02794nas a2200277   4500008004100000022001400041245014800055210006900203260009900272300001400371490000700385520173200392653003002124653002202154653001702176653002202193653002802215653002302243653002002266100002102286700002002307700002202327700001802349700001702367856013202384       2006                        eng d  a1359-511300aOptimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques0 aOptimization of fermentation media for exopolysaccharide product  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcAUG  a1842-18480 v413 a<p>A Lactobacillus strain was isolated from the fermented Eleusine coracana. This strain was characterized as Lactobacillus plantarum and was found to produce an exopolysaccharide (EPS) in quantitative amounts. The objective of the present paper is to determine optimum media composition and inoculum volume for the stated fermentative production of the EPS. A hybrid methodology comprising the Plackett-Burman (PB) design method, artificial neural networks (ANN) and genetic algorithms (GA) was utilized. Specifically, the PB, ANN and GA forrnalisms were used for identifying influential media components, modeling non-linear process and optimizing the process, respectively. More specifically, the PB method was used to determine those media components, which significantly influence the EPS yield. By ignoring the less influential media components, the dimensionality of the input space of the process model could be reduced significantly. Out of the five media components only three were found influential namely, lactose, casein hydrolysate and triammonium citrate. Next, an ANN-based process model was developed for approximating the non-linear relationship between the fermentation operating variables and the EPS yield. The average % error and correlation coefficient for the developed ANN model were 4.8 and 0.999, respectively. The input parameters of ANN model were subsequently optimized using the GA formalism for obtaining maximum EPS yield in batch fermentation. The optimized media composition has predicted the yield of 7.01 g/l. The GA-optimized solution comprising media composition and inoculum volume was verified experimentally and it comes out be 7.14 g/l. (c) 2006 Elsevier Ltd. All rights reserved.</p>10aArtificial neural network10aexopolysaccharide10aFermentation10aGenetic algorithm10aLactobacillus plantarum10aMedia optimization10aPlackett-Burman1 aDesai, Kiran, M.1 aAkolkar, S., K.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aLele, S., S.  uhttps://library.ncl.res.in/content/optimization-fermentation-media-exopolysaccharide-production-lactobacillus-plantarum-using-002577nas a2200277   4500008004100000022001400041245008200055210006900137260009900206300001200305490000700317520157600324653003001900653003201930653003601962100002401998700001802022700002202040700001902062700001602081700002202097700002002119700002302139700002602162856011102188       2007                        eng d  a0016-236100aEstimation of gross calorific value of coals using artificial neural networks0 aEstimation of gross calorific value of coals using artificial ne  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcFEB  a334-3440 v863 a<p>The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels. (c) 2006 Elsevier Ltd. All rights reserved.</p>10aArtificial neural network10agross calorific value (GCV)10aproximate and ultimate analyses1 aPatel, Shagufta, U.1 aKumar, Jeevan1 aBadhe, Yogesh, P.1 aSharma, B., K.1 aSaha, Sujan1 aBiswas, Subhasish1 aChaudhury, Asim1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/estimation-gross-calorific-value-coals-using-artificial-neural-networks02362nas a2200277   4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013101953       2009                        eng d  a1367-543500aMedia optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach0 aMedia optimization for biosurfactant production by rhodococcus e  aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY  a747-7560 v363 a<p>This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques-(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)-were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were similar to 3 and similar to 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were similar to 0.99 and similar to 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.</p>10aArtificial neural network10aBiosurfactant10aGenetic algorithm10aMedia optimization10aResponse surface methodology10aRhodococcus1 aPal, Moumita, P.1 aVaidya, Bhalchandra, K.1 aDesai, Kiran, M.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-001692nas a2200253   4500008004100000022001400041245011600055210006900171260000800240300001400248490000700262520086000269653000601129653000801135653002401143653003001167653001201197653001601209100002001225700002001245700002301265700002101288856012901309       2020                        eng d  a1735-147200aBiomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network0 aBiomass ashes as potent adsorbent for pesticide prediction of ad  cJUN  a3209-32160 v173 a<p>Biomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon-hydrogen-nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity was established and modeled using artificial neural network. 2,4-Dichlorophenoxyacetic acid (2,4-D) a commonly used herbicide is chosen a representative for this study. The artificial neural network model was trained, validated and tested using 35 data sets and was equipped with nine neuron hidden layers having tansig (tangent sigmoid) transfer function and an output layer with purelin (purely linear) transfer function. This model can be used to predict 2,4-D removal efficacy of any biomass ash by knowing its physicochemical properties like C, H, N, Si and BET surface area.</p>
10a210a4-D10aAdsorption capacity10aArtificial neural network10aBiochar10abiomass ash1 aGokhale, N., A.1 aTrivedi, N., S.1 aMandavgane, S., A.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/biomass-ashes-potent-adsorbent-pesticide-prediction-adsorption-capacity-artificial-neural01970nas a2200277   4500008004100000022001400041245010500055210006900160260000800229300001400237490000700251520108800258653003001346653003601376653000801412653001001420653003001430100001701460700002001477700001501497700001401512700001701526700001901543700001401562856011601576       2023                        eng d  a2662-472900aAutomated myocardial infarction and angina detection using second derivative of photoplethysmography0 aAutomated myocardial infarction and angina detection using secon  cSEP  a1259-12690 v463 a<p>
	Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.</p>
10aArtificial neural network10aMyocardial infarction detection10aPPG10aSDPPG10aUnstable angina detection1 aNeha, H., K.1 aSardana, H., K.1 aDahiya, N.1 aDogra, N.1 aKanawade, R.1 aSharma, Y., P.1 aKumar, S.  uhttps://library.ncl.res.in/content/automated-myocardial-infarction-and-angina-detection-using-second-derivative