Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Venkata Sukumar Gurugubelli"'
Autor:
Venkata Sukumar Gurugubelli, Hua Fang, James M. Shikany, Salvador V. Balkus, Joshua Rumbut, Hieu Ngo, Honggang Wang, Jeroan J. Allison, Lyn M. Steffen
Publikováno v:
Smart Health (Amst)
Data harmonization is the process by which each of the variables from different research studies are standardized to similar units resulting in comparable datasets. These data may be integrated for more powerful and accurate examination and predictio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a1845ec7c15e4bff1783f59383606f6
https://pubmed.ncbi.nlm.nih.gov/35252528
https://pubmed.ncbi.nlm.nih.gov/35252528
Publikováno v:
ICNC
Fuzzy-logic based algorithms have been applied in learning longitudinal behavioral intervention data. This paper proposes a modified generalized network-based neuro-fuzzy (mGNNF) classifier for longitudinal randomized controlled trial (RCT) data with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::657a72b498a20190fdc96ef5d97a7250
https://doi.org/10.1109/iccnc.2019.8685574
https://doi.org/10.1109/iccnc.2019.8685574
Publikováno v:
CHASE
Our previous research has shown that the enhanced Fuzzy C-Means algorithm (eFCM) can improve the clustering quality in the big cross-sectional "synthetic data" [1] where a substantive number of clusters are identified with different degrees of overla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0838311da176a8c2e6afaec5c7320c5f
https://doi.org/10.1145/3278576.3278588
https://doi.org/10.1145/3278576.3278588
Publikováno v:
ICNC
Clustering methods become increasingly important in analyzing heterogeneity of treatment effects, especially in longitudinal behavioral intervention studies. Methods such as K-means and Fuzzy C-means (FCM) have been widely endorsed to identify distin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07e3b2b92b0d6d8b2e375b9bb854c119
https://doi.org/10.1109/iccnc.2018.8390419
https://doi.org/10.1109/iccnc.2018.8390419