Pratyusha Rakshit
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 2%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Cognitive Neuroscience
- Co-authors
- Amit KonarAtulya K. NagarSwagatam DasLakhmi C. JainAnca RalescuSriparna SahaAnuradha SahaJosé A. Lozano
- Topics
- Metaheuristic Optimization Algorithms Research (24 papers)Evolutionary Algorithms and Applications (22 papers)Advanced Multi-Objective Optimization Algorithms (15 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- IndiaUnited KingdomUnited States
In The Last Decade
Pratyusha Rakshit
65 papers receiving 740 citations
Peers
Comparison fields: 5 of 84
- Artificial Intelligence 435
- Computational Theory and Mathematics 216
- Computer Vision and Pattern Recognition 163
- Control and Systems Engineering 115
- Cognitive Neuroscience 89
Countries citing papers authored by Pratyusha Rakshit
This map shows the geographic impact of Pratyusha Rakshit'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 Pratyusha Rakshit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pratyusha Rakshit more than expected).
Fields of papers citing papers by Pratyusha Rakshit
This network shows the impact of papers produced by Pratyusha Rakshit. 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 Pratyusha Rakshit. The network helps show where Pratyusha Rakshit may publish in the future.
Co-authorship network of co-authors of Pratyusha Rakshit
This figure shows the co-authorship network connecting the top 25 collaborators of Pratyusha Rakshit. A scholar is included among the top collaborators of Pratyusha Rakshit 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 Pratyusha Rakshit. Pratyusha Rakshit is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | Cognitive Modeling of Human Memory and Learning: A Non-invasive Brain-Computer Interfacing Approach | 1 |
| 3 | 2 | |
| 4 | 8 | |
| 5 | 13 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 2 | |
| 10 | 5 | |
| 11 | 2 | |
| 12 | 11 | |
| 13 | 6 | |
| 14 | 1 | |
| 15 | 22 | |
| 16 | 9 | |
| 17 | 5 | |
| 18 | 5 | |
| 19 | 76 | |
| 20 | 18 |
About Pratyusha Rakshit
Pratyusha Rakshit is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Human-Computer Interaction, having authored 69 papers that have together received 754 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (24 papers), Evolutionary Algorithms and Applications (22 papers) and Advanced Multi-Objective Optimization Algorithms (15 papers). The work is most often cited by research in Computational Theory and Mathematics (216 citations), Artificial Intelligence (435 citations) and Computer Vision and Pattern Recognition (163 citations). Pratyusha Rakshit has collaborated with scholars based in India, United Kingdom and United States. Frequent co-authors include Amit Konar, Atulya K. Nagar, Swagatam Das, Lakhmi C. Jain, Lakhmi C. Jain, Anca Ralescu, Sriparna Saha, Anuradha Saha, José A. Lozano and Aruna Chakraborty. Their work appears in journals such as Scientific Reports, Information Sciences and Artificial Intelligence.
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.