Endpoint Temperature Prediction model for LD Converters Using Machine-Learning Techniques

2019 ◽  
Author(s):  
Hyeontae Jo ◽  
Hyung Ju Hwang ◽  
Du Phan ◽  
Youmin Lee ◽  
Hyeokjae Jang
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Machine Learning Techniques ◽  
Temperature Prediction ◽  
Learning Techniques

Mandibular shape prediction model using machine learning techniques

2022 ◽  
Author(s):  
Tania Camila Niño-Sandoval ◽  
Robinson Andrés Jaque ◽  
Fabio A. González ◽  
Belmiro C. E. Vasconcelos
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Machine Learning Techniques ◽  
Shape Prediction ◽  
Learning Techniques

Machine learning based temperature prediction of poly(N-isopropylacrylamide)-capped plasmonic nanoparticle solutions

2019 ◽  
Vol 21 (44) ◽  
pp. 24808-24819
Author(s):  
Sudaraka Mallawaarachchi ◽  
Yiyi Liu ◽  
San H. Thang ◽  
Wenlong Cheng ◽  
Malin Premaratne
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Solution Temperature ◽  
Thermosensitive Polymer ◽  
Temperature Prediction ◽  
Plasmonic Nanoparticle ◽  
Learning Techniques

Machine learning techniques can predict the solution temperature of thermosensitive polymer-capped nanoparticle solutions to within 1 °C of accuracy.

scholarly journals Financial Distress Prediction Using Hybrid Machine Learning Techniques

2020 ◽  
pp. 1-12
Author(s):  
Suduan Chen ◽  
Zong-De Shen
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Distress ◽  
Machine Learning Techniques ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Learning Techniques ◽  
Distress Prediction ◽  
Hybrid Machine ◽  
Financially Distressed

The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

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