Analysis of Cancer Data Set with Statistical and Unsupervised Machine Learning Methods

2018 ◽  
pp. 267-276
Author(s):  
T. Panduranga Vital ◽  
K. Dileep Kumar ◽  
H. V. Bhagya Sri ◽  
M. Murali Krishna
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Data Set ◽  
Unsupervised Machine Learning ◽  
Cancer Data ◽  
Machine Learning Methods

scholarly journals Identifying structural changes with unsupervised machine learning methods

2018 ◽  
Vol 98 (5) ◽  
Author(s):  
Nicholas Walker ◽  
Ka-Ming Tam ◽  
Brian Novak ◽  
M. Jarrell
Keyword(s):  
Machine Learning ◽  
Structural Changes ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

scholarly journals Automatic Misinformation Detection About COVID-19 in Brazilian Portuguese WhatsApp Messages

2021 ◽  
Author(s):  
Antônio Diogo Forte Martins ◽  
José Maria Monteiro ◽  
Javam Machado
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Brazilian Portuguese ◽  
Primary Sources ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods

During the coronavirus pandemic, the problem of misinformation arose once again, quite intensely, through social networks. In Brazil, one of the primary sources of misinformation is the messaging application WhatsApp. However, due to WhatsApp's private messaging nature, there still few methods of misinformation detection developed specifically for this platform. In this context, the automatic misinformation detection (MID) about COVID-19 in Brazilian Portuguese WhatsApp messages becomes a crucial challenge. In this work, we present the COVID-19.BR, a data set of WhatsApp messages about coronavirus in Brazilian Portuguese, collected from Brazilian public groups and manually labeled. Then, we are investigating different machine learning methods in order to build an efficient MID for WhatsApp messages. So far, our best result achieved an F1 score of 0.774 due to the predominance of short texts. However, when texts with less than 50 words are filtered, the F1 score rises to 0.85.

scholarly journals Comparison of machine learning methods for crack localization

2019 ◽  
Vol 23 (1) ◽  
pp. 125-142
Author(s):  
Helle Hein ◽  
Ljubov Jaanuska
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Crack Depth ◽  
Haar Wavelet ◽  
Extensive Investigation ◽  
Learning Methods ◽  
Data Set ◽  
Crack Location ◽  
Machine Learning Methods ◽  
Discrete Transform

In this paper, the Haar wavelet discrete transform, the artificial neural networks (ANNs), and the random forests (RFs) are applied to predict the location and severity of a crack in an Euler–Bernoulli cantilever subjected to the transverse free vibration. An extensive investigation into two data collection sets and machine learning methods showed that the depth of a crack is more difficult to predict than its location. The data set of eight natural frequency parameters produces more accurate predictions on the crack depth; meanwhile, the data set of eight Haar wavelet coefficients produces more precise predictions on the crack location. Furthermore, the analysis of the results showed that the ensemble of 50 ANN trained by Bayesian regularization and Levenberg–Marquardt algorithms slightly outperforms RF.

Modelling of diesel engine performance using advanced machine learning methods under scarce and exponential data set

2013 ◽  
Vol 13 (11) ◽  
pp. 4428-4441 ◽  
Author(s):  
Ka In Wong ◽  
Pak Kin Wong ◽  
Chun Shun Cheung ◽  
Chi Man Vong
Keyword(s):  
Machine Learning ◽  
Diesel Engine ◽  
Engine Performance ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods

Machine Learning for Chaotic Rayleigh Transitions

2021 ◽  
Author(s):  
Ben Tribelhorn ◽  
H. E. Dillon
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Natural Convection ◽  
Prandtl Number ◽  
Learning System ◽  
Lorenz Equations ◽  
Time Step ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods

Abstract This paper is a preliminary report on work done to explore the use of unsupervised machine learning methods to predict the onset of turbulent transitions in natural convection systems. The Lorenz system was chosen to test the machine learning methods due to the relative simplicity of the dynamic system. We developed a robust numerical solution to the Lorenz equations using a fourth order Runge-Kutta method with a time step of 0.001 seconds. We solved the Lorenz equations for a large range of Raleigh ratios from 1–1000 while keeping the geometry and Prandtl number constant. We calculated the spectral density, various descriptive statistics, and a cluster analysis using unsupervised machine learning. We examined the performance of the machine learning system for different Raleigh ratio ranges. We found that the automated cluster analysis aligns well with well known key transition regions of the convection system. We determined that considering smaller ranges of Raleigh ratios may improve the performance of the machine learning tools. We also identified possible additional behaviors not shown in z-axis bifurcation plots. This unsupervised learning approach can be leveraged on other systems where numerical analysis is computationally intractable or more difficult. The results are interesting and provide a foundation for expanding the study for Prandtl number and geometry variations. Future work will focus on applying the methods to more complex natural convection systems, including the development of new methods for Nusselt correlations.

scholarly journals Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

2020 ◽  
Vol 18 ◽  
pp. 1509-1524 ◽  
Author(s):  
Jocelyn Gal ◽  
Caroline Bailleux ◽  
David Chardin ◽  
Thierry Pourcher ◽  
Julia Gilhodes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Methods ◽  
Unsupervised Machine Learning ◽  
Machine Learning Methods
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