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.
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
2016695 citationsWei Luo, Dinh Phung et al.Journal of Medical Internet Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Chandan Karmakar
Since
Specialization
Citations
This map shows the geographic impact of Chandan Karmakar'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 Chandan Karmakar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chandan Karmakar more than expected).
Fields of papers citing papers by Chandan Karmakar
This network shows the impact of papers produced by Chandan Karmakar. 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 Chandan Karmakar. The network helps show where Chandan Karmakar may publish in the future.
Co-authorship network of co-authors of Chandan Karmakar
This figure shows the co-authorship network connecting the top 25 collaborators of Chandan Karmakar.
A scholar is included among the top collaborators of Chandan Karmakar 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 Chandan Karmakar. Chandan Karmakar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karmakar, Chandan, et al.. (2018). Multi-lag tone–entropy in neonatal stress. Journal of The Royal Society Interface. 15(146). 20180420–20180420.45 indexed citations
11.
Luo, Wei, Dinh Phung, Truyen Tran, et al.. (2016). Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. Journal of Medical Internet Research. 18(12). e323–e323.695 indexed citations breakdown →
12.
Imam, Hasan, Chandan Karmakar, Ahsan H. Khandoker, Herbert F. Jelinek, & Marimuthu Palaniswami. (2014). Analysing cardiac autonomic neuropathy in diabetes using electrocardiogram derived systolic-diastolic interval interactions. Computing in Cardiology Conference. 41. 85–88.7 indexed citations
Karmakar, Chandan, Hasan Imam, Ahsan H. Khandoker, & Marimuthu Palaniswami. (2014). Influence of psychological stress on QT interval. Deakin Research Online (Deakin University). 41. 1009–1012.7 indexed citations
15.
Khandoker, Ahsan H., Chandan Karmakar, Yoshitaka Kimura, & Marimuthu Palaniswami. (2013). Development of fetal heart rate dynamics before and after 30 and 35 weeks of gestation. Deakin Research Online (Deakin University). 40. 453–456.2 indexed citations
16.
Karmakar, Chandan, Ahsan H. Khandoker, Mikko P. Tulppo, et al.. (2012). Dynamics of heart rate changes following moderate and high volume exercise training. Deakin Research Online (Deakin University). 39. 953–956.1 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.