Sanjeev S. Tambe

2.3k total citations
85 papers, 1.8k citations indexed

About

Sanjeev S. Tambe is a scholar working on Mechanical Engineering, Control and Systems Engineering and Biomedical Engineering. According to data from OpenAlex, Sanjeev S. Tambe has authored 85 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Mechanical Engineering, 17 papers in Control and Systems Engineering and 16 papers in Biomedical Engineering. Recurrent topics in Sanjeev S. Tambe's work include Advanced Control Systems Optimization (11 papers), Mineral Processing and Grinding (9 papers) and Nonlinear Dynamics and Pattern Formation (8 papers). Sanjeev S. Tambe is often cited by papers focused on Advanced Control Systems Optimization (11 papers), Mineral Processing and Grinding (9 papers) and Nonlinear Dynamics and Pattern Formation (8 papers). Sanjeev S. Tambe collaborates with scholars based in India, United States and Türkiye. Sanjeev S. Tambe's co-authors include Bhaskar D. Kulkarni, B. D. Kulkarni, Yogesh Badhe, Somnath Nandi, Kiran M. Desai, Bijay Kumar Sharma, Savita Kulkarni, Sujan Saha, B.S. Rao and K. Lal Gauri and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and Bioinformatics.

In The Last Decade

Sanjeev S. Tambe

84 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sanjeev S. Tambe India 25 495 436 355 217 203 85 1.8k
Evaristo C. Biscaia Brazil 25 560 1.1× 455 1.0× 450 1.3× 205 0.9× 301 1.5× 122 2.1k
Zainal Ahmad Malaysia 26 691 1.4× 451 1.0× 368 1.0× 191 0.9× 156 0.8× 106 1.9k
Kerry Wilson United Kingdom 2 536 1.1× 613 1.4× 151 0.4× 403 1.9× 166 0.8× 3 3.4k
Surendra Kumar India 29 568 1.1× 346 0.8× 486 1.4× 182 0.8× 340 1.7× 142 2.7k
Argimiro R. Secchi Brazil 25 683 1.4× 300 0.7× 579 1.6× 364 1.7× 340 1.7× 220 2.3k
S. Feyo de Azevedo Portugal 23 220 0.4× 249 0.6× 893 2.5× 379 1.7× 236 1.2× 66 1.7k
Marcio Schwaab Brazil 22 447 0.9× 353 0.8× 282 0.8× 161 0.7× 477 2.3× 54 1.7k
Bhaskar D. Kulkarni India 34 1.0k 2.1× 529 1.2× 326 0.9× 535 2.5× 578 2.8× 105 3.5k
Mario R. Eden United States 26 686 1.4× 453 1.0× 965 2.7× 202 0.9× 361 1.8× 72 2.2k

Countries citing papers authored by Sanjeev S. Tambe

Since Specialization
Citations

This map shows the geographic impact of Sanjeev S. Tambe's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sanjeev S. Tambe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjeev S. Tambe more than expected).

Fields of papers citing papers by Sanjeev S. Tambe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sanjeev S. Tambe. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sanjeev S. Tambe. The network helps show where Sanjeev S. Tambe may publish in the future.

Co-authorship network of co-authors of Sanjeev S. Tambe

This figure shows the co-authorship network connecting the top 25 collaborators of Sanjeev S. Tambe. A scholar is included among the top collaborators of Sanjeev S. Tambe based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sanjeev S. Tambe. Sanjeev S. Tambe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tambe, Sanjeev S., et al.. (2018). Prediction of coal ash fusion temperatures using computational intelligence based models. International Journal of Coal Science & Technology. 5(4). 486–507. 17 indexed citations
2.
Tambe, Sanjeev S., et al.. (2017). Genetic Programming based Drag Model with Improved Prediction Accuracy for Fluidization Systems. International Journal of Chemical Reactor Engineering. 15(2). 3 indexed citations
3.
4.
Ponrathnam, S., et al.. (2016). Adsorption of strontium (II) metal ions using phosphonate-functionalized polymer. Bulletin of Materials Science. 39(6). 1541–1556. 9 indexed citations
5.
Shrinivas, Krishna, Ravindra V. Ghorpade, Renu Vyas, et al.. (2015). 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. 14(1). 361–372. 3 indexed citations
6.
Kamal, K., et al.. (2007). STUDY OF A LABORATORY-SCALE FROTH FLOTATION PROCESS USING ARTIFICIAL NEURAL NETWORKS. Mineral Processing and Extractive Metallurgy Review. 29(2). 130–142. 22 indexed citations
7.
Tambe, Sanjeev S., et al.. (2003). Artificial neural networks for prediction of mycobacterial promoter sequences. Computational Biology and Chemistry. 27(6). 555–564. 30 indexed citations
8.
Iyer, C.S.P., et al.. (2003). Statistical analysis of the physico–chemical data on the coastal waters of Cochin. Journal of Environmental Monitoring. 5(2). 324–327. 32 indexed citations
9.
Sankpal, Narendra V., et al.. (2002). Genetic Programming Assisted Stochastic Optimization Strategies for Optimization of Glucose to Gluconic Acid Fermentation. Biotechnology Progress. 18(6). 1356–1365. 26 indexed citations
10.
Tambe, Sanjeev S., et al.. (2001). Consider genetic algorithms to optimize batch distillation. Hydrocarbon processing. 80(9). 59–66. 5 indexed citations
11.
Tambe, Sanjeev S., et al.. (2000). ANN modeling of DNA sequences: new strategies using DNA shape code. Computers & Chemistry. 24(6). 699–711. 9 indexed citations
12.
Tambe, Sanjeev S., et al.. (1997). Counterpropagation neural networks for fault detection and diagnosis. Computers & Chemical Engineering. 21(2). 177–185. 19 indexed citations
13.
Bhattacharya, D., Sanjeev S. Tambe, & S. Sivasanker. (1997). The influence of reaction temperature on the cracking mechanism of n-hexane over H-ZSM-48. Applied Catalysis A General. 154(1-2). 139–153. 17 indexed citations
14.
Deshpande, Pradeep B., et al.. (1995). Robust nonlinear control with neural networks. Proceedings of the Royal Society of London Series A Mathematical and Physical Sciences. 449(1937). 655–667. 6 indexed citations
15.
Jha, Brajesh Kumar, Sanjeev S. Tambe, & Bhaskar D. Kulkarni. (1995). Estimating Diffusion Coefficients of a Micellar System Using an Artificial Neural Network. Journal of Colloid and Interface Science. 170(2). 392–398. 25 indexed citations
16.
Nair, T. Murlidharan, Sanjeev S. Tambe, & B. D. Kulkarni. (1994). Application of artificial neural networks for prokaryotic transcription terminator prediction. FEBS Letters. 346(2-3). 273–277. 20 indexed citations
17.
Tambe, Sanjeev S., et al.. (1994). Kinetics of SO2‐Dolomite Reaction: Application of Random Pore Model. Journal of Materials in Civil Engineering. 6(1). 65–77. 7 indexed citations
18.
Tambe, Sanjeev S., Vaidyanathan Jayaraman, & B. D. Kulkarni. (1994). Cellular automata modelling of a surface catalytic reaction with Eley-Rideal step: the case of CO oxidation. Chemical Physics Letters. 225(4-6). 303–308. 13 indexed citations
19.
Rajadhyaksha, R.A., et al.. (1990). Correlation effects in counterdiffusion in zeolites. Chemical Engineering Science. 45(7). 1935–1938. 6 indexed citations
20.
Tambe, Sanjeev S., B. D. Kulkarni, & L. K. Doraiswamy. (1985). A stochastic approach to the analysis of chemically reacting systems—V. Estimation of mean passage time for reaching a threshold value using the asymptotic theory of Fokker-Planck processes. Chemical Engineering Science. 40(12). 2297–2302. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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