Predicting the Happiness of Koreans Using Machine Learning: Using Data from the National Leisure Activity Survey

2021 ◽  
Vol 4 (2) ◽  
pp. 1-16
Author(s):  
Kyung-sik Kim
Keyword(s):  
Machine Learning ◽  
Leisure Activity ◽  
Using Data ◽  
Activity Survey

scholarly journals A Study on Types of Leisure Index Based on National Leisure Activities Survey 2019 in Korea

2021 ◽  
Vol 13 (7) ◽  
pp. 3619
Author(s):  
Jongsoon Kim ◽  
Saesook Oh ◽  
Boonhong Yeon
Keyword(s):  
Quality Of Life ◽  
Local Community ◽  
Leisure Activities ◽  
Leisure Activity ◽  
Condition Index ◽  
Leisure Constraints ◽  
Resource Index ◽  
Using Data ◽  
Activity Survey

This study investigated differences in main purposes of leisure activities, leisure constraints, and the quality of life among segmented clusters based on leisure condition index, leisure resource index, and leisure attitude index utilizing Korean Better Leisure Index (K-BLI). Characteristics of each cluster were aggregated for profiling using data from the ⎡National Leisure Activity Survey 2019⎦ in Korea. Results of this study provide room for debate and response regarding leisure experience and sustainability of recreation service in local community based on characteristics of each cluster. This study semanticized adults living in Gyeonggi-do having the highest population density in Korea by conducting K-means clustering. This study segmented subjects into three clusters. Characteristics of each cluster were determined and t-test was conducted to determine associations among the main purpose of leisure activities, leisure constraints, and quality of life. As a result, adults living in Gyeonggi-do were divided into “dissatisfaction with leisure resource”, “dissatisfaction with the quality of life”, and “sensitive to the leisure constraints” clusters. Their desire for improvement for quality of life and leisure activity were definitely clear. This means that results of this study through segmentation based on leisure index are meaningful as baseline data to suggest an actual policy plan.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation

2021 ◽  
Vol 21 (4) ◽  
pp. 1-10
Author(s):  
V. Gomathy ◽  
K. Janarthanan ◽  
Fadi Al-Turjman ◽  
R. Sitharthan ◽  
M. Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Mortality Rate ◽  
Respiratory Diseases ◽  
Past Research ◽  
Viral Disease ◽  
Machine Learning Method ◽  
Learning Method ◽  
Death Rates ◽  
Using Data

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

Intrusion Detection Using Data Fusion and Machine Learning

2018 ◽  
Author(s):  
Jridi Mohamed Hechmi ◽  
Hacen Khlaifi ◽  
Amine Bouatay ◽  
Amira Zrelli ◽  
Tahar Ezzedine
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Data Fusion ◽  
Using Data
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