based on the various criteria using MIVES approach. The relative SIs of clay and fly ash bricks, were 0.25 and 0.26, respectively. Overall, bricks made of industrial and agro wastes are found more sustainable with the highest SI for cotton waste bricks (0.94). Sensitivity analysis also confirmed that brickwork from waste based bricks is more sustainable compared to brickwork made from clay brick or fly ash brick.}, keywords = {Life cycle assessment, Low cost housing, Multicriteria decision, Sustainability index, Sustainable construction material}, issn = {22106707}, doi = {10.1016/j.scs.2017.11.025}, author = {Joglekar, S. N.}, editor = {Kharkar, R. A.} }