Results 221 to 230 of about 774,682 (254)

ViMDT: a clinical data visual analysis tool for multi-disciplinary treatment of lung cancer. [PDF]

Clin Exp Med
Zhu W, Jiang X, Zhang L, Zhou P, Xie X, Wang H.   +5 more
europepmc   +1 more source

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study. [PDF]

JMIR Form Res
Ryan P, Davoren O, Elwyn G.
europepmc   +1 more source

The Evolution of Nanoparticle Regulation: A Meta-Analysis of Research Trends and Historical Parallels (2015-2025). [PDF]

Nanomaterials (Basel)
Shin SK, Sharma N, An SSA, Kim MJ.
europepmc   +1 more source

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

JMIR Cancer
De La Hoz-M J, Montes-Escobar K, Salas-Macias CA, Fors M, Ballaz SJ.   +4 more
europepmc   +1 more source

Exploring Social Media Discourse on Cigars: Insights from Twitter/X Data. [PDF]

Nicotine Tob Res
Yao J, Li D, Ross JC, Xie Z.
europepmc   +1 more source

Combining Natural Language Processing with Patient-Reported Outcome Measures scores to investigate the impact of pandemic regulations on anxiety in children

Luijten MAJ, Gibbons C, Harrison CJ, Oers HA, Zijlmans J, Tieskens JM, Alrouh H, Broek EM, Boer JN, Haverman L, Polderman TJC.   +10 more
europepmc   +1 more source

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Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)

2020
Feature extraction is one of the challenging works in the Machine Learning (ML) arena. The more features one able to extract correctly, the more accurate knowledge one can exploit from data. Latent Dirichlet Allocation (LDA) is a form of topic modeling used to extract features from text data.
Mahedi Hasan, Anichur Rahman, Md. Razaul Karim, Md. Saikat Islam Khan, Md. Jahidul Islam   +4 more
openaire   +1 more source

Estimation of an Optimized Number of Topics by Consensus Soft Clustering Using NMF

Electronics and Communications in Japan, 2012
SUMMARYWe propose here a novel approach to exploring an optimized number of topics in a document set using consensus clustering based on nonnegative matrix factorization (NMF). It is useful to automatically determine the number of topics in a document set because various approaches to heuristic topic extraction determine it. Consensus clustering merges
openaire   +1 more source