Ritam Guha
- Artificial Intelligence top 5%
- Metaheuristic Optimization Algorithms Research 10
- Evolutionary Algorithms and Applications 9
- Machine Learning and Data Classification 3
- Machine Learning and Algorithms 2
- Reinforcement Learning in Robotics 2
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- Face and Expression Recognition 3
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- Advanced Multi-Objective Optimization Algorithms 7
- Health Information Management top 10%
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- Aquaculture disease management and microbiota 3
- Co-authors
- Ram SarkarManosij GhoshAjith AbrahamKushal Kanti GhoshNeeraj KumarPawan Kumar SinghVikrant BhatejaSeyedali Mirjalili
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- SHILAP Revista de lepidopterología (2 papers)IEEE Access (1 paper)IEEE Transactions on Evolutionary Computation (2 papers)
- Partner nations
- IndiaUnited StatesAustralia
In The Last Decade
Ritam Guha
22 papers receiving 571 citations
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 404
- Computer Vision and Pattern Recognition 164
- Computational Theory and Mathematics 87
- Health Information Management 22
- Signal Processing 36
Countries citing papers authored by Ritam Guha
This map shows the geographic impact of Ritam Guha'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 Ritam Guha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ritam Guha more than expected).
Fields of papers citing papers by Ritam Guha
This network shows the impact of papers produced by Ritam Guha. 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 Ritam Guha. The network helps show where Ritam Guha may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ritam Guha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 5 | |
| 5 | 2023 | 18 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 9 | |
| 11 | 2021 | 8 | |
| 12 | 2021 | 83 | |
| 13 | 2021 | 7 | |
| 14 | 2020 | 42 | |
| 15 | 2020 | 17 | |
| 16 | 2019 | 13 | |
| 17 | 2019 | 43 | |
| 18 | 2019 | 69 | |
| 19 | 2019 | 13 | |
| 20 | 2019 | 136 |
About Ritam Guha
Ritam Guha is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 24 papers that have together received 594 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (10 papers), Evolutionary Algorithms and Applications (9 papers), Advanced Multi-Objective Optimization Algorithms (7 papers), Machine Learning and Data Classification (3 papers), Face and Expression Recognition (3 papers), Aquaculture disease management and microbiota (3 papers), Machine Learning and Algorithms (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Artificial Intelligence (404 citations), Computer Vision and Pattern Recognition (164 citations) and Computational Theory and Mathematics (87 citations). Ritam Guha has collaborated with scholars based in India, United States and Australia. Frequent co-authors include Ram Sarkar, Manosij Ghosh, Ajith Abraham, Kushal Kanti Ghosh, Neeraj Kumar, Pawan Kumar Singh, Vikrant Bhateja, Seyedali Mirjalili, Debotosh Bhattacharjee and Andrew Tomkins. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Evolutionary Computation.
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.