T. Ganesan
Impact in
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- Advanced Multi-Objective Optimization Algorithms
Papers in
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- Metaheuristic Optimization Algorithms Research 10
- Evolutionary Algorithms and Applications 4
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- Advanced Multi-Objective Optimization Algorithms 11
- Co-authors
- Irraivan Elamvazuthi (35 shared papers)Pandian Vasant (30 shared papers)Ku Zilati Ku Shaari (7 shared papers)Lila Iznita Izhar (2 shared papers)Igor Litvinchev (2 shared papers)S. Parasuraman (2 shared papers)M.K.A. Ahamed Khan (3 shared papers)Bander Almutairi (5 shared papers)
In The Last Decade
T. Ganesan
44 papers receiving 428 citations
Peers
Comparison fields: 5 of 83
- Industrial and Manufacturing Engineering 57
- Computational Theory and Mathematics 74
- Control and Systems Engineering 104
- Energy Engineering and Power Technology 13
- Artificial Intelligence 108
Countries citing papers authored by T. Ganesan
This map shows the geographic impact of T. Ganesan'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 T. Ganesan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Ganesan more than expected).
Fields of papers citing papers by T. Ganesan
This network shows the impact of papers produced by T. Ganesan. 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 T. Ganesan. The network helps show where T. Ganesan may publish in the future.
Co-authors
The 18 scholars most cited alongside T. Ganesan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 68 | |
| 2 | 2017 | 35 | |
| 3 | 2011 | 27 | |
| 4 | 2016 | 23 | |
| 5 | 2011 | 20 | |
| 6 | 2020 | 19 | |
| 7 | 2012 | 18 | |
| 8 | 2012 | 18 | |
| 9 | 2014 | 18 | |
| 10 | 2012 | 17 | |
| 11 | 2015 | 15 | |
| 12 | 2017 | 14 | |
| 13 | 2012 | 13 | |
| 14 | 2012 | 13 | |
| 15 | 2015 | 12 | |
| 16 | 2014 | 10 | |
| 17 | 2011 | 9 | |
| 18 | 2013 | 9 | |
| 19 | 2024 | 7 | |
| 20 | Advances in Metaheuristics: Applications in Engineering Systems | 2016 | 7 |
About T. Ganesan
T. Ganesan is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Biomedical Engineering, Astronomy and Astrophysics and Control and Systems Engineering, having authored 49 papers that have together received 448 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (11 papers), Metaheuristic Optimization Algorithms Research (10 papers), Cosmology and Gravitation Theories (7 papers), Process Optimization and Integration (5 papers), Stroke Rehabilitation and Recovery (4 papers), Prosthetics and Rehabilitation Robotics (4 papers), Muscle activation and electromyography studies (4 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (57 citations), Computational Theory and Mathematics (74 citations), Control and Systems Engineering (104 citations), Energy Engineering and Power Technology (13 citations) and Artificial Intelligence (108 citations). T. Ganesan has collaborated with scholars based in Malaysia, Canada and Pakistan. Frequent co-authors include Irraivan Elamvazuthi, Pandian Vasant, Ku Zilati Ku Shaari, Lila Iznita Izhar, Igor Litvinchev, S. Parasuraman, M.K.A. Ahamed Khan, Bander Almutairi, Z. Yousaf and M. Z. Bhatti. Their work appears in journals such as Physics of the Dark Universe, International Journal of Modern Physics D, Mathematical and Computer Modelling, Applied Energy and Soft Computing.
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.