Using Neural Network Analysis to Define Methods of DINA Model Estimation for Small Sample Sizes

2013 ◽  
Vol 30 (2) ◽  
pp. 173-194 ◽  
Author(s):  
Zhan Shu ◽  
Robert Henson ◽  
John Willse
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Small Sample ◽  
Model Estimation ◽  
Neural Network Analysis ◽  
Sample Sizes ◽  
Dina Model ◽  
Small Sample Sizes

A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes

2018 ◽  
Vol 89 (17) ◽  
pp. 3539-3555 ◽  
Author(s):  
Bing Wei ◽  
Kuangrong Hao ◽  
Xue-song Tang ◽  
Yongsheng Ding
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Compressive Sensing ◽  
Classification Accuracy ◽  
Small Sample ◽  
Compressive Sampling ◽  
New Method ◽  
Sample Sizes ◽  
Fabric Defect ◽  
Small Sample Sizes

The convolutional neural network (CNN) has recently achieved great breakthroughs in many computer vision tasks. However, its application in fabric texture defects classification has not been thoroughly researched. To this end, this paper carries out a research on its application based on the CNN model. Meanwhile, since the CNN cannot achieve good classification accuracy in small sample sizes, a new method combining compressive sensing and the convolutional neural network (CS-CNN) is proposed. Specifically, this paper uses the compressive sampling theorem to compress and augment the data in small sample sizes; then the CNN can be employed to classify the data features directly from compressive sampling; finally, we use the test data to verify the classification performance of the method. The explanatory experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our CS-CNN approach can effectively improve the classification accuracy in fabric defect samples, even with a small number of defect samples.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

NEURAL NETWORK ANALYSIS OF INTERACTION OF POSTURAL CONTROL MACHANISMS UNDER ARTUFICIAL FEEDBACK

2016 ◽  
Vol 34 (2) ◽  
pp. 025-036
Author(s):  
Oleg G. Gorshkov ◽  
◽  
Irina B. Starchenko ◽  
Andrey S. Sliva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Postural Control ◽  
Neural Network Analysis

scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

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