scholarly journals Informatics and machine learning methods for health applications

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Li Shen ◽  
Xinghua Shi ◽  
Zhongming Zhao ◽  
Kai Wang
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Video Conferencing ◽  
Special Issue ◽  
High Quality ◽  
Intelligent Computing ◽  
Machine Learning Methods ◽  
Virtual Conference ◽  
Oral Presentations ◽  
Health Applications

AbstractThe 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9–10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.

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 Special Issue “Machine Learning in Insurance”

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 54 ◽  
Author(s):  
Vali Asimit ◽  
Ioannis Kyriakou ◽  
Jens Perch Nielsen
Keyword(s):  
Machine Learning ◽  
Special Issue ◽  
High Quality ◽  
Avant Garde

It is our pleasure to prologue the special issue on “Machine Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field [...]

scholarly journals Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

2018 ◽  
Vol 2 ◽  
pp. 247054701774755 ◽  
Author(s):  
Isaac R. Galatzer-Levy ◽  
Kelly V. Ruggles ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Research Domain Criteria ◽  
Computational Tools ◽  
Machine Learning Methods ◽  
Current Review ◽  
Health And Illness ◽  
Multiple Levels ◽  
Complex Relationships ◽  
Research Domain

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria initiative provides a theoretical framework to understand health and illness as the product of multiple interrelated systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, and environmental factors) as they relate to outcomes that are free from prior diagnostic benchmarks represent a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals.

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.

scholarly journals Special Issue on Selected Papers from IVAPP 2018

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 171
Author(s):  
Alexandru Telea ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Information Visualization ◽  
Visual Analytics ◽  
Data Science ◽  
Time Dependent ◽  
Dependent Data ◽  
Special Issue ◽  
Making Sense ◽  
Recent Developments

Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]

Advances of Quantum Machine Learning

2021 ◽  
pp. 257-275
Author(s):  
Bhanu Chander
Keyword(s):  
Machine Learning ◽  
Kernel Methods ◽  
High Quality ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Quantum Machine Learning ◽  
Time Quantum ◽  
Computational Resources ◽  
Future Consequences ◽  
Future Work

The basic idea of artificial intelligence and machine learning is that machines have the talent to learn from data, previous experience, and perform the work in future consequences. In the era of the digitalized world which holds big data has long-established machine learning methods consistently with requisite high-quality computational resources in numerous useful and realistic tasks. At the same time, quantum machine learning methods work exponentially faster than their counterparts by making use of quantum mechanics. Through taking advantage of quantum effects such as interference or entanglement, quantum computers can proficiently explain selected issues that are supposed to be tough for traditional machines. Quantum computing is unexpectedly related to that of kernel methods in machine learning. Hence, this chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work of QML.

scholarly journals Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal

Genes ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 505
Author(s):  
Manfred Grabherr ◽  
Bozena Kaminska ◽  
Jan Komorowski
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Dna Sequencing ◽  
Computational Power ◽  
Next Generation ◽  
Special Issue ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sequencing Technologies ◽  
Generation Sequencing

The massive increase in computational power over the recent years and wider applicationsof machine learning methods, coincidental or not, were paralleled by remarkable advances inhigh-throughput DNA sequencing technologies.[...]

Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language

2019 ◽  
Vol 44 (3) ◽  
pp. 348-361 ◽  
Author(s):  
Jiangang Hao ◽  
Tin Kam Ho
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
And Behavior ◽  
Python Programming

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

scholarly journals MACHINE LEARNING METHODS FOR IMPROVING THE QUALITY OF IMAGES FROM RAILWAY SURVEILLANCE CAMERAS

2021 ◽  
Vol 73 (1) ◽  
pp. 126-133
Author(s):  
B.S. Akhmetov ◽  
◽  
D.V. Isaykin ◽  
М.B. Bereke ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Content ◽  
Railway Transport ◽  
High Quality ◽  
Learning Methods ◽  
Training Time ◽  
Machine Learning Methods ◽  
Surveillance Cameras

The article shows the development of the methodology for changing the resolution of images obtained from CCTV cameras on railway transport. The research was carried out on the basis of the application of machine learning methods (MLM). Thanks to the implementation of this approach, it was possible to expand the functionality of the MMO. In particular, it is proposed to carry out the oversampling process with the target coefficient of information content of the image frames. This factor is applicable for both increasing and decreasing RI. This should provide a high quality resampling and, at the same time, reduce the training time for neural-like structures (NLS). The proposed solutions are characterized by a reduction in the size of the computing resources that are required for such a procedure.

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