Swarup Chandra

1.1k total citations · 1 hit paper
20 papers, 660 citations indexed

About

Swarup Chandra is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Swarup Chandra has authored 20 papers receiving a total of 660 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 6 papers in Signal Processing and 5 papers in Information Systems. Recurrent topics in Swarup Chandra's work include Data Stream Mining Techniques (12 papers), Machine Learning and Data Classification (8 papers) and Anomaly Detection Techniques and Applications (7 papers). Swarup Chandra is often cited by papers focused on Data Stream Mining Techniques (12 papers), Machine Learning and Data Classification (8 papers) and Anomaly Detection Techniques and Applications (7 papers). Swarup Chandra collaborates with scholars based in United States, Spain and Saudi Arabia. Swarup Chandra's co-authors include Latifur Khan, Ahsanul Haque, Kevin W. Hamlen, Bo Dong, Zhuoyi Wang, Bhavani Thuraisingham, Charų C. Aggarwal, Zhiqiang Lin, Ahmad Mustafa and Yang Gao and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Dependable and Secure Computing and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Swarup Chandra

19 papers receiving 624 citations

Hit Papers

FUSION 2017 2026 2020 2023 2017 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. FUSION
    2017 333 citations Ahsanul Haque, Zhuoyi Wang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Swarup Chandra United States 9 255 111 105 104 73 20 660
Telecommunications Board 13 71 0.3× 88 0.8× 55 0.5× 66 0.6× 16 0.2× 26 601
Remo Burkhard Switzerland 11 119 0.5× 83 0.7× 41 0.4× 19 0.2× 25 0.3× 33 653
Hamid Turab Mirza Pakistan 11 184 0.7× 269 2.4× 92 0.9× 78 0.8× 9 0.1× 34 559
Eko Sediyono Indonesia 12 167 0.7× 276 2.5× 17 0.2× 30 0.3× 64 0.9× 170 596
Annika Hinze New Zealand 16 199 0.8× 356 3.2× 83 0.8× 289 2.8× 42 0.6× 129 1.1k
James A. Malcolm United Kingdom 13 235 0.9× 102 0.9× 36 0.3× 107 1.0× 11 0.2× 48 567
Jeongkyu Lee United States 13 124 0.5× 101 0.9× 87 0.8× 67 0.6× 62 0.8× 89 631
Choo‐Yee Ting Malaysia 13 202 0.8× 80 0.7× 37 0.4× 96 0.9× 34 0.5× 50 556
Laura Radaelli Israel 7 192 0.8× 90 0.8× 44 0.4× 62 0.6× 4 0.1× 9 488
Mark Ware United Kingdom 11 77 0.3× 168 1.5× 38 0.4× 13 0.1× 93 1.3× 24 683

Countries citing papers authored by Swarup Chandra

Since Specialization
Citations

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

Fields of papers citing papers by Swarup Chandra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Swarup Chandra

This figure shows the co-authorship network connecting the top 25 collaborators of Swarup Chandra. A scholar is included among the top collaborators of Swarup Chandra 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 Swarup Chandra. Swarup Chandra 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.
Gao, Yang, et al.. (2020). SACCOS: A Semi-Supervised Framework for Emerging Class Detection and Concept Drift Adaption Over Data Streams. IEEE Transactions on Knowledge and Data Engineering. 34(3). 1416–1426. 32 indexed citations
2.
Dong, Bo, et al.. (2020). GCI: A GPU Based Transfer Learning Approach for Detecting Cheats of Computer Game. IEEE Transactions on Dependable and Secure Computing. 1–1. 5 indexed citations
3.
Dong, Bo, Yang Gao, Swarup Chandra, & Latifur Khan. (2019). Multistream Classification with Relative Density Ratio Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 3478–3485. 10 indexed citations
4.
Wang, Zhuoyi, et al.. (2019). Metric Learning based Framework for Streaming Classification with Concept Evolution. 1–8. 6 indexed citations
5.
Ayoade, Gbadebo, Swarup Chandra, Latifur Khan, Kevin W. Hamlen, & Bhavani Thuraisingham. (2018). Automated Threat Report Classification over Multi-Source Data. 236–245. 21 indexed citations
6.
Dong, Bo, et al.. (2018). GCI: A Transfer Learning Approach for Detecting Cheats of Computer Game. 10. 1188–1197. 3 indexed citations
7.
Chandra, Swarup, et al.. (2018). BCD. 393–398. 8 indexed citations
8.
Chandra, Swarup, et al.. (2018). Ensemble Direct Density Ratio Estimation for Multistream Classification. 11. 1364–1367. 5 indexed citations
9.
Haque, Ahsanul, et al.. (2018). A Framework for Multistream Regression With Direct Density Ratio Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 11 indexed citations
10.
Haque, Ahsanul, Swarup Chandra, Latifur Khan, Kevin W. Hamlen, & Charų C. Aggarwal. (2017). Efficient Multistream Classification Using Direct Density Ratio Estimation. 155–158. 3 indexed citations
11.
Haque, Ahsanul, Zhuoyi Wang, Swarup Chandra, et al.. (2017). FUSION. 919–928. 333 indexed citations breakdown →
12.
Chandra, Swarup, et al.. (2017). Focus location extraction from political news reports with bias correction. 1956–1964. 9 indexed citations
13.
Chandra, Swarup, Ahmad Mustafa, Latifur Khan, et al.. (2016). Adaptive encrypted traffic fingerprinting with bi-directional dependence. 177–188. 71 indexed citations
14.
Chandra, Swarup, Ahsanul Haque, Latifur Khan, & Charų C. Aggarwal. (2016). An Adaptive Framework for Multistream Classification. 1181–1190. 35 indexed citations
15.
Chandra, Swarup, Ahsanul Haque, Latifur Khan, & Charų C. Aggarwal. (2016). Efficient Sampling-Based Kernel Mean Matching. 10. 811–816. 6 indexed citations
16.
Haque, Ahsanul, Zhuoyi Wang, Swarup Chandra, et al.. (2016). Sampling-based distributed Kernel mean matching using spark. 10. 462–471. 4 indexed citations
17.
Haque, Ahsanul, Swarup Chandra, Latifur Khan, & Charų C. Aggarwal. (2014). Distributed Adaptive Importance Sampling on graphical models using MapReduce. 597–602. 5 indexed citations
18.
Chandra, Swarup, Justin Sahs, Latifur Khan, Bhavani Thuraisingham, & Charų C. Aggarwal. (2014). Stream Mining Using Statistical Relational Learning. 8. 743–748.
19.
Haque, Ahsanul, Swarup Chandra, Latifur Khan, & Michael Baron. (2014). MapReduce guided approximate inference over graphical models. 12. 446–453. 1 indexed citations
20.
Chandra, Swarup, et al.. (2011). Estimating Twitter User Location Using Social Interactions--A Content Based Approach. 838–843. 92 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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