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Conrad S. Tucker

ResearcherPublications, citations & collaboration network

Conrad S. Tucker is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 208 works, 3,272 citations, an h-index of 31 and an i10-index of 75.

208
Works
3,272
Citations
31
h-index
75
i10-index

How has Conrad S. Tucker's publication output changed over time?

ScholarIQpublication output · 2014–2023

Output grew0% over the shown period — from 1 works in 2014 to 1 in 2023.

1
3
1
2
1
1
1
2014201520162018201920212023

What are the most-cited papers on Conrad S. Tucker?

ScholarIQmost cited works
The effects of player type on performance: A gamification case study
Christian López, Conrad S. Tucker
Computers in Human Behavior. 2018175 Citations
An unsupervised machine learning method for discovering patient clusters based on genetic signatures
Christian López, Scott Tucker, T. Salameh, Conrad S. Tucker
Journal of Biomedical Informatics. 2018135 CitationsOPEN ACCESS
An unsupervised machine learning model for discovering latent infectious diseases using social media data
Sunghoon Lim, Conrad S. Tucker, Soundar Kumara
Journal of Biomedical Informatics. 2016127 CitationsOPEN ACCESS
3D Design Using Generative Adversarial Networks and Physics-Based Validation
Dule Shu, James Cunningham, Gary Stump, Simon W. Miller, Michael A. Yukish, Timothy W. Simpson, Conrad S. Tucker
S88457320. 2019114 CitationsOPEN ACCESS
An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages
Suppawong Tuarob, Conrad S. Tucker, Marcel Salathé, Nilám Ram
Journal of Biomedical Informatics. 2014113 CitationsOPEN ACCESS

Related on ScholarIQ

The University of Western Australia
Institution
The effects of player type on performance: A gamification case study
Paper
An unsupervised machine learning method for discovering patient clusters based on genetic signatures
Paper
An unsupervised machine learning model for discovering latent infectious diseases using social media data
Paper
3D Design Using Generative Adversarial Networks and Physics-Based Validation
Paper
An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages
Paper
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