scholarly journals Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 46
Author(s):  
Obuks Augustine Ejohwomu ◽  
Olakekan Shamsideen Oshodi ◽  
Majeed Oladokun ◽  
Oyegoke Teslim Bukoye ◽  
Nwabueze Emekwuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollutants ◽  
Data Science ◽  
Climatic Factors ◽  
Extreme Weather Events ◽  
Learning Methods ◽  
Rapid Urbanization ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Pm2.5 Concentration

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the 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.

scholarly journals Prediction of Concrete Properties Using Ensemble Machine Learning Methods

2020 ◽  
Vol 1625 ◽  
pp. 012024
Author(s):  
D Prayogo ◽  
D I Santoso ◽  
D Wijaya ◽  
T Gunawan ◽  
J A Widjaja
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Concrete Properties ◽  
Ensemble Machine Learning

scholarly journals Investigating Algorithmic Stock Market Trading using Ensemble Machine Learning Methods

Informatica ◽  
2020 ◽  
Vol 44 (3) ◽  
Author(s):  
Ramzi Saifan ◽  
Khaled Sharif ◽  
Mohammad Abu-Ghazaleh ◽  
Mohammad Abdel-Majeed
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Stock Market Trading

scholarly journals APPLICATION OF ENSEMBLE MACHINE LEARNING METHODS FOR PREDICTING THE TECHNICAL STATE OF AN OBJECT

2021 ◽  
Vol 23 (1) ◽  
pp. 111-114
Author(s):  
Yu.E. Kuvayskova ◽  
Keyword(s):  
Machine Learning ◽  
The State ◽  
Technical State ◽  
Supervised Machine Learning ◽  
Time Interval ◽  
Technical Documentation ◽  
Technical Object ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% –9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.

Sign in / Sign up

Export Citation Format

Share Document