Partha Pakray

1.7k citations
120 papers · 861 · h-index 15

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Text Analysis Techniques
    • Text and Document Classification Technologies
    • Sentiment Analysis and Opinion Mining
    • Multimodal Machine Learning Applications
    • Handwritten Text Recognition Techniques

Papers in

Partha Pakray

114 papers receiving 763 citations

Peers

Partha Pakray
Comparison fields: 5 of 89
  • Artificial Intelligence 674
  • Computer Vision and Pattern Recognition 194
  • Theoretical Computer Science 8
  • Health Informatics 7
  • Computer Science Applications 27
Replace Luheng He with:
Luheng He United States
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Saïd Ouatik El Alaoui Morocco
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Citations per field
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Citations per year

Countries citing papers authored by Partha Pakray

Since Specialization
Citations

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

Fields of papers citing papers by Partha Pakray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201642
2 201835
3 202125
4 201822
5 202120
6 202419
7 201917
8
A hybrid question answering system based on information retrieval and answer validation
201117
9 202217
10 202316
11 201116
12 201916
13 202315
14 202115
15 202214
16 201914
17 202014
18 202114
19 201414
20 202114

About Partha Pakray

Partha Pakray is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Information Systems and Molecular Biology, having authored 120 papers that have together received 861 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (78 papers), Topic Modeling (77 papers), Multimodal Machine Learning Applications (26 papers), Text and Document Classification Technologies (16 papers), Advanced Text Analysis Techniques (16 papers), Mathematics, Computing, and Information Processing (14 papers), Speech and dialogue systems (6 papers) and Biomedical Text Mining and Ontologies (5 papers). The work is most often cited by research in Artificial Intelligence (674 citations), Computer Vision and Pattern Recognition (194 citations), Theoretical Computer Science (8 citations), Health Informatics (7 citations) and Computer Science Applications (27 citations). Partha Pakray has collaborated with scholars based in India, Mexico and France. Frequent co-authors include Sivaji Bandyopadhyay, Alexander Gelbukh, Ranjita Das, Arnab Kumar Maji, David Pinto, Santanu Pal, Dipankar Das, Badal Soni, Somnath Banerjee and Soujanya Poria. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Sadhana, Theory and applications of categories, Neural Computing and Applications and Arabian Journal for Science and Engineering.

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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