Prognosis of NBTI aging using a machine learning scheme

2016 ◽  
Author(s):  
Naghmeh Karimi ◽  
Ke Huang
Keyword(s):  
Machine Learning ◽  
Learning Scheme

Decoding the Torque of Lower Limb Joints from EEG Recordings of Pre-gait Movements Using a Machine Learning Scheme

2021 ◽  
Author(s):  
Luis Mercado ◽  
Lucero Alvarado ◽  
Griselda Quiroz-Compean ◽  
Rebeca Romo-Vazquez ◽  
Hugo Vélez-Pérez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Learning Scheme ◽  
Eeg Recordings

scholarly journals Hybrid Machine Learning Scheme for Classification of BECTS and TLE Patients Using EEG Brain Signals

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 218924-218935
Author(s):  
Wonsik Yang ◽  
Minsoo Joo ◽  
Yujaung Kim ◽  
Se Hee Kim ◽  
Jong-Moon Chung
Keyword(s):  
Machine Learning ◽  
Brain Signals ◽  
Learning Scheme ◽  
Hybrid Machine

scholarly journals Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e33829 ◽  
Author(s):  
Max K. Leong ◽  
Hong-Bin Chen ◽  
Yu-Hsuan Shih
Keyword(s):  
Machine Learning ◽  
P Glycoprotein ◽  
Learning Scheme ◽  
Glycoprotein Inhibition

scholarly journals Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Gang Liu ◽  
Yanan Gao ◽  
Ying Liu ◽  
Yaomin Guo ◽  
Zhicong Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Disease Severity ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Learning Scheme ◽  
The Individual ◽  
Jankovic Rating Scale

Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly correlated with disease severity. However, whether the individual severity of BSP can be identified using these DTI metrics remains unknown. We aimed to investigate whether a combination of machine learning techniques and LDH or FA can accurately identify the individual severity of BSP. Forty-one patients with BSP were assessed using the Jankovic Rating Scale and DTI. The patients were assigned to non-functionally and functionally limited groups according to their Jankovic Rating Scale scores. A machine learning scheme consisting of beam search and support vector machines was designed to identify non-functionally versus functionally limited outcomes, with the input features being LDH or FA in 68 white matter regions. The proposed machine learning scheme with LDH or FA yielded an overall accuracy of 88.67 versus 85.19% in identifying non-functionally limited versus functionally limited outcomes. The scheme also identified a sensitivity of 91.40 versus 85.87% in correctly identifying functionally limited outcomes, a specificity of 83.33 versus 83.67% in accurately identifying non-functionally limited outcomes, and an area under the curve of 93.7 versus 91.3%. These findings suggest that a combination of LDH or FA measurements and a sophisticated machine learning scheme can accurately and reliably identify the individual disease severity in patients with BSP.

scholarly journals Document Classification

2021 ◽  
pp. 132-136
Keyword(s):  
Machine Learning ◽  
Contextual Effects ◽  
Document Classification ◽  
Feature Vectors ◽  
Learning Scheme ◽  
Document Collection

Keywords can be used as attributes for mining rules or as a basis for measuring the similarity of new (unclassified) documents with existing (classified) ones. The focus is on the problem of extracting keywords from document collection in order to use them as attributes for document classification. Document classification is a hot topic in machine learning. Typical approaches extract “features,” generally words, from document, and use the feature vectors as input to a machine learning scheme that learns how to classify documents. This “bag of keywords” model neglects keyword order and contextual effects.

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