@article { ISI:000238398000019, title = {Optimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques}, journal = {Process Biochemistry}, volume = {41}, number = {8}, year = {2006}, month = {AUG}, pages = {1842-1848}, publisher = {ELSEVIER SCI LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}, abstract = {

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.

}, keywords = {Artificial neural network, exopolysaccharide, Fermentation, Genetic algorithm, Lactobacillus plantarum, Media optimization, Plackett-Burman}, issn = {1359-5113}, doi = {10.1016/j.procbio.2006.03.037}, author = {Desai, Kiran M. and Akolkar, S. K. and Badhe, Yogesh P. and Tambe, S. S. and Lele, S. S.} } @article { ISI:000243799400004, title = {Estimation of gross calorific value of coals using artificial neural networks}, journal = {Fuel}, volume = {86}, number = {3}, year = {2007}, month = {FEB}, pages = {334-344}, publisher = {ELSEVIER SCI LTD}, type = {Article}, address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}, abstract = {

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.

}, keywords = {Artificial neural network, gross calorific value (GCV), proximate and ultimate analyses}, issn = {0016-2361}, doi = {10.1016/j.fuel.2006.07.036}, author = {Patel, Shagufta U. and Kumar, B. Jeevan and Badhe, Yogesh P. and Sharma, B. K. and Saha, Sujan and Biswas, Subhasish and Chaudhury, Asim and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} } @article { ISI:000265398300016, title = {Media optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach}, journal = {Journal of Industrial Microbiology \& Biotechnology}, volume = {36}, number = {5}, year = {2009}, month = {MAY}, pages = {747-756}, publisher = {SPRINGER HEIDELBERG}, address = {TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY}, abstract = {

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.

}, keywords = {Artificial neural network, Biosurfactant, Genetic algorithm, Media optimization, Response surface methodology, Rhodococcus}, issn = {1367-5435}, doi = {10.1007/s10295-009-0547-6}, author = {Pal, Moumita P. and Vaidya, Bhalchandra K. and Desai, Kiran M. and Joshi, Renuka M. and Nene, Sanjay N. and Kulkarni, Bhaskar D.} } @article {46983, title = {Biomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network}, journal = {International Journal of Environmental Science and Technology}, volume = {17}, year = {2020}, month = {JUN}, pages = {3209-3216}, type = {Article}, abstract = {

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.

}, keywords = {2, 4-D, Adsorption capacity, Artificial neural network, Biochar, biomass ash}, issn = {1735-1472}, doi = {10.1007/s13762-020-02645-9, Early Access Date = FEB 2020}, author = {Gokhale, N. A. and Trivedi, N. S. and Mandavgane, S. A. and Kulkarni, B. D.} } @article {48807, title = {Automated myocardial infarction and angina detection using second derivative of photoplethysmography}, journal = {Physical and Engineering Sciences in Medicine}, volume = {46}, year = {2023}, month = {SEP}, pages = {1259-1269}, type = {Article}, abstract = {

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.

}, keywords = {Artificial neural network, Myocardial infarction detection, PPG, SDPPG, Unstable angina detection}, issn = {2662-4729}, doi = {10.1007/s13246-023-01293-w}, author = {Neha, H. K. and Sardana, H. K. and Dahiya, N. and Dogra, N. and Kanawade, R. and Sharma, Y. P. and Kumar, S.} }