Pratik Narang

48 papers receiving 1.9k citations

Hit Papers

FakeBERT: Fake news detection in social media with a BERT-based deep learning approach 2021 · 427 citations
4270+2+4Years since publication100200300400

Peers

Pratik Narang
Comparison fields: 5 of 122
  • Information Systems 792
  • Signal Processing 354
  • Artificial Intelligence 721
  • Computer Vision and Pattern Recognition 442
  • Sociology and Political Science 866
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Zhenguang Liu China
Yuan Tian China
Abdulrahman Alarifi Saudi Arabia
Jeremy Straub United States
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Jian Xu China
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Asiya Khan United Kingdom
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Countries citing papers authored by Pratik Narang

Since Specialization
Citations

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

Fields of papers citing papers by Pratik Narang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach
Hit paper breakdown →
2021427
2
FNDNet – A deep convolutional neural network for fake news detection
Hit paper breakdown →
2020300
3
Drone-surveillance for search and rescue in natural disaster
Hit paper breakdown →
2020253
4 2017161
5 2020119
6 202092
7 202172
8 202156
9 202051
10 202139
11 201439
12 202038
13 201935
14 202131
15 201931
16 202228
17 202124
18 202120
19 201320
20 202118

About Pratik Narang

Pratik Narang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Computer Networks and Communications and Sociology and Political Science, having authored 48 papers that have together received 2.0k indexed citations. Recurring topics across this work include Spam and Phishing Detection (13 papers), Image Enhancement Techniques (11 papers), Misinformation and Its Impacts (9 papers), Network Security and Intrusion Detection (9 papers), Advanced Neural Network Applications (8 papers), Video Surveillance and Tracking Methods (8 papers), Advanced Malware Detection Techniques (8 papers) and Internet Traffic Analysis and Secure E-voting (7 papers). The work is most often cited by research in Information Systems (792 citations), Signal Processing (354 citations), Artificial Intelligence (721 citations), Computer Vision and Pattern Recognition (442 citations) and Sociology and Political Science (866 citations). Pratik Narang has collaborated with scholars based in India, United States and Singapore. Frequent co-authors include Anurag Goswami, Rohit Kumar Kaliyar, Soumendu Sinha, Deepak Garg, Balmukund Mishra, Vipul Kumar Mishra, Vinay Chamola, Murari Mandal, Mukund Lahoti and Kang Hai Tan. Their work appears in journals such as Multimedia Tools and Applications, Neural Computing and Applications, IEEE Transactions on Intelligent Transportation Systems, Computer Communications and IEEE Transactions on Vehicular Technology.

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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