M Kawade

1.5k citations
30 papers · 1.1k indexed · h-index 13

M Kawade

25 papers receiving 1.0k citations

Peers

M Kawade
Comparison fields: 5 of 110
  • Computer Vision and Pattern Recognition 329
  • Cardiology and Cardiovascular Medicine 209
  • Endocrinology, Diabetes and Metabolism 148
  • Cancer Research 126
  • Surgery 332
Replace Manoj Aggarwal with:
Manoj Aggarwal United States
Shaoyan Zhang China
Takuya Inoue Japan
Seongjoon Park South Korea
K. Walker Canada
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Citations per field
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Citations per year

Countries citing papers authored by M Kawade

Since Specialization
Citations

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

Fields of papers citing papers by M Kawade

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20035
2 200220
3 200217
4 199683
5 19966
6 1989296
7 198925
8 198850
9 198711
10 19872
11 198512
12 198527
13
[Studies on Lp(a) lipoprotein (I)--preparation of Lp(a) specific antisera and frequency distributions of Lp(a) concentration in Japanese].
19840
14 198411
15
[Studies on the Lp(a) lipoprotein in Japanese].
19832
16 19729
17
[Free fatty acid].
197218
18 19682
19
An automatic method for determination of alkaline phosphatase in serum using p-nitrophenylphosphate as substrate.
196410
20
Microdetermination of lipids in serum, with notes on the estimation of triglycerides.
19622

About M Kawade

M Kawade is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 30 papers that have together received 1.1k indexed citations. Recurring topics across this work include Face recognition and analysis (5 papers), Lipoproteins and Cardiovascular Health (5 papers), Face and Expression Recognition (5 papers), Cancer, Lipids, and Metabolism (4 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (3 papers), Robot Manipulation and Learning (2 papers), Robotic Mechanisms and Dynamics (2 papers) and Manufacturing Process and Optimization (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (329 citations), Cardiology and Cardiovascular Medicine (209 citations) and Endocrinology, Diabetes and Metabolism (148 citations). M Kawade has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Kazuhiko Makino, Akira Abe, Akio Noma, Shin Maeda, M Seishima, Shihong Lao, Yuan Li, Haizhou Ai, Takayoshi Yamashita and Ito H. Their work appears in journals such as Atherosclerosis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Circulation Research, The American Journal of Cardiology and PEDIATRICS.

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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