J. Premaladha

469 citations
15 papers · 324 indexed · h-index 7
Topics
AI in cancer detection (6 papers)Cutaneous Melanoma Detection and Management (5 papers)Multi-Criteria Decision Making (4 papers)
Partner nations
IndiaFinlandLithuania

In The Last Decade

J. Premaladha

15 papers receiving 306 citations

Peers

J. Premaladha
Comparison fields: 5 of 85
  • Oncology 149
  • Artificial Intelligence 143
  • Management Science and Operations Research 70
  • Computer Vision and Pattern Recognition 60
  • Epidemiology 35
Replace Syed Jamal Safdar Gardezi with:
Syed Jamal Safdar Gardezi Malaysia
Youssef Filali Morocco
Anisha P Rodrigues India
Warner ten Kate Netherlands
Antesar Shabut United Kingdom
Mahamudul Hasan Bangladesh
Chandrabose Aravindan India
Nitin Pise India
Vikas Deep India
Bogdan Mazoure Canada
J. Premaladha relative to Syed Jamal Safdar Gardezi Malaysia Syed Jamal Safdar Gardezi's profile →
Citations per field
00.5×4.7×
Syed Jamal Safdar Gardezi · 1×
Citations per year

Countries citing papers authored by J. Premaladha

Since Specialization
Citations

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

Fields of papers citing papers by J. Premaladha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Premaladha

This figure shows the co-authorship network connecting the top 25 collaborators of J. Premaladha. A scholar is included among the top collaborators of J. Premaladha 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 J. Premaladha. J. Premaladha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
#WorkIndexed citations
1 2
2 58
3 4
4 22
5 6
6 2
7 152
8 6
9 6
10 3
11 3
12 44
13 2
14 4
15 10

About J. Premaladha

J. Premaladha is a scholar working on Computational Mathematics, Biophysics and Management Science and Operations Research, having authored 15 papers that have together received 324 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Cutaneous Melanoma Detection and Management (5 papers) and Multi-Criteria Decision Making (4 papers). The work is most often cited by research in Management Science and Operations Research (70 citations), Oncology (149 citations) and Biophysics (26 citations). J. Premaladha has collaborated with scholars based in India, Finland and Lithuania. Frequent co-authors include K. S. Ravichandran, R. Krishankumar, Xiao‐Zhi Gao, R. Manikandan, Edmundas Kazimieras Zavadskas, Samarjit Kar, Jurgita Antuchevičienė, K. S. Ravichandran and K. Geetha. Their work appears in journals such as Sustainability, Soft Computing and Journal of Medical Systems.

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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