Soumadip Ghosh
- Artificial Intelligence top 10%
- Molecular Biology
- Health Information Management top 5%
- Information Systems
- Computer Vision and Pattern Recognition
- Co-authors
- Amitava NagPartha SarkarDebasree SarkarSushanta BiswasBhaskar GhoshPrithwineel PaulAnjan PalSnehasish Banerjee
- Topics
- Artificial Intelligence in Healthcare (4 papers)Imbalanced Data Classification Techniques (4 papers)Gene expression and cancer classification (4 papers)
- Partner nations
- IndiaUnited StatesChina
In The Last Decade
Soumadip Ghosh
22 papers receiving 276 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 143
- Molecular Biology 46
- Health Information Management 45
- Information Systems 40
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Soumadip Ghosh
This map shows the geographic impact of Soumadip Ghosh'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 Soumadip Ghosh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soumadip Ghosh more than expected).
Fields of papers citing papers by Soumadip Ghosh
This network shows the impact of papers produced by Soumadip Ghosh. 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 Soumadip Ghosh. The network helps show where Soumadip Ghosh may publish in the future.
Co-authorship network of co-authors of Soumadip Ghosh
This figure shows the co-authorship network connecting the top 25 collaborators of Soumadip Ghosh. A scholar is included among the top collaborators of Soumadip Ghosh 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 Soumadip Ghosh. Soumadip Ghosh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 21 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 4 | |
| 8 | 3 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 9 | |
| 12 | 40 | |
| 13 | 2 | |
| 14 | 13 | |
| 15 | 8 | |
| 16 | 33 | |
| 17 | 47 | |
| 18 | NOVEL GRAY SCALE CONVERSION TECHNIQUES BASED ON PIXEL DEPTH | 9 |
| 19 | 10 | |
| 20 | Indian woods : their identification, properties and uses | 63 |
About Soumadip Ghosh
Soumadip Ghosh is a scholar working on Health Information Management, Artificial Intelligence and Management Science and Operations Research, having authored 24 papers that have together received 313 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (4 papers), Imbalanced Data Classification Techniques (4 papers) and Gene expression and cancer classification (4 papers). The work is most often cited by research in Health Information Management (45 citations), Artificial Intelligence (143 citations) and Forestry (8 citations). Soumadip Ghosh has collaborated with scholars based in India, United States and China. Frequent co-authors include Amitava Nag, Partha Sarkar, Debasree Sarkar, Sushanta Biswas, Bhaskar Ghosh, Prithwineel Paul, Anjan Pal, Snehasish Banerjee, Jyoti Prakash Singh and Zhongming Zhao. Their work appears in journals such as IEEE Access, Applied Sciences and Frontiers in Genetics.
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.