Cognitive Health Reflection to Keto Diet Attitude with Machine Learning Clustering

2021 ◽  
Author(s):  
Gent Imeraj ◽  
Erilda Muka ◽  
Maaruf Ali ◽  
Ali Osman Topal ◽  
Mevlida Zenuni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Health

scholarly journals Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

2019 ◽  
Vol 70 (1) ◽  
pp. 277-286 ◽  
Author(s):  
Michael F. Bergeron ◽  
Sara Landset ◽  
Franck Tarpin-Bernard ◽  
Curtis B. Ashford ◽  
Taghi M. Khoshgoftaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Status ◽  
Episodic Memory ◽  
Memory Performance ◽  
Cognitive Health ◽  
Status Classification

Automated cognitive health assessment in smart homes using machine learning

2020 ◽  
pp. 102572
Author(s):  
Abdul Rehman Javed ◽  
Labiba Gillani Fahad ◽  
Asma Ahmad Farhan ◽  
Sidra Abbas ◽  
Gautam Srivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Assessment ◽  
Smart Homes ◽  
Cognitive Health

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

Cognitive Health Counseling 40+

2013 ◽  
Vol 21 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Anne Eschen ◽  
Franzisca Zehnder ◽  
Mike Martin
Keyword(s):  
Outcome Variable ◽  
Pilot Phase ◽  
Subjective Memory ◽  
Cognitive Health ◽  
Health Counseling ◽  
Agents Of Change ◽  
Main Outcome Variable ◽  
Further Development

This article introduces Cognitive Health Counseling 40+ (CH.CO40+), an individualized intervention that is conceptually based on the orchestration model of quality-of-life management ( Martin & Kliegel, 2010 ) and aims at improving satisfaction with cognitive health in adults aged 40 years and older. We describe the theoretically deduced characteristics of CH.CO40+, its target group, its multifactorial nature, its individualization, the application of subjective and objective measures, the role of participants as agents of change, and the rationale for choosing participants’ satisfaction with their cognitive health as main outcome variable. A pilot phase with 15 middle-aged and six older adults suggests that CH.CO40+ attracts, and may be particularly suitable for, subjective memory complainers. Implications of the pilot data for the further development of the intervention are discussed.

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