Results 201 to 210 of about 93,510 (232)

Revolutionizing clinical decision making through deep learning and topic modeling for pathway optimization. [PDF]

Sci Rep
Tianzhao L, Jinzhi H, Rong Z, Jun S, Hailong L, Yan L.   +5 more
europepmc   +1 more source

From practice to lecture hall: Optimizing communication courses in medical education. [PDF]

GMS J Med Educ
Laudage F, Kötter T, Wiswede D.
europepmc   +1 more source

Sustaining Progress of the Journal of Korean Neurosurgical Society Amidst the Crisis in Korean Healthcare System. [PDF]

J Korean Neurosurg Soc
Yang HJ.
europepmc   +1 more source

Intervention in Health Misinformation Using Large Language Models for Automated Detection, Thematic Analysis, and Inoculation: Case Study on COVID-19.

J Med Internet Res
Malek S, Griffin C, Fraleigh RD, Lennon R, Monga V, Shen L.   +5 more
europepmc   +1 more source

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   +2 more sources

Estimation of Optimal Number of Topics Detection and their Assessment Using Hybrid Topic Models for Visualization Enhancement

2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), 2023
Jyoshna Rama Devika Bonu, Rm Noorullah, Moulana Mohammed, K Rashmi   +3 more
openaire   +2 more sources

Determination of the Optimal Number of Topics in the LDA Model When Working with Large Arrays of Text Data

2023 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2023
Mukhit Baimakhanbetov
openaire   +2 more sources

Optimal Number of Topics in Topic Modeling Using Deep Auto Encoder Graph Regularized Non-Negative Matrix Factorization Algorithm

Journal of Systems Science and Systems Engineering
Pooja Kherwa, Jyoti Arora
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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