Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

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.

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Classification and photometric redshift estimation of quasars in photometric surveys

Proceedings of the International Astronomical Union ◽

10.1017/s1743921320001829 ◽

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.

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Effective Prediction of Heart Disease Using Data Mining and Machine Learning: A Review

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) ◽

10.1109/icais50930.2021.9395963 ◽

2021 ◽

Author(s):

Simran Verma ◽

Abhishek Gupta

Keyword(s):

Machine Learning ◽

Data Mining ◽

Heart Disease ◽

Using Data

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Efficient prediction of concentrating solar power plant productivity using data clustering

Solar Energy ◽

10.1016/j.solener.2021.06.002 ◽

2021 ◽

Vol 224 ◽

pp. 730-741

Author(s):

Janna Martinek ◽

Michael J. Wagner

Keyword(s):

Power Plant ◽

Data Clustering ◽

Solar Power ◽

Plant Productivity ◽

Solar Power Plant ◽

Concentrating Solar Power ◽

Efficient Prediction ◽

Using Data

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

ACM Transactions on Internet Technology ◽

10.1145/3424222 ◽

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.

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Using of data science in healthcare

Problems of Innovation and Investment Development ◽

10.33813/2224-1213.24.2021.15 ◽

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.

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