Sanjeev S. Tambe

Sanjeev S. Tambe, Pradeep B. Deshpande, Bhaskar D. Kulkarni, 1995, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis

Renu Vyas, Sanjeev S. Tambe, Muthukumarasamy Karthikeyan, 2016, Comput. Biol. Chem..

Artificial neural networks for prediction of mycobacterial promoter sequences

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Rupali N. Kalate, 2003, Comput. Biol. Chem..

Analysis of transcription control signals using artificial neural networks

Sanjeev S. Tambe, Bhaskar D. Kulkarni, T. M. Nair, 1995, Comput. Appl. Biosci..

Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Somnath Nandi, 2004 .

Soft-sensor development for fed-batch bioreactors using support vector regression

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Y. P. Badhe, 2006 .

Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Renu Vyas, Purva Goel, Sanjeev S. Tambe, 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Analysis of DNA curvature using artificial neural networks

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Rupali V. Parbhane, 1998, Bioinform..

Development of genetic programming based softsensor model for styrene polymerization process and its application in model based control

Sanjeev S. Tambe, Suhas B. Ghugare, S. Tambe, 2016, 2016 Indian Control Conference (ICC).

Estimation of gross calorific value of coals using artificial neural networks

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Y. P. Badhe, 2007 .

The Removal of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: Computational intelligence based reaction modeling and optimization

Sanjeev S. Tambe, Khudbudin Mulani, Veena Patil-Shinde, 2016 .

Hydroxylation of Phenol to Dihydroxybenzenes: Development of Artificial Neural-Network-Based Process Identification and Model Predictive Control Strategies for a Pilot Plant Scale Reactor

Sanjeev S. Tambe, Bhaskar D. Kulkarni, B. Kulkarni, 1998 .

Estimating diffusion coefficients of a micellar system using an artificial neural network

Sanjeev S. Tambe, Bhaskar D. Kulkarni, B. Kulkarni, 1995 .

Artificial intelligence-based modeling of high ash coal gasification in a pilot plant scale fluidized bed gasifier

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Veena Patil-Shinde, 2014 .

Artificial neural‐network‐assisted stochastic process optimization strategies

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Somnath Nandi, 2001 .

Soft-sensor development for biochemical systems using genetic programming

Sanjeev S. Tambe, S. Tambe, Suraj Sharma, 2014 .

Reaction Modeling and Optimization Using Neural Networks and Genetic Algorithms: Case Study Involving TS-1-Catalyzed Hydroxylation of Benzene

Sanjeev S. Tambe, Bhaskar D. Kulkarni, Somnath Nandi, 2002 .

Intermittency route to chaos in a periodically forced model reaction system

Sanjeev S. Tambe, Bhaskar D. Kulkarni, 1993 .

Prediction of all India summer monsoon rainfall using error-back-propagation neural networks

Sanjeev S. Tambe, Bhaskar D. Kulkarni, B. Kulkarni, 1997 .

Optimization of fermentation media for exopolysaccharide production from Lactobacillus plantarum using artificial intelligence-based techniques

Sanjeev S. Tambe, Y. P. Badhe, Smita S. Lele, 2006 .