Ayush Kumar

683 citations
53 papers · 437 · h-index 11

Impact in

Papers in

Ayush Kumar

47 papers receiving 411 citations

Peers

Ayush Kumar
Comparison fields: 5 of 72
  • Human-Computer Interaction 53
  • Artificial Intelligence 217
  • Computer Vision and Pattern Recognition 119
  • Signal Processing 54
  • Computer Networks and Communications 90
Replace Manuel Huber with:
Manuel Huber Germany
Bingyan Liu China
Shingchern D. You Taiwan
Kun Zhao China
Tomoki Yoshihisa Japan
Minghao Wang China
A. Pounds-Cornish United Kingdom
Abas Md Said Malaysia
Ahmad Sami Al‐Shamayleh Jordan
Ayush Kumar relative to Manuel Huber Germany Manuel Huber's profile →
Citations per field
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Manuel Huber · 1×
Citations per year

Countries citing papers authored by Ayush Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Ayush Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Hybrid Deep Learning Architecture for Sentiment Analysis
201679
2 202253
3 201649
4 201535
5 202217
6 201216
7 201913
8 201911
9 201811
10 201210
11 201810
12 20169
13 20169
14 20199
15 20218
16 20197
17 20187
18 20197
19 20236
20 20186

About Ayush Kumar

Ayush Kumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Computer Networks and Communications and Signal Processing, having authored 53 papers that have together received 437 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (11 papers), Data Visualization and Analytics (9 papers), Image and Signal Denoising Methods (7 papers), Advanced Image Processing Techniques (6 papers), Topic Modeling (6 papers), Advanced Vision and Imaging (5 papers), IoT and Edge/Fog Computing (4 papers) and Digital Media Forensic Detection (4 papers). The work is most often cited by research in Human-Computer Interaction (53 citations), Artificial Intelligence (217 citations), Computer Vision and Pattern Recognition (119 citations), Signal Processing (54 citations) and Computer Networks and Communications (90 citations). Ayush Kumar has collaborated with scholars based in India, United States and Netherlands. Frequent co-authors include Asif Ekbal, Pushpak Bhattacharyya, Chris Biemann, Michael Burch, Teng Joon Lim, Klaus Mueller, Daniel Weiskopf, Anil Kumar Tiwari, Sunil Jaiswal and Vinit Jakhetiya. Their work appears in journals such as IEEE Wireless Communications Letters, IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, Computers in Biology and Medicine, Computers & Security and International Journal of Systems Assurance Engineering and Management.

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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