Vipul Narayan

21 papers receiving 416 citations

Hit Papers

A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease 2023 · 79 citations
790+1+2Years since publication4080120

Peers

Vipul Narayan
Comparison fields: 5 of 98
  • Health Information Management 63
  • Health Informatics 18
  • Computer Networks and Communications 133
  • Computer Vision and Pattern Recognition 74
  • Neurology 30
Replace Ahmed Alahmadi with:
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Shubhi Gupta India
V. D. Ambeth Kumar India
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Countries citing papers authored by Vipul Narayan

Since Specialization
Citations

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

Fields of papers citing papers by Vipul Narayan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Vipul Narayan, 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 Vipul Narayan Line = papers co-authored together Vipul Narayan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Hit paper breakdown →
2023123
2
A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease
Hit paper breakdown →
202379
3 202339
4 202127
5 202323
6 202221
7 202319
8 202317
9 202215
10 202414
11 202014
12 202010
13 20199
14 20237
15 20227
16 20197
17 20236
18 20236
19 20204
20 20193

About Vipul Narayan

Vipul Narayan is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 452 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (10 papers), IoT-based Smart Home Systems (5 papers), Energy Harvesting in Wireless Networks (4 papers), COVID-19 diagnosis using AI (3 papers), AI in cancer detection (3 papers), Security in Wireless Sensor Networks (3 papers), Water Quality Monitoring Technologies (1 paper) and Biometric Identification and Security (1 paper). The work is most often cited by research in Health Information Management (63 citations), Health Informatics (18 citations), Computer Networks and Communications (133 citations), Computer Vision and Pattern Recognition (74 citations) and Neurology (30 citations). Vipul Narayan has collaborated with scholars based in India, China and Poland. Frequent co-authors include A. K. Daniel, Pawan Kumar Mall, Pradeep Kumar Singh, Maria Ganzha, Marcin Paprzycki, Mohammad Faiz, Anurag Gupta, Xuan Tang, V. Saravanan and Arun Pratap Srivastava. Their work appears in journals such as Wireless Personal Communications, Journal of Food Quality, International Journal of Systems Assurance Engineering and Management, Scalable Computing Practice and Experience and Cluster Computing.

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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