@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.} } @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: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.} }