On-line optimization and monitoring of power plant performance through machine learning techniques

1998 ◽  
Author(s):  
E. Swidenbank
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Plant Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
On Line

COMMITTEES OF LEARNING AGENTS

2000 ◽  
Vol 08 (02) ◽  
pp. 187-202 ◽  
Author(s):  
LARS ASKER ◽  
MATS DANIELSON ◽  
LOVE EKENBERG
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Decision Analysis ◽  
Automatic System ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Control Room ◽  
Learning Agents ◽  
Learning Techniques ◽  
Current Context

We describe how machine learning and decision theory is combined in an application that supports control room operators of a combined heating and power plant to cope with the overwhelming complexity of situations when severe plant disturbances occur. The application is designed as an assistant, rather than as an automatic system that intervenes directly in the operator/plant loop. The application is required to handle vague and numerically imprecise background information in the construction of classifier committees. A classifier committee (or ensemble) is a classifier created by combining the predictions of multiple sub-classifiers. The presented method combines classifiers into a committee by using computational methods for decision analysis that are designed to work when the information at hand is imprecise. The application evaluates and make priorities between classified alarms according to credibilities that depend on the current context. Machine learning techniques are used to construct classifiers that recognize various malfunctions in a process, determine whether a situation is normal or not, and make priorities among alarms.

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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