scholarly journals Adaptive Semi-Supervised Classification by Joint Global and Local Graph

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87212-87222 ◽  
Author(s):  
Siyang Deng ◽  
Qianqian Wang ◽  
Quanxue Gao
Keyword(s):  
Supervised Classification ◽  
Local Graph ◽  
Global And Local

scholarly journals Global and Local Graph Modifiers

2009 ◽  
Vol 231 ◽  
pp. 293-307 ◽  
Author(s):  
Guillaume Aucher ◽  
Philippe Balbiani ◽  
Luis Fariñas del Cerro ◽  
Andreas Herzig
Keyword(s):  
Local Graph ◽  
Global And Local

scholarly journals A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaojin Li ◽  
Xintao Hu ◽  
Changfeng Jin ◽  
Junwei Han ◽  
Tianming Liu ◽  
...  
Keyword(s):  
Comparative Study ◽  
Human Brain ◽  
Brain Networks ◽  
Brain Network ◽  
Graph Models ◽  
Graph Properties ◽  
Local Graph ◽  
Structural Networks ◽  
Global And Local ◽  
Structural Brain

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts

2014 ◽  
Vol 38 (5) ◽  
pp. 369-380 ◽  
Author(s):  
Ling Zhang ◽  
Hui Kong ◽  
Chien Ting Chin ◽  
Shaoxiong Liu ◽  
Zhi Chen ◽  
...  
Keyword(s):  
Graph Cuts ◽  
Cervical Cytology ◽  
Abnormal Cells ◽  
Local Graph ◽  
Global And Local

Sunspot Groups as Tracers of Sub-Surface Processes

2000 ◽  
Vol 179 ◽  
pp. 155-160
Author(s):  
M. H. Gokhale
Keyword(s):  
Magnetic Flux ◽  
Indian Institute ◽  
Surface Processes ◽  
The Sun ◽  
Global And Local ◽  
Sunspot Groups

AbstractData on sunspot groups have been quite useful for obtaining clues to several processes on global and local scales within the sun which lead to emergence of toroidal magnetic flux above the sun’s surface. I present here a report on such studies carried out at Indian Institute of Astrophysics during the last decade or so.

Establishing global and local coherence in discourse processing: Evidence from fMRI

2009 ◽  
Author(s):  
Paul van den Broek ◽  
Ben Seipel ◽  
Virginia Clinton ◽  
Edward J. O'Brien ◽  
Philip Burton ◽  
...  
Keyword(s):  
Discourse Processing ◽  
Local Coherence ◽  
Global And Local

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

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