Next generation pure component property estimation models: With and without machine learning techniques

AIChE Journal ◽  
2021 ◽  
Author(s):  
Abdulelah S. Alshehri ◽  
Anjan K. Tula ◽  
Fengqi You ◽  
Rafiqul Gani
Keyword(s):  
Machine Learning ◽  
Pure Component ◽  
Machine Learning Techniques ◽  
Next Generation ◽  
Property Estimation ◽  
Learning Techniques ◽  
Component Property ◽  
Estimation Models

scholarly journals Next-Generation Climate Models Could Learn, Improve on the Fly

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Sarah Stanley
Keyword(s):  
Machine Learning ◽  
Climate Models ◽  
Machine Learning Techniques ◽  
Next Generation ◽  
Learning Techniques ◽  
New Models

Scientists propose development of new models that use machine learning techniques to reduce uncertainties in climate predictions.

scholarly journals Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey

2021 ◽  
pp. 103005
Author(s):  
Junaid Shuja ◽  
Kashif Bilal ◽  
Waleed Alasmary ◽  
Hassan Sinky ◽  
Eisa Alanazi
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Next Generation ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Edge Networks

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.

Blind Spoofing Detection for Multi-Antenna Snapshot Receivers using Machine-Learning Techniques

2020 ◽  
Author(s):  
J. Rossouw van der Merwe ◽  
Ana Nikolikj ◽  
Sebastian Kram ◽  
Ivana Lukcin ◽  
Gorjan Nadzinski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Spoofing Detection

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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