Rupesh Gupta

2.2k citations
210 papers · 1.3k indexed · h-index 20

Impact in

Papers in

Rupesh Gupta

167 papers receiving 1.3k citations

Peers

Rupesh Gupta
Comparison fields: 5 of 153
  • Statistics and Probability 97
  • Computer Vision and Pattern Recognition 233
  • Health Information Management 49
  • Neurology 83
  • Artificial Intelligence 245
Replace Farid García‐Lamont with:
Farid García‐Lamont Mexico
Saad Sadiq United States
Dalwinder Singh India
Debo Cheng China
En Fan China
Jiawei Luo China
Jyotir Moy Chatterjee India
Yifan Shi China
Samina Khalid Pakistan
Tehmina Khalil Pakistan
Rupesh Gupta relative to Farid García‐Lamont Mexico Farid García‐Lamont's profile →
Citations per field
00.5×6.9×
Farid García‐Lamont · 1×
Citations per year

Countries citing papers authored by Rupesh Gupta

Since Specialization
Citations

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

Fields of papers citing papers by Rupesh Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Rupesh Gupta, 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 Rupesh Gupta Line = papers co-authored together Rupesh Gupta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20250
4 20251
5 20240
6 20242
7 20241
8 20240
9 202324
10 20233
11 20237
12 20230
13 20237
14 20232
15 202255
16
Effect of Inhibitors on the Blood taken from Earthy Surfaces and D.N.A. Profiling in Forensic Cases
20171
17 20160
18
Study of Correlation, Magnitude of Genetic Diversity and Selection Indices in Popular Rice ( Oryza sativa L.) Landraces of Bangladesh
20143
19 20043
20 199427

About Rupesh Gupta

Rupesh Gupta is a scholar working on Computer Vision and Pattern Recognition, Neurology, Analytical Chemistry, Statistics and Probability and Health Information Management, having authored 210 papers that have together received 1.3k indexed citations. Recurring topics across this work include Smart Agriculture and AI (43 papers), AI in cancer detection (17 papers), COVID-19 diagnosis using AI (16 papers), Spectroscopy and Chemometric Analyses (16 papers), Digital Imaging for Blood Diseases (15 papers), Brain Tumor Detection and Classification (14 papers), Leaf Properties and Growth Measurement (10 papers) and Radiomics and Machine Learning in Medical Imaging (10 papers). The work is most often cited by research in Statistics and Probability (97 citations), Computer Vision and Pattern Recognition (233 citations), Health Information Management (49 citations), Neurology (83 citations) and Artificial Intelligence (245 citations). Rupesh Gupta has collaborated with scholars based in India, Canada and United States. Frequent co-authors include Vatsala Anand, Kanwarpartap Singh Gill, Neha Sharma, Avinash Sharma, Rahul Singh, Sheifali Gupta, Anupma Prakash, Rahul Chauhan, Dilip Roy and Subhash C. Kochar. Their work appears in journals such as Scientific Reports, Biometrika, Indian Journal of Science and Technology, IEEE Transactions on Consumer Electronics and Journal of Multivariate Analysis.

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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