scholarly journals Revisiting the Economic Crisis after a Decade: Statistical and Machine Learning Perspectives

2019 ◽  
Vol 25 (2) ◽  
pp. 14-19
Author(s):  
Jiayan Yu ◽  
◽  
Jingqian Zhang ◽  
Hee Eun Shin ◽  
Jooan Kong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Economic Crisis

Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning

2018 ◽  
Vol 53 (4) ◽  
pp. 1467-1485 ◽  
Author(s):  
Mamta Mittal ◽  
Lalit Mohan Goyal ◽  
Jasleen Kaur Sethi ◽  
D. Jude Hemanth
Keyword(s):  
Machine Learning ◽  
Economic Crisis ◽  
The Impact

Healthy and wellbeing activities’ promotion using a Big Data approach

2016 ◽  
Vol 24 (2) ◽  
pp. 125-135 ◽  
Author(s):  
Diego Gachet Páez ◽  
Manuel de Buenaga Rodríguez ◽  
Enrique Puertas Sánz ◽  
María Teresa Villalba ◽  
Rafael Muñoz Gil
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Economic Crisis ◽  
Healthy Lifestyle ◽  
Developed Countries ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Public And Private ◽  
Private Healthcare

The aging population and economic crisis specially in developed countries have as a consequence the reduction in funds dedicated to health care; it is then desirable to optimize the costs of public and private healthcare systems, reducing the affluence of chronic and dependent people to care centers; promoting healthy lifestyle and activities can allow people to avoid chronic diseases as for example hypertension. In this article, we describe a system for promoting an active and healthy lifestyle for people and to recommend with guidelines and valuable information about their habits. The proposed system is being developed around the Big Data paradigm using bio-signal sensors and machine-learning algorithms for recommendations.

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.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

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
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