Pranjal Kumar

770 citations
12 papers · 343 · 1 hit paper · h-index 6

Impact in

Papers in

Pranjal Kumar

12 papers receiving 331 citations

Pranjal Kumar's Hit Papers

Large language models (LLMs): survey, technical frameworks, and future challenges 2024 · 73 citations
730+1Years since publication204060

Peers

Pranjal Kumar
Comparison fields: 5 of 102
  • Health Informatics 57
  • Health Information Management 19
  • Artificial Intelligence 126
  • Computer Vision and Pattern Recognition 77
  • Human-Computer Interaction 13
Replace Asma Salhi with:
Asma Salhi Australia
Tanoy Debnath Bangladesh
Surendrabikram Thapa United States
Ammar Almasri Jordan
Oğuzhan Topsakal United States
Qizhang Feng United States
Angelo Croatti Italy
Subhankar Ghosh India
Muhammad Azeem Akbar Finland
Wadha Labda Qatar
Pranjal Kumar relative to Asma Salhi Australia Asma Salhi's profile →
Citations per field
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Asma Salhi · 1×
Citations per year

Countries citing papers authored by Pranjal Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Pranjal Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 5 scholars most cited alongside Pranjal 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 Pranjal Kumar Line = papers co-authored together Pranjal Kumar links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2023114
2
Large language models (LLMs): survey, technical frameworks, and future challenges
Hit paper breakdown →
202473
3 202360
4 202249
5 202318
6 202213
7 20214
8 20253
9 20223
10 20232
11 20142
12 20222

About Pranjal Kumar

Pranjal Kumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering and Management Information Systems, having authored 12 papers that have together received 343 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (3 papers), Human Pose and Action Recognition (3 papers), Energy Efficient Wireless Sensor Networks (2 papers), Multimodal Machine Learning Applications (2 papers), Video Surveillance and Tracking Methods (2 papers), Natural Language Processing Techniques (1 paper), COVID-19 diagnosis using AI (1 paper) and IoT-based Smart Home Systems (1 paper). The work is most often cited by research in Health Informatics (57 citations), Health Information Management (19 citations), Artificial Intelligence (126 citations), Computer Vision and Pattern Recognition (77 citations) and Human-Computer Interaction (13 citations). Pranjal Kumar has collaborated with scholars based in India and South Korea. Frequent co-authors include Siddhartha Chauhan, Lalit Kumar Awasthi, Piyush Rawat, Gyanendra Prasad Joshi and Woong Cho. Their work appears in journals such as International Journal of Multimedia Information Retrieval, Archives of Computational Methods in Engineering, Engineering Applications of Artificial Intelligence, Soft Computing and Artificial Intelligence Review.

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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