@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 = {<p>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.</p>},
	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:000264313200007,
	title = {Autocatalysis: a unit process of biological systems},
	journal = {Chemical Engineering Progress},
	volume = {105},
	number = {3},
	year = {2009},
	month = {MAR},
	pages = {8},
	publisher = {AMER INST CHEMICAL ENGINEERS},
	address = {3 PARK AVE, NEW YORK, NY 10016-5901 USA},
	issn = {0360-7275},
	author = {Gadgil, Chetan J. and Kulkarni, B. D.}
}
@article { ISI:000263980300001,
	title = {Autocatalysis in biological systems},
	journal = {Aiche Journal},
	volume = {55},
	number = {3},
	year = {2009},
	month = {MAR},
	pages = {556-562},
	publisher = {JOHN WILEY \& SONS INC},
	address = {111 RIVER ST, HOBOKEN, NJ 07030 USA},
	issn = {0001-1541},
	doi = {10.1002/aic.11842},
	author = {Gadgil, Chetan J. and Kulkarni, B. D.}
}
@article { ISI:000346322200010,
	title = {Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier},
	journal = {Industrial \& Engineering Chemistry Research },
	volume = {53},
	number = {49},
	year = {2014},
	month = {DEC},
	pages = {18678-18689},
	publisher = {AMER CHEMICAL SOC},
	type = {Article},
	address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
	abstract = {<p>The quality of coalespecially its high ash contentsignificantly affects the performance of coal-based processes. Coal gasification is a cleaner and an efficient alternative to the coal combustion for producing the syngas. The high-ash coals are found in a number of countries, and they form an important source for the gasification. Accordingly, in this study, extensive gasification experiments were conducted in a pilot-plant scale fluidized-bed coal gasifier (FBCG) using high-ash coals from India. Specifically, the effects of eight coal and gasifier process related parameters on the four gasification performance variables, namely CO+H-2 generation rate, syngas production rate, carbon conversion, and heating value of the syngas, were rigorously studied. The data collected from these experiments were used in the FBCG modeling, which was conducted by utilizing two artificial intelligence (AI) strategies namely genetic programming (GP) and artificial neural networks (ANNs). The novelty of the GP formalism is that it searches and optimizes both the form and parameters of an appropriate linear/nonlinear function that best fits the given process data. The original eight-dimensional input space of the FBCG models was reduced to three-dimensional space using the principal component analysis (PCA) and the PCA-transformed three variables were used in the AI-based FBCG modeling. A comparison of the GP and ANN-based models reveals that their output prediction accuracies and the generalization performance vary from good to excellent as indicated by the high training and test set correlation coefficient magnitudes lying between 0.92 and 0.996. This study also presents results of the sensitivity analysis performed to identify those coal and process related parameters, which significantly affect the FBCG process performance.</p>},
	issn = {0888-5885},
	doi = {10.1021/ie500593j},
	author = {Patil-Shinde, Veena and Kukarni, Tejas and Kulkarni, Rahul and Chavan, Prakash D. and Sharma, Tripurari and Sharma, Bijay Kumar and Tambe, Sanjeey S. and Kulkarni, B. D.}
}
