AIMC Topic: Factor Analysis, Statistical

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One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis.

Psychological methods Mar 5, 2020
Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-comb...
Models, Statistical Factor Analysis, Statistical Data Interpretation, Statistical Psychology Humans Machine Learning
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Stress among Portuguese Medical Students: the EuStress Solution.

Journal of medical systems Jan 2, 2020
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this pa...
Wearable Electronic Devices Humans Sleep Adult Factor Analysis, Statistical Male Neural Networks, Computer Monitoring, Ambulatory Occupational Stress Mental Health Heart Rate Female Energy Intake Body Temperature Students, Medical Young Adult Burnout, Professional Portugal Adolescent
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Development and validation of a questionnaire to assess public receptivity toward autonomous vehicles and its relation with the traffic safety climate in China.

Accident; analysis and prevention Apr 12, 2019
The advent of autonomous vehicles (AVs) has gained increasing attention in China. Although auto manufacturers and innovators have attempted to confirm that AVs are safe and have introduced them on public roads, it is vital to understand end-users' ac...
Humans Artificial Intelligence Reproducibility of Results China Surveys and Questionnaires Factor Analysis, Statistical Accidents, Traffic Young Adult Male Middle Aged Automobile Driving Attitude Organizational Culture
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Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PloS one Mar 8, 2018
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free B...
Fourier Analysis Markov Chains Datasets as Topic Monte Carlo Method Algorithms Unsupervised Machine Learning Factor Analysis, Statistical Bayes Theorem Signal Processing, Computer-Assisted
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Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts.

Ergonomics Dec 19, 2017
Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens...
Accidents, Aviation Systems Analysis Factor Analysis, Statistical Military Personnel Ergonomics Humans Models, Organizational Neural Networks, Computer
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An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning.

The Journal of allergy and clinical immunology Dec 19, 2017
BACKGROUND: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status.
Genetic Markers Infant Immunohistochemistry Decision Support Techniques Humans Adolescent Female Single-Blind Method Child Machine Learning Sensitivity and Specificity Child, Preschool Registries Decision Support Systems, Clinical Male Algorithms Eosinophilic Esophagitis RNA, Messenger Factor Analysis, Statistical
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The neuromorphological caudate-putaminal clustering of neostriate interneurons: Kohonen self-organizing maps and supervised artificial neural networks with multivariate analysis.

Journal of theoretical biology Nov 21, 2017
AIMS: The objective of this study is to investigate the possibility of the neuromorphotopological clustering of neostriate interneurons (NSIN) and their consequent classification into caudate (CIN) and putaminal neuron type (PIN), according to the nu...
Neostriatum Principal Component Analysis Factor Analysis, Statistical Multivariate Analysis Interneurons Humans Dendrites Neural Networks, Computer Putamen ROC Curve Caudate Nucleus Cluster Analysis
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A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Computational intelligence and neuroscience Nov 9, 2017
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collec...
Factor Analysis, Statistical Machine Learning Weather Water Resources Environment Design Forecasting Algorithms Water Supply Water Fresh Water Time Factors Regression Analysis Taiwan
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Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project.

Scientific reports Mar 10, 2017
A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is u...
Humans Machine Learning Longitudinal Studies Health Status Factor Analysis, Statistical Healthy Aging
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Advanced Residuals Analysis for Determining the Number of PARAFAC Components in Dissolved Organic Matter.

Applied spectroscopy Jan 18, 2016
Parallel factor analysis (PARAFAC) has facilitated an explosion in research connecting the fluorescence properties of dissolved organic matter (DOM) to its functions and biogeochemical cycling in natural and engineered systems. However, the validatio...
Neural Networks, Computer Factor Analysis, Statistical Machine Learning Models, Theoretical Humic Substances Cluster Analysis Plant Leaves
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