Prachi Jain

486 citations
28 papers · 237 · h-index 7

Impact in

Papers in

Prachi Jain

21 papers receiving 209 citations

Peers

Prachi Jain
Comparison fields: 5 of 58
  • Computational Mathematics 4
  • Signal Processing 59
  • Health Informatics 7
  • Artificial Intelligence 167
  • Management Science and Operations Research 28
Replace Çağatay Demiralp with:
Çağatay Demiralp United States
Pengzhou Zhang China
Kai Gao China
Ben Allison United Kingdom
Austin Waters United States
Bo Shao China
Stanislau Semeniuta Italy
Ehsan Zare Borzeshi Australia
Su-Lin Wu United States
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Citations per field
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Citations per year

Countries citing papers authored by Prachi Jain

Since Specialization
Citations

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

Fields of papers citing papers by Prachi Jain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Prachi Jain, 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 Prachi Jain Line = papers co-authored together Prachi Jain links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202348
2 200247
3 202046
4 201833
5 199212
6 202210
7 20138
8 20225
9 20245
10 20165
11 20123
12
Distributed Speech Recognition
20022
13 20182
14 20112
15 20242
16
A Survey on Single Image Super-resolution
20171
17 20211
18 20221
19 20231
20 20181

About Prachi Jain

Prachi Jain is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems and Biomedical Engineering, having authored 28 papers that have together received 237 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Advanced Data Compression Techniques (6 papers), Video Coding and Compression Technologies (5 papers), Digital Filter Design and Implementation (5 papers), Advanced Graph Neural Networks (4 papers), Natural Language Processing Techniques (4 papers), Speech and Audio Processing (3 papers) and Blockchain Technology Applications and Security (2 papers). The work is most often cited by research in Computational Mathematics (4 citations), Signal Processing (59 citations), Health Informatics (7 citations), Artificial Intelligence (167 citations) and Management Science and Operations Research (28 citations). Prachi Jain has collaborated with scholars based in India, United States and Belgium. Frequent co-authors include Mausam Mausam, Soumen Chakrabarti, Sanjay Sharma, Sachin Kajarekar, Daniel P. W. Ellis, Hynek Heřmanský, Pankaj Kumar, Sunayana Sitaram, Akshay Nambi and Mohamed Ahmed. Their work appears in journals such as IEEE Transactions on Consumer Electronics, Journal of Visualized Experiments, IEEE Transactions on Electromagnetic Compatibility, IETE Journal of Research and Journal of Physics Conference Series.

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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