Nagendra Kumar

471 total citations
36 papers, 255 citations indexed

About

Nagendra Kumar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Nagendra Kumar has authored 36 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 7 papers in Information Systems. Recurrent topics in Nagendra Kumar's work include Sentiment Analysis and Opinion Mining (14 papers), Topic Modeling (6 papers) and Complex Network Analysis Techniques (6 papers). Nagendra Kumar is often cited by papers focused on Sentiment Analysis and Opinion Mining (14 papers), Topic Modeling (6 papers) and Complex Network Analysis Techniques (6 papers). Nagendra Kumar collaborates with scholars based in India, United States and Netherlands. Nagendra Kumar's co-authors include Manish Singh, Kuldeep Singh, S.S. Dangi, Shivam Singh, Manish Kumar Goyal, Anoop Yadav, Matt Coler, A. K., Vijay Laxmi and Harekrishna Yadav and has published in prestigious journals such as Expert Systems with Applications, Applied Soft Computing and Computers and Electronics in Agriculture.

In The Last Decade

Nagendra Kumar

31 papers receiving 245 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nagendra Kumar India 11 166 51 49 40 24 36 255
R Rajasree India 5 261 1.6× 104 2.0× 34 0.7× 53 1.3× 12 0.5× 17 353
Ahmed Abbasi Pakistan 7 129 0.8× 47 0.9× 42 0.9× 41 1.0× 7 0.3× 9 215
Hui Yin China 8 139 0.8× 45 0.9× 25 0.5× 52 1.3× 10 0.4× 19 257
Shufeng Xiong China 9 282 1.7× 78 1.5× 16 0.3× 57 1.4× 4 0.2× 27 369
Silviu Paun United Kingdom 9 299 1.8× 32 0.6× 38 0.8× 29 0.7× 17 0.7× 19 324
Udayan Ghose India 8 124 0.7× 46 0.9× 18 0.4× 17 0.4× 7 0.3× 32 232
Na Cheng China 7 169 1.0× 105 2.1× 55 1.1× 32 0.8× 13 0.5× 18 279
Geetanjali Garg India 6 251 1.5× 70 1.4× 24 0.5× 34 0.8× 16 0.7× 7 324
Kiyonori Ohtake Japan 9 232 1.4× 32 0.6× 22 0.4× 32 0.8× 50 2.1× 32 277

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-authorship network of co-authors of Nagendra Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Nagendra Kumar. A scholar is included among the top collaborators of Nagendra Kumar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nagendra Kumar. Nagendra Kumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kumar, Nagendra, et al.. (2025). A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification. Expert Systems with Applications. 279. 127292–127292. 5 indexed citations
2.
Laxmi, Vijay, et al.. (2025). Numerical investigation of entry length dependence on inlet turbulence intensity in pipe flow. Flow Measurement and Instrumentation. 108. 103170–103170.
3.
Li, Zhu, et al.. (2025). Intra-modal Relation and Emotional Incongruity Learning using Graph Attention Networks for Multimodal Sarcasm Detection. University of Groningen research database (University of Groningen / Centre for Information Technology). 1–5.
4.
Kumar, Nagendra, et al.. (2025). Emotion-aware dual cross-attentive neural network with label fusion for stance detection in misinformative social media content. Engineering Applications of Artificial Intelligence. 156. 111109–111109.
5.
Dangi, S.S., et al.. (2025). A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction. Computers and Electronics in Agriculture. 239. 110895–110895. 1 indexed citations
6.
Kumar, Nagendra, et al.. (2025). A multimodal–multitask framework with cross-modal relation and hierarchical interactive attention for semantic comprehension. Information Fusion. 126. 103628–103628. 1 indexed citations
7.
Kumar, Nagendra, et al.. (2025). T-MPEDNet: Unveiling the Synergy of Transformer-aware Multiscale Progressive Encoder-Decoder Network with Feature Recalibration for Tumor and Liver Segmentation. Biomedical Signal Processing and Control. 110. 108225–108225. 1 indexed citations
8.
Jain, Arnav, et al.. (2025). An explainable deep neural network with frequency-aware channel and spatial refinement for flood prediction in sustainable cities. Sustainable Cities and Society. 130. 106480–106480. 2 indexed citations
9.
10.
Kumar, Nagendra, et al.. (2024). A hybrid filtering for micro-video hashtag recommendation using graph-based deep neural network. Engineering Applications of Artificial Intelligence. 138. 109417–109417. 3 indexed citations
11.
Kumar, Nagendra, et al.. (2024). A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies. Expert Systems with Applications. 259. 125337–125337. 10 indexed citations
12.
Kumar, Nagendra, et al.. (2024). An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection. Applied Soft Computing. 167. 112258–112258. 4 indexed citations
13.
Kumar, Nagendra, et al.. (2024). A context-aware attention and graph neural network-based multimodal framework for misogyny detection. Information Processing & Management. 62(1). 103895–103895. 8 indexed citations
14.
Kumar, Nagendra, et al.. (2024). CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection. Computers in Biology and Medicine. 179. 108821–108821. 13 indexed citations
15.
Kumar, Nagendra, et al.. (2024). Hierarchical Attention-enhanced Contextual CapsuleNet for Multilingual Hope Speech Detection. Expert Systems with Applications. 268. 126285–126285. 2 indexed citations
16.
Yadav, Anoop, et al.. (2024). MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for segmentation of polyps in colonoscopy. Biomedical Signal Processing and Control. 102. 107363–107363. 7 indexed citations
17.
Kumar, Nagendra, et al.. (2024). A contrastive topic-aware attentive framework with label encodings for post-disaster resource classification. Knowledge-Based Systems. 304. 112526–112526. 7 indexed citations
18.
Kumar, Nagendra, et al.. (2023). Hashtag recommendation for enhancing the popularity of social media posts. Social Network Analysis and Mining. 13(1). 21–21. 21 indexed citations
19.
Kumar, Nagendra, et al.. (2019). Unsupervised tag recommendation for popular and cold products. Journal of Intelligent Information Systems. 54(3). 545–566. 11 indexed citations
20.
Kumar, Nagendra. (2009). The CEOs marketing manifesto. Strategic Direction. 25(5). 2 indexed citations

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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