Using Government Data and Machine Learning for Predicting Firms’ Vulnerability to Economic Crisis

2020 ◽  
pp. 345-358
Author(s):  
Euripidis Loukis ◽  
Niki Kyriakou ◽  
Manolis Maragoudakis
Keyword(s):  
Machine Learning ◽  
Economic Crisis ◽  
Government Data

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.

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

scholarly journals Predicting Sense of Community and Participation by Applying Machine Learning to Open Government Data

2017 ◽  
Vol 9 (1) ◽  
pp. 55-75 ◽  
Author(s):  
Alessandro Piscopo ◽  
Ronald Siebes ◽  
Lynda Hardman
Keyword(s):  
Machine Learning ◽  
Sense Of Community ◽  
Open Government ◽  
Open Government Data ◽  
Government Data

scholarly journals Vote Recommendation System using Aspect based Machine Learning Approach

2020 ◽  
Vol 9 (6) ◽  
pp. 135-139
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Child Labor ◽  
Language Processing ◽  
Recommendation System ◽  
Common People ◽  
Area Of Interest ◽  
Machine Learning Approach ◽  
Government Data ◽  
Day By Day

Over time, the information on WWW has escalated exponentially, paramounting to embryonic research in the field of Data Analysis using Natural Language Processing (NLP) and Machine Learning (ML). As data is increasing day by day there is huge demand for data analysis to get subjective information and analyzing government data is very useful and demanding task. So, in this paper, an application is being developed which will recommend the user to which party to vote will be benignant for themselves and for country, depending on the area of interest of different users. The data is collected from various governmental websites of multiple areas like women empowerment, education, employment, child labor etc. which will enhance the authenticity of the output. The main ground of this research is to lubricate common people and politicians as well. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves and other politicians that which party is preferable and which is not preferable in respective areas, so that the politicians can work accordingly.

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.

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