Enhancing prediction of student success: Automated machine learning approach

2021 ◽  
Vol 89 ◽  
pp. 106903
Author(s):  
Hassan Zeineddine ◽  
Udo Braendle ◽  
Assaad Farah
Keyword(s):  
Machine Learning ◽  
Student Success ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Automated Machine Learning

Accelerating Surgical Site Infection Abstraction With a Semi-automated Machine-learning Approach

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Steven J. Skube ◽  
Zhen Hu ◽  
Gyorgy J. Simon ◽  
Elizabeth C. Wick ◽  
Elliot G. Arsoniadis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surgical Site Infection ◽  
Learning Approach ◽  
Site Infection ◽  
Machine Learning Approach ◽  
Automated Machine Learning

Developing smart devices with automated Machine learning Approach: A review

2021 ◽  
Author(s):  
Sunil A. Patel ◽  
Sanjay P. Patel ◽  
Yagna Bhupendra Kumar Adhyaru ◽  
Santosh Maheshwari ◽  
Pankaj Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Devices ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Automated Machine Learning

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach

2021 ◽  
Author(s):  
Adolfo M. García ◽  
Tomás Arias‐Vergara ◽  
Juan Vasquez‐Correa ◽  
Elmar Nöth ◽  
Maria Schuster ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Automated Machine Learning ◽  
Cognitive Determinants

scholarly journals An automated machine learning approach to predict brain age from cortical anatomical measures

2020 ◽  
Vol 41 (13) ◽  
pp. 3555-3566 ◽  
Author(s):  
Jessica Dafflon ◽  
Walter H. L. Pinaya ◽  
Federico Turkheimer ◽  
James H. Cole ◽  
Robert Leech ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Automated Machine Learning ◽  
Brain Age

scholarly journals Development and Initial Validation of Two Brief Measures of Left-wing Authoritarianism: A Machine Learning Approach

2021 ◽  
Author(s):  
Thomas H Costello ◽  
Christopher Patrick
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Political Violence ◽  
Community Sample ◽  
Learning Approach ◽  
Left Wing ◽  
Initial Validation ◽  
Machine Learning Approach ◽  
Automated Machine Learning ◽  
Political Spectrum

Although authoritarianism has predominantly been studied among conservatives, newer work on left-wing authoritarianism (LWA) has suggested that authoritarian individuals exist on both poles of the political spectrum. A 39-item multidimensional measure, the Left-wing Authoritarianism Index, was recently developed to measure LWA. The present study used a fully automated machine learning approach (i.e., a genetic algorithm) in a large, demographically representative American sample (N = 834) to generate two abbreviated versions of the LWA Index. A second community sample (N = 477) was used to conduct extensive validational tests of the abbreviated measures, which comprise 25- and 13-items. The abbreviated forms demonstrated remarkable convergence with the full LWA Index in terms of their psychometric (e.g., internal consistency) and distributional (e.g., mean, standard deviation, skew, kurtosis) properties. Further, this convergence extended to virtually identical cross-measure patterns of correlations with 14 external criteria, including need for chaos, political violence, anomia, and low institutional trust. In light of these results, the LWA-25 and LWA-13 scales appeared to function effectively as measures of LWA. We conclude by examining the items retained (vs. excluded) by the genetic algorithm to clarify the central vs. peripheral conceptual elements of LWA.

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