Tapas Si

791 citations
61 papers · 450 · h-index 12

Impact in

  • Neurology top 10%
    • Brain Tumor Detection and Classification
    • Metaheuristic Optimization Algorithms Research
    • AI in cancer detection
    • Evolutionary Algorithms and Applications

Papers in

Tapas Si

52 papers receiving 436 citations

Peers

Tapas Si
Comparison fields: 5 of 79
  • Neurology 66
  • Artificial Intelligence 238
  • Computer Vision and Pattern Recognition 119
  • Radiology, Nuclear Medicine and Imaging 73
  • Health Information Management 14
Replace Jianhua Qu with:
Jianhua Qu China
Tahereh Hassanzadeh Australia
Shanxiong Chen China
Jinze Liu China
Jamal Atif France
Wilfrido Gómez‐Flores Mexico
Shankar Thawkar India
Abeer Saber Egypt
B. S. Murugan India
Tapas Si relative to Jianhua Qu China Jianhua Qu's profile →
Citations per field
00.5×
Jianhua Qu · 1×
Citations per year

Countries citing papers authored by Tapas Si

Since Specialization
Citations

This map shows the geographic impact of Tapas Si'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 Tapas Si with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tapas Si more than expected).

Fields of papers citing papers by Tapas Si

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tapas Si. 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 Tapas Si. The network helps show where Tapas Si may publish in the future.

Co-authors

The 25 scholars most cited alongside Tapas Si, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tapas Si Line = papers co-authored together Tapas Si links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202245
2 202242
3 202134
4 202227
5 201923
6 201819
7 202218
8 202313
9 202312
10 202112
11 201712
12 202211
13 201511
14 201110
15 201410
16 201510
17 20159
18 20238
19 20248
20 20168

About Tapas Si

Tapas Si is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Neurology and Computational Theory and Mathematics, having authored 61 papers that have together received 450 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (18 papers), Medical Image Segmentation Techniques (10 papers), Brain Tumor Detection and Classification (9 papers), Evolutionary Algorithms and Applications (8 papers), Advanced Neural Network Applications (7 papers), Advanced Multi-Objective Optimization Algorithms (7 papers), Neural Networks and Applications (7 papers) and AI in cancer detection (7 papers). The work is most often cited by research in Neurology (66 citations), Artificial Intelligence (238 citations), Computer Vision and Pattern Recognition (119 citations), Radiology, Nuclear Medicine and Imaging (73 citations) and Health Information Management (14 citations). Tapas Si has collaborated with scholars based in India, Brazil and United States. Frequent co-authors include Péricles Miranda, Saurav Mallik, Anup Kumar Bhattacharjee, Nanda Dulal Jana, Utpal Nandi, Pabitra Pal, Rituparna Ghosh, Asim Bikas Das, Biplab Mandal and Sagnik Chakraborty. Their work appears in journals such as Expert Systems with Applications, IEEE Access, Multimedia Tools and Applications, Scientific Reports and Pattern Analysis and Applications.

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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