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Journal Article
R. Vyas, Bapat, S., Goel, P., Karthikeyan, M., Tambe, S. S., and Kulkarni, B. D., Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data, IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 1, pp. 27-37, 2018.
A. Hamid, Deshpande, A. S., Badhe, Y. P., Barve, P. P., Tambe, S. S., and Kulkarni, B. D., Biodegradable iron chelate for H2S abatement: modeling and optimization using artificial intelligence strategies, Chemical Engineering Research & Design, vol. 92, no. 6, pp. 1119-1132, 2014.
Y. P. Pandit, Badhe, Y. P., Sharma, B. K., Tambe, S. S., and Kulkarni, B. D., Classification of Indian power coals using K-means clustering and self organizing map neural network, Fuel, vol. 90, no. 1, pp. 339-347, 2011.
S. Tiwary, Ghugare, S. B., Chavan, P. D., Saha, S., Datta, S., Sahu, G., and Tambe, S. S., Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling, Waste and Biomass Valorization, vol. 11, no. 1, pp. 1-19, 2018.
S. Tiwary, Ghugare, S. B., Chavan, P. D., Saha, S., Datta, S., Sahu, G., and Tambe, S. S., Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: , Waste and Biomass Valorization , vol. 11, no. 9, pp. 323–341, 2020.
S. U. Patel, B. Kumar, J., Badhe, Y. P., Sharma, B. K., Saha, S., Biswas, S., Chaudhury, A., Tambe, S. S., and Kulkarni, B. D., Estimation of gross calorific value of coals using artificial neural networks, Fuel, vol. 86, no. 3, pp. 334-344, 2007.
P. B. Karadkar, Kharul, U. K., Bhole, Y. S., Badhe, Y. P., Tambe, S. S., and Kulkarni, B. D., Gas sorption and transport in polyarylates: effect of substituent symmetry and polarity, Journal of Membrane Science, vol. 303, no. 1-2, pp. 244-251, 2007.
S. B. Ghugare and Tambe, S. S., Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies, Journal of the Energy Institute, vol. 90, no. 3, pp. 476-484, 2017.
V. Patil-Shinde and Tambe, S. S., Genetic programming based models for prediction of vapor-liquid equilibrium, Calphad-Computer Coupling of Phase Diagrams and Thermochemistry, vol. 60, pp. 68-80, 2018.
P. Goel, Bapat, S., Vyas, R., Tambe, A., and Tambe, S. S., Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices, Journal of Chromatography A, vol. 1420, pp. 98-109, 2015.
V. Patil-Shinde, Saha, S., Sharma, B. K., Tambe, S. S., and Kulkarni, B. D., High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms, Chemical Engineering Communications, vol. 203, no. 8, pp. 1029-1044, 2016.
P. Goel, Saurabh, K., Patil-Shinde, V., and Tambe, S. S., Prediction of degrees API values of crude oils by use of saturates/aromatics/resins/ asphaltenes analysis: computational-intelligence-based models, SPE Journal, vol. 22, no. 3, pp. 817-853, 2017.
K. Shrinivas, Kulkarni, R. P., Shaikh, S., Ghorpade, R. V., Vyas, R., Tambe, S. S., Ponrathnam, S., and Kulkarni, B. D., Prediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models, International Journal of Chemical Reactor Engineering, vol. 14, no. 1, pp. 361-372, 2016.
M. Karthikeyan, Vyas, R., Tambe, S. S., Radhamohan, D., and Kulkarni, B. D., Role of chemical reactivity and transition state modeling for virtual screening, Combinatorial Chemistry & High Throughput Screening, vol. 18, no. 7, pp. 638-657, 2015.
S. Sharma and Tambe, S. S., Soft-sensor development for biochemical systems using genetic programming, Biochemical Engineering Journal, vol. 85, pp. 89-100, 2014.
R. Vyas, Bapat, S., Jain, E., Tambe, S. S., Karthikeyan, M., and Kulkarni, B. D., Study of applications of machine learning based classification methods for virtual screening of lead molecules, Combinatorial Chemistry & High Throughput Screening, vol. 18, no. 7, pp. 658-672, 2015.