Sample-size dependence or time dependence of statistical measures in informetrics?

2003 ◽  
Vol 55 (2) ◽  
pp. 183-184 ◽  
Author(s):  
Quentin L. Burrell
Keyword(s):  
Time Dependence ◽  
Sample Size ◽  
Size Dependence ◽  
Statistical Measures

Sample-size dependence of the fluorescence of 2-(N-acetylamino)-6-methylpyridine and its Zinc(II) chloride complex

2017 ◽  
Vol 190 ◽  
pp. 261-266 ◽  
Author(s):  
Alexey S. Berezin ◽  
Olga V. Antonova ◽  
Elizaveta V. Lider ◽  
Anton I. Smolentsev ◽  
Vladimir A. Nadolinny ◽  
...  
Keyword(s):  
Sample Size ◽  
Size Dependence ◽  
Chloride Complex

scholarly journals The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

2020 ◽  
Vol 17 (3) ◽  
pp. 1117
Author(s):  
Xiue Gao ◽  
Wenxue Xie ◽  
Shifeng Chen ◽  
Junjie Yang ◽  
Bo Chen
Keyword(s):  
Support Vector Machine ◽  
Sample Size ◽  
Size Dependence ◽  
Prediction Models ◽  
Test Group ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Abdominal Adiposity ◽  
Modeling Group ◽  
Svm Algorithm

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

scholarly journals Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3777 ◽  
Author(s):  
Ataollah Shirzadi ◽  
Karim Soliamani ◽  
Mahmood Habibnejhad ◽  
Ataollah Kavian ◽  
Kamran Chapi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Sample Sizes ◽  
Promising Alternative ◽  
Statistical Measures

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

Hyperplastic Forming: Process Potential and Factors Affecting Formability

1999 ◽  
Vol 601 ◽  
Author(s):  
Glenn S. Daehn ◽  
Vincent J. Vohnout ◽  
Subrangshu Datta
Keyword(s):  
Boundary Conditions ◽  
Sample Size ◽  
Active Control ◽  
High Velocity ◽  
Size Dependence ◽  
Recent Model ◽  
The Body ◽  
Electromagnetic Forming ◽  
Forming Process ◽  
Factors Affecting

AbstractThis paper has two distinct goals. First, we argue in an extended introduction that high velocity forming, as can be implemented through electromagnetic forming, is a technology that should be developed. As a process used in conjunction with traditional stamping, it may offer dramatically improved formability, reduced wrinkling and active control of springback among other advantages. In the body of the paper we describe the important factors that lead to improved formability at high velocity. In particular, high sample velocity can inhibit neck growth. There is a sample size dependence where larger samples have better ductility than those of smaller dimensions. These aspects are at least partially described by the recent model of Freund and Shenoy. In addition to this, boundary conditions imposed by sample launch and die impact can have important effects on formability.

scholarly journals Sample-size dependence of the ground-state energy in a one-dimensional localization problem

1996 ◽  
Vol 54 (1) ◽  
pp. 231-242 ◽  
Author(s):  
C. Monthus ◽  
G. Oshanin ◽  
A. Comtet ◽  
S. F. Burlatsky
Keyword(s):  
Sample Size ◽  
Ground State Energy ◽  
Ground State ◽  
Size Dependence ◽  
State Energy ◽  
Localization Problem ◽  
One Dimensional
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