Combining deterministic compressed sensing and machine learning for data reduction in connected health

2020 ◽  
pp. 301-314
Keyword(s):  
Machine Learning ◽  
Compressed Sensing ◽  
Data Reduction ◽  
Connected Health

Machine Learning for Real-time Data Reduction in Cloud of Things

2020 ◽  
Author(s):  
Atheer Alahmed ◽  
Amal Alrasheedi ◽  
Maha Alharbi ◽  
Norah Alrebdi ◽  
Marwan Aleasa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Reduction ◽  
Time Data ◽  
Real Time Data

scholarly journals Statistical Machine Learning and Compressed Sensing Approaches for Analytical Electron Tomography - Application to Phase Change Materials

2019 ◽  
Vol 25 (S2) ◽  
pp. 156-157 ◽  
Author(s):  
Martin Jacob ◽  
Loubna El Gueddari ◽  
Gabriele Navarro ◽  
Marie-Claire Cyrille ◽  
Pascale Bayle-Guillemaud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressed Sensing ◽  
Phase Change ◽  
Phase Change Materials ◽  
Electron Tomography ◽  
Statistical Machine Learning ◽  
Analytical Electron

scholarly journals Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Small ◽  
2020 ◽  
Vol 16 (37) ◽  
pp. 2002878
Author(s):  
Kyle P. Kelley ◽  
Maxim Ziatdinov ◽  
Liam Collins ◽  
Michael A. Susner ◽  
Rama K. Vasudevan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressed Sensing ◽  
Gaussian Process ◽  
Process Optimization ◽  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Probe Microscopy

Machine learning of octahedral tilting in oxide perovskites by symbolic classification with compressed sensing

2020 ◽  
Vol 180 ◽  
pp. 109690
Author(s):  
Stephen R. Xie ◽  
Parker Kotlarz ◽  
Richard G. Hennig ◽  
Juan C. Nino
Keyword(s):  
Machine Learning ◽  
Compressed Sensing ◽  
Octahedral Tilting

scholarly journals Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Amir H. Gandomi ◽  
Mieke H. J. Schulte ◽  
Anna E. Goudriaan ◽  
Simon Y. Foo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Decision Tree ◽  
Data Reduction ◽  
Resting State ◽  
Naive Bayes ◽  
Resting State Fmri ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayes Classifier

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

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