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 Temperature Prediction of Anti-frost Layer using Machine Learning Techniques

2020 ◽  
Vol 20 (1) ◽  
pp. 9-17
Author(s):  
Jaeho Son ◽  
Youngwon Seo ◽  
Youngmog Park ◽  
Gyutae Cho
Keyword(s):  
Machine Learning ◽  
Measurement Data ◽  
Differential Method ◽  
Machine Learning Techniques ◽  
Freezing And Thawing ◽  
Temperature Prediction ◽  
Ground Freezing ◽  
Learning Techniques ◽  
Learning Machine ◽  
Frost Layer

In Korea, the temperature falls below 0 °C in winter and rises above 0 °C in spring. This change in temperature between the two seasons results in the ground alternatively freezing and thawing, which leads to road surfaces being damaged. Predicting the ground temperature becomes very important in identifying and responding to potential infrastructure damage due to the ground freezing and thawing. A simulation was conducted through numerical analysis using the Crank–Nicholson differential method to predict the temperature of each layer of a road. Moreover, the data gathered from measuring the temperature at each layer of a road over a period of 42 days in “Evaluation of Validity for Anti-frost Layer and Development of its Construction Criteria,” organized by the Ministry of Land, Transport and Maritime Affairs (2012), were used for the simulation. The training for temperature prediction of the anti-frost layer was performed using deep learning machine learning techniques with 650 days of measurement data by changing the number of hidden layers and nodes. With two hidden layers, 40 nodes, and 100 nodes, the reliability of the training result was close to 1. The reliability of the predictive model, a by-product of the training, was approximately 0.87.

Temperature prediction of heat sources using machine learning techniques

Heat Transfer ◽  
2021 ◽  
Author(s):  
Shankar Durgam ◽  
Ajinkya Bhosale ◽  
Vivek Bhosale ◽  
Revati Deshpande ◽  
Pankaj Sutar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Heat Sources ◽  
Temperature Prediction ◽  
Learning Techniques

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

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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