Nagendra Kumar

471 citations
36 papers · 255 · h-index 11

Impact in

    • Sentiment Analysis and Opinion Mining
    • Topic Modeling
    • Advanced Text Analysis Techniques
    • Text and Document Classification Technologies
    • Hate Speech and Cyberbullying Detection

Papers in

Nagendra Kumar

31 papers receiving 245 citations

Peers

Nagendra Kumar
Comparison fields: 5 of 61
  • Artificial Intelligence 166
  • Communication 24
  • Computer Vision and Pattern Recognition 49
  • Information Systems 51
  • Biophysics 11
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Citations per year

Countries citing papers authored by Nagendra Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Nagendra Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Nagendra 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 Nagendra Kumar Line = papers co-authored together Nagendra Kumar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202024
2 202321
3 201821
4 202318
5 202218
6 201817
7 202317
8 202315
9 202413
10 201911
11 202410
12 20239
13 20248
14 20247
15 20247
16 20255
17 20235
18 20244
19 20184
20 20234

About Nagendra Kumar

Nagendra Kumar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics and Communication, having authored 36 papers that have together received 255 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (14 papers), Complex Network Analysis Techniques (6 papers), Topic Modeling (6 papers), Multimodal Machine Learning Applications (5 papers), Hate Speech and Cyberbullying Detection (4 papers), Recommender Systems and Techniques (4 papers), Text and Document Classification Technologies (3 papers) and Public Relations and Crisis Communication (3 papers). The work is most often cited by research in Artificial Intelligence (166 citations), Communication (24 citations), Computer Vision and Pattern Recognition (49 citations), Information Systems (51 citations) and Biophysics (11 citations). Nagendra Kumar has collaborated with scholars based in India, United States and Netherlands. Frequent co-authors include Manish Singh, Kuldeep Singh, S.S. Dangi, Manish Kumar Goyal, Anoop Yadav, Shivam Singh, Zhu Li, A. K., Arnav Jain and Matt Coler. Their work appears in journals such as Expert Systems with Applications, Computers and Electronics in Agriculture, Engineering Applications of Artificial Intelligence, Social Network Analysis and Mining and Information Processing & 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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