@conference { ISI:000234856700033, title = {Arrhythmia classification using local Holder exponents and support vector machine}, booktitle = {1st International Conference on Pattern Recognition and Machine Intelligence}, series = {LECTURE NOTES IN COMPUTER SCIENCE}, volume = {3776}, year = {2005}, note = {1st International Conference on Pattern Recognition and Machine Intelligence, Statist Inst Kolkata, Kolkata, INDIA, DEC 20-22, 2005}, month = {DEC}, pages = {242-247}, publisher = {Springer-Verlag Berlin, Heidelberger Platz 3, D-14197 Berlin, Germany}, organization = {Springer-Verlag Berlin, Heidelberger Platz 3, D-14197 Berlin, Germany}, address = { Statist Inst. Kolkata, India}, abstract = {

We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94\% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.

}, isbn = {3-540-30506-8}, issn = {0302-9743}, author = {Joshi, Aniruddha and Rajshekhar and Chandran, S. and Phadke, S. and Jayaraman, Valadi K. and Kulkarni, B. D.}, editor = {Pal, S. K. and Bandyopadhyay, Sanjoy and Biswas, S.} } @article { ISI:000232596300002, title = {Identification of coding and non-coding sequences using local holder exponent formalism}, journal = {Bioinformatics}, volume = {21}, number = {20}, year = {2005}, month = {OCT}, pages = {3818-3823}, publisher = {OXFORD UNIV PRESS}, type = {Article}, address = {GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND}, abstract = {

Motivation: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. Results: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7\%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences.

}, issn = {1367-4803}, doi = {10.1093/bioinformatics/bti639}, author = {Kulkarni, O. C. and Vigneshwar, R. and Jayaraman, Valadi K. and Kulkarni, B. D.} } @article { ISI:000232838800009, title = {Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process}, journal = {Computers \& Chemical Engineering}, volume = {29}, number = {10}, year = {2005}, month = {SEP}, pages = {2128-2133}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

A support vector machine with knowledge incorporation is applied to detect the faults in Tennessee Eastman Process, a benchmark problem in chemical engineering. The knowledge incorporated algorithm takes advantage of the information on horizontal translation invariance in tangent direction of the instances in dataset. This essentially changes the representation of the input data while training the algorithm. These local translations do not alter the class membership of the instances in the dataset. The results on binary as well as multiple fault detection justify the use of knowledge incorporation. (c) 2005 Elsevier Ltd. All rights reserved.

}, keywords = {fault detection, knowledge, support vector machines, Tennessee Eastman Process}, issn = {0098-1354}, doi = {10.1016/j.compchemeng.2005.06.006}, author = {Kulkarni, A. and Jayaraman, Valadi K. and Kulkarni, B. D.} } @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 = {

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.

}, 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: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 { ISI:000238978400001, title = {Improved time series prediction with a new method for selection of model parameters}, journal = {Journal of Physics A-Mathematical and General}, volume = {39}, number = {30}, year = {2006}, month = {JUL}, pages = {L483-L491}, publisher = {IOP PUBLISHING LTD}, type = {Article}, address = {DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND}, abstract = {

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.

}, issn = {0305-4470}, doi = {10.1088/0305-4470/39/30/L01}, author = {Jade, A. M. and Jayaraman, Valadi K. and Kulkarni, B. D.} } @article { ISI:000251244900032, title = {Data-driven dynamic modeling and control of a surface aeration system}, journal = {Industrial \& Engineering Chemistry Research}, volume = {46}, number = {25}, year = {2007}, note = {Joint 6th International Symposium on Catalysis in Multiphase Reactors/5th International Symposium on Multifunctional Reactors (CAMURE-6/ISMR-5-), Pune, INDIA, JAN 14-17, 2007}, month = {DEC}, pages = {8607-8613}, publisher = {AMER CHEMICAL SOC}, type = {Article}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

In this study we have developed a support vector regression (SVR) based data-driven model for predicting two important design parameters of surface aerators, namely, the volumetric mass transfer coefficient (k(L)(a) under bar) and fractional gas hold-up (epsilon(G)). The dynamical state of the surface aerator system was captured by acquiring pressure fluctuation signals (PFSs) at various design and operating conditions. The most informative features from PFS were extracted using the chaos analysis technique, which includes estimation of Lyapunov exponent, correlation dimensions, and Kolmogorov entropy. At similar conditions the values of k(L)(a) under bar and epsilon(G) were also measured. Two different SVR models for predicting the volumetric mass transfer coefficient (k(L)(a) under bar) and overall gas hold-up (epsilon(G)) as a function of chaotic invariants, design parameters, and operating parameters were developed showing test accuracies of 98.8\% and 97.1\%, respectively. Such SVM based models for the surface aerator can be potentially useful on a commercial scale for online monitoring and control of desired process output variables.

}, issn = {0888-5885}, doi = {10.1021/ie0700765}, author = {Gandhi, Ankit B. and Joshi, Jyeshtharaj B. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} } @article { ISI:000251763600015, title = {Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems}, journal = {Chemical Engineering Science}, volume = {62}, number = {24, SI}, year = {2007}, note = {8th International Conference on Gas-Liquid and Gas-Liquid-Solid Reactor Engineering, Indian Inst Technol Delhi, New Delhi, INDIA, DEC 16-19, 2007}, month = {DEC}, pages = {7078-7089}, publisher = {PERGAMON-ELSEVIER SCIENCE LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND}, abstract = {

The objective of this study was to develop a unified data-driven correlation for the overall gas hold-up for various gas-liquid systems using support vector regression (SVR)-based modeling technique. Over the years, researchers have amply quantified the hydrodynamics of bubble column reactors in terms of the overall gas hold-up. In this work, about 1810 experimental points were collected from 40 open sources spanning the years 1965-2007. The model for overall gas hold-up was established as a function of several parameters which include superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, operating temperature, operating pressure and column diameter of the gas-liquid system. For understanding the hold-up behavior, the data used for training the model was grouped into various gas-liquid systems viz., air-water, gas-aqueous viscous liquids, gas-organic liquids, gas-aqueous electrolyte solutions and gas-liquid systems operated over a wide range of pressure. A generalized model established using SVR was evaluated for its performance for various gas-liquid systems. Statistical analysis showed that the proposed generalized SVR-based correlation for overall gas hold-up has prediction accuracy of 97\% with average absolute relative error (\% AARE) of 12.11\%. A comparison of this correlation with the selected system specific correlations in the literature showed that the developed SVR-based correlation significantly gives enhanced prediction of overall gas hold-up. (C) 2007 Published by Elsevier Ltd.

}, keywords = {bubble column reactor, overall gas hold-up, support vector regression}, issn = {0009-2509}, doi = {10.1016/j.ces.2007.07.071}, author = {Gandhi, Ankit B. and Joshi, Jyeshtharaj B. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} } @conference { ISI:000252140200008, title = {Fault diagnosis using dynamic time warping}, booktitle = {Pattern Recognition and Machine Intelligence, Proceedings}, series = {LECTURE NOTES IN COMPUTER SCIENCE}, volume = {4815}, year = {2007}, note = {2nd International Conference on Pattern Recognition and Machine Intelligence, Calcutta, INDIA, DEC 18-22, 2007}, month = {DEC}, pages = {57-66}, publisher = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia}, organization = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia}, address = {Heidelberger Platz 3, D-14197 Berlin, Germany}, abstract = {

Owing to the superiority of Dynamic Time Warping as a similarity measure of time series, it can become an effective tool for fault diagnosis in chemical process plants. However, direct application of Dynamic Time Warping can be computationally inefficient, given the complexity involved. In this work we have tackled this problem by employing a warping window constraint and a Lower Bounding measure. A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies.

}, isbn = {978-3-540-77045-9}, issn = {0302-9743}, author = {Rajshekhar and Gupta, Ankur and Samanta, A. N. and Kulkarni, B. D. and Jayaraman, Valadi K.}, editor = {Ghosh, A. and De, R. K. and Pal, S. K.} } @conference { ISI:000252140200050, title = {Granular support vector machine based method for prediction of solubility of proteins on overexpression in Escherichia coli}, booktitle = {Pattern Recognition and Machine Intelligence, Proceedings}, series = {LECTURE NOTES IN COMPUTER SCIENCE}, volume = {4815}, year = {2007}, note = {2nd International Conference on Pattern Recognition and Machine Intelligence, Calcutta, INDIA, DEC 18-22, 2007}, month = {DEC}, pages = {406-415}, publisher = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia}, organization = {Indian Stat Inst, Machine Intelligence Univ; ISI Ctr Soft Comp Res; Int Assoc Pattern Recognit; Int Ctr Pure \& Appl Math; Web Intelligence Consortium; Yahoo India Res \& Dev; Philips Res Asia}, address = {Heidelberger Platz 3, D-14197 Berlin, Germany}, abstract = {

We employed a granular support vector Machines(GSVM) for prediction of soluble proteins on over expression in Escherichia coli. Granular computing splits the feature space into a set of subspaces (or information granules) such as classes, subsets, clusters and intervals [14]. By the principle of divide and conquer it decomposes a. bigger complex problem into smaller and computationally simpler problems. Each of the granules is then solved independently and all the results are aggregated to form the final solution. For the purpose of granulation association rules was employed. The results indicate that a difficult imbalanced classification problem can be successfully solved by employing GSVM.

}, isbn = {978-3-540-77045-9}, issn = {0302-9743}, author = {Kumar, Pankaj and Jayaraman, Valadi K. and Kulkarni, B. D.}, editor = {Ghosh, A. and De, R. K. and Pal, S. K.} } @article { ISI:000248860300003, title = {Using pseudo amino acid composition to predict protein subnuclear localization: approached with PSSM}, journal = {Pattern Recognition Letters}, volume = {28}, number = {13}, year = {2007}, month = {OCT}, pages = {1610-1615}, publisher = {ELSEVIER SCIENCE BV}, type = {Article}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

Identification of Nuclear protein localization assumes significance as it can provide in depth insight for genome regulation and function annotation of novel proteins. A multiclass SVM classifier with various input features was employed for nuclear protein compartment identification. The input features include factor solution scores and evolutionary information (position specific scoring matrix (PSSM) score) apart from conventional dipeptide composition and pseudo amino acid composition. All the SVM classifiers with different sets of input features performed better than the previously available prediction classifiers. The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou [Shen, H.B., Chou, K.C., 2005, Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337, 752-756] is 71.23\%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods. (c) 2007 Elsevier B.V. All rights reserved.

}, keywords = {factor solution score, multiclass SVM, nuclear protein, PSSM, subnuclear localization}, issn = {0167-8655}, doi = {10.1016/j.patrec.2007.04.001}, author = {Mundra, Piyushkumar and Kumar, Madhan and Kumar, K. Krishna and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} } @article { ISI:000250986500008, title = {Using recurrence quantification analysis descriptors for protein sequence classification with support vector machines}, journal = {Journal of Biomolecular Structure \& Dynamics}, volume = {25}, number = {3}, year = {2007}, month = {DEC}, pages = {289-297}, publisher = {ADENINE PRESS}, type = {Article}, address = {2066 CENTRAL AVE, SCHENECTADY, NY 12304 USA}, abstract = {

In this work, we integrate a non-linear signal analysis method, recurrence quantification analysis (RQA), with the well-known machine-learning algorithm, support vector machines for the binary classification of protein sequences. Two different classification problems were selected, discriminating between aggregating and non-aggregating proteins and mostly disordered and completely ordered proteins, respectively. It has also been shown that classification performance of SVM models improve on selection of the most informative RQA descriptors as SVM input features.

}, issn = {0739-1102}, author = {Mitra, Joydeep and Mundra, Piyushkumar and Kulkarni, B. D. and Jayaraman, Valadi K.} } @article { ISI:000251070500021, title = {Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes}, journal = {European Journal of Operational Research}, volume = {185}, number = {3}, year = {2008}, month = {MAR}, pages = {1213-1229}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

A hybrid methodology, viz., multicanonical jump walk annealing assisted by tabu list (MJWAT) is proposed for solving dynamic optimization problems in chemically reacting systems. This method combines the power of multicanonical sampling with the beneficial features of simulated annealing. Incorporating tabu list further enhances the efficiency of the method. The superior performance of the MJWAT is highlighted with the help of five benchmark case studies. (C) 2006 Elsevier B.V. All rights reserved.

}, keywords = {dynamic optimization, metaheuristics, Monte Carlo simulations, multicanonical algorithms, simulated annealing, tabu conditions}, issn = {0377-2217}, doi = {10.1016/j.ejor.2006.06.049}, author = {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 = {

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.

}, 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.} } @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:000271103500032, title = {Development of correlations for overall gas hold-up, volumetric mass transfer coefficient, and effective interfacial area in bubble column reactors using hybrid genetic algorithm-support vector regression technique: viscous newtonian and non-newtonian liq}, journal = {Industrial \& Engineering Chemistry Research}, volume = {48}, number = {21}, year = {2009}, month = {NOV}, pages = {9631-9654}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

The objective of this study was to develop hybrid genetic algorithm-support vector regression (GA-SVR)-based correlations for overall gas hold-up (epsilon(G)), volumetric mass-transfer coefficient (kt,a), and effective interfacial area (a) in bubble Column reactors for gas-liquid systems employing Viscous Newtonian and non-Newtonian systems as the liquid phase. The hybrid GA-SVR is a novel technique based on the feature 0 generation approach using genetic algorithm (GA). In the present study, GA has been used for nonlinear rescaling of attributes. These, exponentially scaled, are eventually subjected to SVR training The technique is an extension of conventional SVR technique, showing relatively enhanced results For this purpose an extensive literature search was done. From the published literature, 1629 data points for viscous Newtonian and 845 data points for VISCOUS non-Newtonian systems for cc;, 500 data points for viscous Newtonian and 556 data points for viscous non-Newtonian systems for k(L)a, and 208 data points for viscous non-Newtonian systems for a, respectively, were collected These data sets were collected spanning the years 1965-2007 Correlations were developed after taking into account all the parameters affecting epsilon(G), k(1)a, and a such as column and sparger geometry, gas-liquid properties, operating temperature, pressure, and Superficial gas and liquid velocities. The correlations thus developed gave prediction accuracies of 0.994 and 0.999 and average absolute relative errors (AARE) of 3.75 and 1.65\% for viscous Newtonian and non-Newtonian systems for epsilon(G), prediction accuracies of 0.983 and 0.998 and AARE of 8 62 and 1.91\% for viscous Newtonian and non-Newtonian systems for k(1)a, and prediction accuracy of 0.999 and AARE of 1\% for viscous non-Newtonian systems for a, respectively. These correlations also showed much improved results when compared with all the existing correlations proposed in literature. To facilitate their usage, all the hybrid GA-SVR-based correlations have been uploaded on the web link http-//wwwesnips.com/web/UICT-NCL.

}, issn = {0888-5885}, doi = {10.1021/ie801834w}, author = {Gupta, Prashant P. and Merchant, Shamel S. and Bhat, Akshay U. and Gandhi, Ankit B. and Bhagwat, Sunil S. and Joshi, Jyeshtharaj B. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} } @article { ISI:000265781200006, title = {Development of unified correlations for volumetric mass-transfer coefficient and effective interfacial area in bubble column reactors for various gas-liquid systems using support vector regression}, journal = {Industrial \& Engineering Chemistry Research}, volume = {48}, number = {9}, year = {2009}, month = {MAY}, pages = {4216-4236}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

The objective of this study was to develop a unified correlation for the volumetric mass-transfer coefficient (k(L)a) and effective interfacial area (a) in bubble columns for various gas-liquid systems using support vector regression (SVR-) based modeling technique. From the data published in the open literature, 1600 data points from 27 open sources spanning the years 1965-2007 for k(L)a and 1330 data points from 28 open sources spanning the years 1968-2007 for a were collected. Generalized SVR-based models were developed for the relationship between k(L)a (and a) and each design and operating parameters such as column and sparger geometry, gas-liquid physical properties, operating temperature, pressure, superficial gas velocity, and so on. Further, these models for k(L)a and a are available online at http://www.esnips.com/web/UICT-NCL. The proposed generalized SVR-based correlations for k(L)a and a have prediction accuracies of 99.08\% and 98.6\% and average absolute relative errors (AAREs) of 7.12\% and 5.01\%, respectively. Also, the SVR-based correlation provided much improved predictions compared to those obtained using empirical correlations from the literature.

}, issn = {0888-5885}, doi = {10.1021/ie8003489}, author = {Gandhi, Ankit B. and Gupta, Prashant P. and Joshi, Jyeshtharaj B. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} } @article { ISI:000266526200007, title = {Feature selection and classification employing hybrid ant colony optimization/random forest methodology}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {12}, number = {5}, year = {2009}, month = {JUN}, pages = {507-513}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filter-wrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence.

}, issn = {1386-2073}, author = {Patil, Diwakar and Raj, Rahul and Shingade, Prashant and Kulkarni, Bhaskar and Jayaraman, Valadi K.} } @article { ISI:000271103500001, title = {Festschrift in Honor of Dr. B. D. Kulkarni}, journal = {Industrial \& Engineering Chemistry Research}, volume = {48}, number = {21}, year = {2009}, month = {NOV}, pages = {9355-9356}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, issn = {0888-5885}, doi = {10.1021/ie901367r}, author = {Yadav, Ganapati D. and Jayaraman, Valadi K. and Ravikumar, V.} } @article { ISI:000278806800025, title = {Identification of defensins employing recurrence quantification analysis and random forest classifiers}, journal = {Pattern Recognition and Machine Intelligence, Proceedings}, volume = {5909}, year = {2009}, note = {3rd International Conference on Pattern Recognition and Machine Intelligence, IIT Delhi, New Delhi, INDIA, DEC 16-20, 2009}, month = {DEC}, pages = {152-157}, publisher = {ISI Kolkata}, address = {HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY}, abstract = {

Defensins represent a class of antimicrobial peptides synthesized in the body acting against various microbes. In this paper we study defensins using a non-linear signal analysis method Recurrence Quantication Analysis (RQA). We used the descriptors calculated employing RQA for the classification of defensins with Random Forest Classifier. The RQA descriptors were able to capture patterns peculiar to defensins leading to an accuracy rate of 78.12\% using 10-fold cross validation.

}, isbn = {978-3-642-11163-1}, issn = {0302-9743}, author = {Karnik, Shreyas and Prasad, Ajay and Diwevedi, Alok and Sundararajan, V. and Jayaraman, Valadi K.}, editor = {Chaudhury, S. and Mitra, S. and Murthy, C. A. and Sastry, P. S. and Pal, S. K.} } @article { ISI:000278806800024, title = {Identification of N-glycosylation sites with sequence and structural features employing random forests}, journal = {Pattern Recognition and Machine Intelligence, Proceedings}, volume = {5909}, year = {2009}, note = {3rd International Conference on Pattern Recognition and Machine Intelligence, IIT Delhi, New Delhi, INDIA, DEC 16-20, 2009}, month = {DEC}, pages = {146-151}, publisher = {ISI Kolkata}, address = {HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY}, abstract = {

N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control. The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features. The best model has cross validation accuracy rate of 72.81\%.

}, isbn = {978-3-642-11163-1}, issn = {0302-9743}, author = {Karnik, Shreyas and Mitra, Joydeep and Singh, Arunima and Kulkarni, B. D. and Sundarajan, V. and Jayaraman, Valadi K.}, editor = {Chaudhury, S. and Mitra, S. and Murthy, C. A. and Sastry, P. S. and Pal, S. K.} } @article { ISI:000271103500037, title = {Quantitative structure-property relationship (QSPR) prediction of liquid viscosities of pure organic compounds employing random forest regression}, journal = {Industrial \& Engineering Chemistry Research}, volume = {48}, number = {21}, year = {2009}, month = {NOV}, pages = {9708-9712}, publisher = {AMER CHEMICAL SOC}, address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA}, abstract = {

A quantitative structure-property relationship (QSPR) approach was used to develop a predictive model for viscosities of pure organic liquids using a set of 403 compounds that belong to diverse classes of organic chemicals. A pool of 116 descriptors that encode topostructural, topochemical, electrotopological, geometrical, and quantum chemical properties of the organic compounds was used to develop QSPR models, based on the robust Random Forest (RF) regression algorithm. The performance of the algorithm, in terms of correlation coefficients and mean square errors, was determined to be good. The capability of the algorithm to build models and select the most-informative features simultaneously is very useful for several quantitative structure-activity/property relationship tasks. The eight most-dominant features selected by the RF regression algorithm primarily contained predictors that encode characteristics of atoms and groups that form hydrogen bonds, as well as factors involving molecular shape and size.

}, issn = {0888-5885}, doi = {10.1021/ie8018406}, author = {Rajappan, Remya and Shingade, Prashant D. and Natarajan, Ramanathan and Jayaraman, Valadi K.} } @article { ISI:000266525900010, title = {Review on lazy learning regressors and their applications in QSAR}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {12}, number = {4}, year = {2009}, month = {MAY}, pages = {440-450}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e. g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.

}, keywords = {classification, lazy learning, machine learning, Quantitative structure activity relationship (QSAR), regression}, issn = {1386-2073}, author = {Kulkarni, Abhijit J. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.} }