Prema Daigavane

498 citations
47 papers · 283 · h-index 10

Impact in

Papers in

Prema Daigavane

37 papers receiving 265 citations

Peers

Prema Daigavane
Comparison fields: 5 of 47
  • Automotive Engineering 76
  • Control and Systems Engineering 100
  • Electrical and Electronic Engineering 173
  • Safety, Risk, Reliability and Quality 23
  • Computer Vision and Pattern Recognition 46
Replace Haixu Yang with:
Haixu Yang China
Farhan A. Salem Saudi Arabia
Sook-Chin Yip Malaysia
A.C. Renfrew United Kingdom
Bansilal Bairwa India
Zhaoyun Zhang China
Pallavi Choudekar India
Subho Paul India
Anuj Abraham India
Mohannad Jabbar Mnati Iraq
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Citations per field
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Citations per year

Countries citing papers authored by Prema Daigavane

Since Specialization
Citations

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

Fields of papers citing papers by Prema Daigavane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 7 scholars most cited alongside Prema Daigavane, 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 Prema Daigavane Line = papers co-authored together Prema Daigavane links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201037
2 201729
3 202216
4 201816
5 201115
6 202114
7 201913
8 201811
9 201711
10 201611
11 20159
12 20238
13 20227
14 20186
15 20246
16 20196
17 20236
18 20196
19 20226
20 20096

About Prema Daigavane

Prema Daigavane is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering, Automotive Engineering, Computer Vision and Pattern Recognition and Safety, Risk, Reliability and Quality, having authored 47 papers that have together received 283 indexed citations. Recurring topics across this work include Advanced Battery Technologies Research (9 papers), Power Quality and Harmonics (8 papers), Power System Optimization and Stability (7 papers), Electric Vehicles and Infrastructure (6 papers), Electric and Hybrid Vehicle Technologies (5 papers), Power Systems Fault Detection (5 papers), Microgrid Control and Optimization (4 papers) and Wind Turbine Control Systems (3 papers). The work is most often cited by research in Automotive Engineering (76 citations), Control and Systems Engineering (100 citations), Electrical and Electronic Engineering (173 citations), Safety, Risk, Reliability and Quality (23 citations) and Computer Vision and Pattern Recognition (46 citations). Prema Daigavane has collaborated with scholars based in India, Japan and Romania. Frequent co-authors include Preeti Bajaj, S. P. Gawande, Swapnil Khubalkar, Rahul Agrawal, Bidyadhar Subudhi, Nikhil and Gunasekaran Kumar. Their work appears in journals such as JOURNAL OF CHEMICAL ENGINEERING OF JAPAN, International Journal of Electrical Engineering Education, Canadian Journal of Electrical and Computer Engineering, International journal of intelligent engineering and systems and Journal of The Institution of Engineers (India) Series B.

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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