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Institution

Chonbuk National University

EducationJeonju, South Korea
About: Chonbuk National University is a education organization based out in Jeonju, South Korea. It is known for research contribution in the topics: Apoptosis & Nanofiber. The organization has 14820 authors who have published 28884 publications receiving 554131 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a novel strategy for synthesis of highly porous nitrogen-sulfur co-doped graphene nanoribbons (NS-GNRs) with enhanced active sites was developed.

116 citations

Journal ArticleDOI
TL;DR: It was found that darkness, (red 780-622 nm, blue 492-455 nm) and white light influenced pigment and biomass yield and growth of fungi in green and yellow wavelengths resulted in low biomass and pigment yield.

115 citations

Journal ArticleDOI
TL;DR: The results suggest that secretory factors released from stem cells could be an important mediator of stem cell therapy in ischemic tissue diseases.

115 citations

Journal ArticleDOI
TL;DR: In this article, a green and facile one-step hydrothermal process was used to synthesize a composite of metal, metal oxide and graphene oxide (GO) for cyclic use.

115 citations

Journal ArticleDOI
TL;DR: Experimental results show that the models using MBPNN outperform than the basic BPNN and the application of LSA for this system can lead to dramatic dimensionality reduction while achieving good classification results.
Abstract: New text categorization models using back-propagation neural network (BPNN) and modified back-propagation neural network (MBPNN) are proposed. An efficient feature selection method is used to reduce the dimensionality as well as improve the performance. The basic BPNN learning algorithm has the drawback of slow training speed, so we modify the basic BPNN learning algorithm to accelerate the training speed. The categorization accuracy also has been improved consequently. Traditional word-matching based text categorization system uses vector space model (VSM) to represent the document. However, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which can also lead to poor classification accuracy. Latent semantic analysis (LSA) can overcome the problems caused by using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which each term or document is represented as a vector in the space. It not only greatly reduces the dimensionality but also discovers the important associative relationship between terms. We test our categorization models on 20-newsgroup data set, experimental results show that the models using MBPNN outperform than the basic BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving good classification results.

115 citations


Authors

Showing all 14943 results

NameH-indexPapersCitations
Hyun-Chul Kim1764076183227
Andrew Ivanov142181297390
Dong-Chul Son138137098686
C. Haber135150798014
Tae Jeong Kim132142093959
Alessandro Cerri1291244103225
Paul M. Vanhoutte12786862177
Jason Nielsen12589372688
Chi Lin1251313102710
Paul Lujan123125576799
Young Hee Lee122116861107
Min Suk Kim11997566214
Alexandre Sakharov11958256771
Yang-Kook Sun11778158912
Rui L. Reis115160863223
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202366
2022203
20212,069
20201,883
20191,798
20181,893