Automated Diagnosis and Cause Analysis of Cesarean Section Using Machine Learning Techniques

2012 ◽  
pp. 677-680 ◽  
Author(s):  
Ayesha Sana ◽  
Saad Razzaq ◽  
Javed Ferzund
Keyword(s):  
Machine Learning ◽  
Cesarean Section ◽  
Machine Learning Techniques ◽  
Automated Diagnosis ◽  
Cause Analysis ◽  
Learning Techniques

Use of machine learning on SCADA data for asset's prognostics health management

2020 ◽  
Vol 60 (2) ◽  
pp. 602
Author(s):  
Alexandre Cesa ◽  
Elliot Press
Keyword(s):  
Machine Learning ◽  
Health Management ◽  
Analytical Techniques ◽  
Root Cause Analysis ◽  
Machine Learning Techniques ◽  
Safe Operation ◽  
Cause Analysis ◽  
Root Cause ◽  
Learning Techniques ◽  
Learning Frameworks

The timely detection of anomalies in the process industry is paramount to ensure effective and safe operation of plant. There typically exists an abundance of historical data recorded in Supervisory Control and Data Acquisition (SCADA) systems, which is most often used for understanding past events through, for example, root cause analysis. It is envisaged that higher levels of insight could be achieved from the same datasets by utilising more advanced analytical techniques such as machine learning frameworks. This would enable moving from a ‘diagnosis–mitigation’ (i.e. a root cause analysis) paradigm to a more desirable ‘detection–prediction–prognosis–prevention’ paradigm. Machine learning techniques can be used on SCADA data to support the detection of plant anomaly conditions that do not necessary manifest as process alarms for example. We used a Bayesian network framework on the Tennessee Eastman Plant benchmark problem to demonstrate the technique’s capability. Our model proved to be effective in detecting anomalous plant conditions in most situations.

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.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Sign in / Sign up

Export Citation Format

Share Document