scholarly journals Assessment of Mental Workload: A Comparison of Machine Learning Methods and Subjective Assessment Techniques

2017 ◽  
pp. 30-50 ◽  
Author(s):  
Karim Moustafa ◽  
Saturnino Luz ◽  
Luca Longo
Keyword(s):  
Machine Learning ◽  
Mental Workload ◽  
Subjective Assessment ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Assessment Techniques

scholarly journals Machine Learning Methods for Preterm Birth Prediction: A Review

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 586
Author(s):  
Tomasz Włodarczyk ◽  
Szymon Płotka ◽  
Tomasz Szczepański ◽  
Przemysław Rokita ◽  
Nicole Sochacki-Wójcicka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Wide Spectrum ◽  
Transvaginal Ultrasound ◽  
Preterm Births ◽  
Subjective Assessment ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Learning Methods ◽  
Machine Learning Methods

Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future.

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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