@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 = {<p>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.</p>},
	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.}
}
@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 = {<p>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.</p>},
	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.}
}
