scholarly journals Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies from Cross‐Phase Spectra

2021 ◽  
Author(s):  
R. Foldes ◽  
A. Del Corpo ◽  
E. Pietropaolo ◽  
M. Vellante
Keyword(s):  
Machine Learning ◽  
Field Line ◽  
Machine Learning Techniques ◽  
Field Line Resonance ◽  
Learning Techniques ◽  
Resonance Frequencies ◽  
Line Resonance ◽  
Phase Spectra

scholarly journals Pc5 field line resonance frequencies and structure observed by SuperDARN and CANOPUS

1998 ◽  
Vol 103 (A6) ◽  
pp. 11771-11785 ◽  
Author(s):  
C. W. S. Ziesolleck ◽  
F. R. Fenrich ◽  
J. C. Samson ◽  
D. R. McDiarmid
Keyword(s):  
Field Line ◽  
Field Line Resonance ◽  
Resonance Frequencies ◽  
Line Resonance

scholarly journals Interhemispheric observations of field line resonance frequencies as a continuous function of ground latitude in the auroral zones

Polar Science ◽  
2008 ◽  
Vol 2 (2) ◽  
pp. 73-86 ◽  
Author(s):  
S. Takasaki ◽  
N. Sato ◽  
A. Kadokura ◽  
H. Yamagishi ◽  
H. Kawano ◽  
...  
Keyword(s):  
Continuous Function ◽  
Field Line ◽  
Field Line Resonance ◽  
Resonance Frequencies ◽  
Line Resonance

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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