01481nas a2200253   4500008004100000020001800041022001400059245008600073210006900159260011000228300001200338490000900350520057300359100002100932700001500953700001700968700001500985700002601000700002101026700001601047700002601063700001501089856012301104       2005                        eng d  a3-540-30506-8  a0302-974300aArrhythmia classification using local Holder exponents and support vector machine0 aArrhythmia classification using local Holder exponents and suppo  a Statist Inst. Kolkata, IndiabSpringer-Verlag Berlin, Heidelberger Platz 3, D-14197 Berlin, GermanycDEC  a242-2470 v37763 a<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>1 aJoshi, Aniruddha1 aRajshekhar1 aChandran, S.1 aPhadke, S.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.1 aPal, S., K.1 aBandyopadhyay, Sanjoy1 aBiswas, S.  uhttps://library.ncl.res.in/content/arrhythmia-classification-using-local-holder-exponents-and-support-vector-machine-000536nas a2200145   4500008004100000022001400041245005600055210005500111260007900166300000600245490000800251100002300259700002100282856008700303       2009                        eng d  a0360-727500aAutocatalysis: a unit process of biological systems0 aAutocatalysis a unit process of biological systems  a3 PARK AVE, NEW YORK, NY 10016-5901 USAbAMER INST CHEMICAL ENGINEERScMAR  a80 v1051 aGadgil, Chetan, J.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/autocatalysis-unit-process-biological-systems-000486nas a2200145   4500008004100000022001400041245004000055210004000095260006800135300001200203490000700215100002300222700002100245856007400266       2009                        eng d  a0001-154100aAutocatalysis in biological systems0 aAutocatalysis in biological systems  a111 RIVER ST, HOBOKEN, NJ 07030 USAbJOHN WILEY & SONS INCcMAR  a556-5620 v551 aGadgil, Chetan, J.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/autocatalysis-biological-systems-002720nas a2200229   4500008004100000022001400041245011900055210006900174260007100243300001600314490000700330520185000337100002402187700001902211700002002230700002402250700002202274700002502296700002302321700002102344856012502365       2014                        eng d  a0888-588500aArtificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier0 aArtificial intelligencebased modeling of high ash coal gasificat  a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcDEC  a18678-186890 v533 a<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>1 aPatil-Shinde, Veena1 aKukarni, Tejas1 aKulkarni, Rahul1 aChavan, Prakash, D.1 aSharma, Tripurari1 aSharma, Bijay, Kumar1 aTambe, Sanjeey, S.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/artificial-intelligence-based-modeling-high-ash-coal-gasification-pilot-plant-scale-0