Sumit Kumar

1.2k citations
67 papers · 845 · h-index 14

Impact in

  • Software top 10%
    • Software Testing and Debugging Techniques
  • Demography top 5%
    • Insurance, Mortality, Demography, Risk Management

Papers in

Sumit Kumar

52 papers receiving 796 citations

Peers

Sumit Kumar
Comparison fields: 5 of 116
  • Software 55
  • Demography 107
  • Computer Vision and Pattern Recognition 146
  • Computer Networks and Communications 153
  • Artificial Intelligence 214
Replace Ning Gui with:
Ning Gui China
José Palma Spain
Ebrahim Mahdipour Iran
Feras A. Batarseh United States
Nor Samsiah Sani Malaysia
Richard Millham South Africa
Janez Bešter Slovenia
Matthew O. Adigun South Africa
Monika Mangla India
Juan Ramón Rico-Juan Spain
Sumit Kumar relative to Ning Gui China Ning Gui's profile →
Citations per field
00.5×5.5×
Ning Gui · 1×
Citations per year

Countries citing papers authored by Sumit Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Sumit Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018143
2 2021107
3 2011105
4 202074
5 201943
6 201230
7 202123
8 201923
9 202223
10 202218
11 202218
12 201916
13 201816
14 202013
15 201713
16 202412
17 202112
18 202111
19 202210
20 20229

About Sumit Kumar

Sumit Kumar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Electrical and Electronic Engineering and Information Systems, having authored 67 papers that have together received 845 indexed citations. Recurring topics across this work include Software Testing and Debugging Techniques (8 papers), AI in cancer detection (7 papers), Metaheuristic Optimization Algorithms Research (7 papers), Software Reliability and Analysis Research (7 papers), Digital Imaging for Blood Diseases (6 papers), Energy Load and Power Forecasting (5 papers), Medical Image Segmentation Techniques (4 papers) and Neural Networks and Applications (4 papers). The work is most often cited by research in Software (55 citations), Demography (107 citations), Computer Vision and Pattern Recognition (146 citations), Computer Networks and Communications (153 citations) and Artificial Intelligence (214 citations). Sumit Kumar has collaborated with scholars based in India, United Arab Emirates and United Kingdom. Frequent co-authors include Mukesh Saraswat, Vineet Kansal, Motahar Reza, Priti Bansal, Avinash Chandra Pandey, Raju Pal, Himanshu Mittal, Dilip Kumar Yadav, Marwa Khalaf-Allah and Kevin Dowd. Their work appears in journals such as Soft Computing, Multimedia Tools and Applications, Contrast Media & Molecular Imaging, Computers & Industrial Engineering and IEEE Robotics & Automation Magazine.

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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