Ambeshwar Kumar

15 papers receiving 219 citations

Ambeshwar Kumar's Hit Papers

Deep learning for enhanced brain Tumor Detection and classification 2024 · 60 citations
600+1Years since publication204060

Peers

Ambeshwar Kumar
Comparison fields: 5 of 67
  • Health Informatics 15
  • Neurology 85
  • Computer Vision and Pattern Recognition 82
  • Artificial Intelligence 94
  • Health Information Management 10
Replace C. John Sundar with:
C. John Sundar India
Khalid Masood Khan Saudi Arabia
Junaid Tariq Pakistan
S. M. Nuruzzaman Nobel Bangladesh
Prasanalakshmi Balaji Saudi Arabia
Shilpa Choudhary India
Sushovan Chaudhury India
Nitesh Pradhan India
Jiaji Wang China
Mohammed Meknassi Morocco
Ambeshwar Kumar relative to C. John Sundar India C. John Sundar's profile →
Citations per field
00.5×
C. John Sundar · 1×
Citations per year

Countries citing papers authored by Ambeshwar Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Ambeshwar Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Ambeshwar 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 Ambeshwar Kumar Line = papers co-authored together Ambeshwar Kumar links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
Deep learning for enhanced brain Tumor Detection and classification
Hit paper breakdown →
202460
2 201839
3 201931
4 201929
5 202128
6 202112
7 20216
8 20214
9 20224
10 20224
11 20203
12 20223
13 20202
14 20222
15 20211

About Ambeshwar Kumar

Ambeshwar Kumar is a scholar working on Artificial Intelligence, Neurology, Information Systems, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 15 papers that have together received 228 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (7 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced Image Fusion Techniques (2 papers), IoT and Edge/Fog Computing (2 papers), Internet of Things and AI (2 papers), Artificial Intelligence in Healthcare (2 papers), COVID-19 diagnosis using AI (2 papers) and User Authentication and Security Systems (2 papers). The work is most often cited by research in Health Informatics (15 citations), Neurology (85 citations), Computer Vision and Pattern Recognition (82 citations), Artificial Intelligence (94 citations) and Health Information Management (10 citations). Ambeshwar Kumar has collaborated with scholars based in India, United States and Indonesia. Frequent co-authors include R. Manikandan, Amir H. Gandomi, Jafar A. Alzubi, Monika Agarwal, Geeta Rani, Rizwan Patan, Mehdi Gheisari, Omar A. Alzubi, Utku Köse and Deepak Gupta. Their work appears in journals such as Wireless Personal Communications, Results in Engineering, Applied Soft Computing, IEEE Transactions on Engineering Management and ACM Transactions on Multimedia Computing Communications 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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