scholarly journals Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

Engineering ◽  
2019 ◽  
Vol 5 (6) ◽  
pp. 1017-1026 ◽  
Author(s):  
Teng Zhou ◽  
Zhen Song ◽  
Kai Sundmacher
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Materials Design ◽  
Materials Research ◽  
Applications Of Machine Learning

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

2019 ◽  
Vol 24 (34) ◽  
pp. 3998-4006
Author(s):  
Shijie Fan ◽  
Yu Chen ◽  
Cheng Luo ◽  
Fanwang Meng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Precision Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Accelerated Studies ◽  
Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

scholarly journals Applications of Machine Learning For Precision Agriculture and Smart Farming

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Sai Gurrapu ◽  
Nazmul Sikder ◽  
Pei Wang ◽  
Nitish Gorentala ◽  
Madison Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Precision Agriculture ◽  
Farm Management ◽  
Time Data ◽  
Quantitative Metrics ◽  
Smart Farming ◽  
Applications Of Machine Learning ◽  
Financial Indices

Recent deglobalization movements have had a transformativeimpact and an increase in uncertainty on manyindustries. The advent of technology, Big Data, and MachineLearning (ML) further accelerated this disposition.Many quantitative metrics that measure the globaleconomy’s equilibrium have strong and interdependentrelationships with the agricultural supply chain and internationaltrade flows. Our research employs econometricsusing ML techniques to determine relationshipsbetween commonplace financial indices (such asthe DowJones), and the production, consumption, andpricing of global agricultural commodities. Producersand farmers can use this data to make their productionmore effective while precisely following global demand.In order to make production more efficient, producerscan implement smart farming and precision agriculturemethods using the processes proposed. It enablesthem to have a farm management system that providesreal-time data to observe, measure, and respondto variability in crops. Drones and robots can be usedfor precise crop maintenance that optimize yield returnswhile minimizing resource expenditure. We developML models which can be used in combinationwith the smart farm data to accurately predict the economicvariables relevant to the farm. To ensure the accuracyof the insights generated by the models, ML assuranceis deployed to evaluate algorithmic trust.

scholarly journals Applications of Machine Learning in Cardiac Electrophysiology

2020 ◽  
Vol 9 (2) ◽  
pp. 71-77
Author(s):  
Rahul G Muthalaly ◽  
Robert M Evans ◽  
◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Diagnostic Accuracy ◽  
Cardiac Electrophysiology ◽  
Electronic Devices ◽  
Cardiac Implantable Electronic Devices ◽  
Supervised And Unsupervised Learning ◽  
The World ◽  
Applications Of Machine Learning

Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In electrophysiology, ML has applications for use in every stage of patient care. However, its use is still in infancy. This article will introduce the potential of ML, before discussing the concept of big data and its pitfalls. The authors review some common ML methods including supervised and unsupervised learning, then examine applications in cardiac electrophysiology. This will focus on surface electrocardiography, intracardiac mapping and cardiac implantable electronic devices. Finally, the article concludes with an overview of how ML may impact on electrophysiology in the future.

scholarly journals Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol (Preprint)

2018 ◽  
Author(s):  
Diwakar Mohan ◽  
Jean Juste Harrisson Bashingwa ◽  
Pierre Dane ◽  
Sara Chamberlain ◽  
Nicki Tiffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Health Information ◽  
Program Effectiveness ◽  
Health Workers ◽  
Interactive Voice Response ◽  
Digital Health ◽  
Health Programs ◽  
Frontline Health Workers ◽  
Applications Of Machine Learning

BACKGROUND Digital health programs, which encompass the subsectors of health information technology, mobile health, electronic health, telehealth, and telemedicine, have the potential to generate “big data.” OBJECTIVE Our aim is to evaluate two digital health programs in India—the maternal mobile messaging service (Kilkari) and the mobile training resource for frontline health workers (Mobile Academy). We illustrate possible applications of machine learning for public health practitioners that can be applied to generate evidence on program effectiveness and improve implementation. Kilkari is an outbound service that delivers weekly gestational age–appropriate audio messages about pregnancy, childbirth, and childcare directly to families on their mobile phones, starting from the second trimester of pregnancy until the child is one year old. Mobile Academy is an Interactive Voice Response audio training course for accredited social health activists (ASHAs) in India. METHODS Study participants include pregnant and postpartum women (Kilkari) as well as frontline health workers (Mobile Academy) across 13 states in India. Data elements are drawn from system-generated databases used in the routine implementation of programs to provide users with health information. We explain the structure and elements of the extracted data and the proposed process for their linkage. We then outline the various steps to be undertaken to evaluate and select final algorithms for identifying gaps in data quality, poor user performance, predictors for call receipt, user listening levels, and linkages between early listening and continued engagement. RESULTS The project has obtained the necessary approvals for the use of data in accordance with global standards for handling personal data. The results are expected to be published in August/September 2019. CONCLUSIONS Rigorous evaluations of digital health programs are limited, and few have included applications of machine learning. By describing the steps to be undertaken in the application of machine learning approaches to the analysis of routine system-generated data, we aim to demystify the use of machine learning not only in evaluating digital health education programs but in improving their performance. Where articles on analysis offer an explanation of the final model selected, here we aim to emphasize the process, thereby illustrating to program implementors and evaluators with limited exposure to machine learning its relevance and potential use within the context of broader program implementation and evaluation. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11456

Applications of Machine Learning in Cyber Forensics

2021 ◽  
pp. 29-46
Author(s):  
Kishore Rajendiran ◽  
Kumar Kannan ◽  
Yongbin Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Cyber Attacks ◽  
Digital Evidence ◽  
Cyber Attack ◽  
Multiple Sources ◽  
Research Issues ◽  
Cyber Forensics ◽  
File Formats ◽  
Applications Of Machine Learning

Nowadays, individuals and organizations experience an increase in cyber-attacks. Combating such cybercrimes has become the greatest struggle for individual persons and organizations. Furthermore, the battle has heightened as cybercriminals have gone a step ahead, employing the complicated cyber-attack technique. These techniques are minute and unobtrusive in nature and habitually disguised as authentic requests and commands. The cyber-secure professionals and digital forensic investigators enforce by collecting large and complex pools of data to reveal the potential digital evidence (PDE) to combat these attacks and helps investigators to arrive at particular conclusions and/or decisions. In cyber forensics, the challenging issue is hard for the investigators to make conclusions as the big data often comes from multiple sources and in different file formats. The objective is to explore the possible applications of machine learning (ML) in cyber forensics and to discuss the various research issues, the solutions of which will serve out to provide better predictions for cyber forensics.

scholarly journals Machine Learning in Big Data

2015 ◽  
Vol 4 (4) ◽  
pp. 117 ◽  
Author(s):  
Lidong Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Value Creation ◽  
New Methods ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

<p>Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.</p>

Small Steps with Big Data: Using Machine Learning in Energy and Environmental Economics

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Matthew C. Harding ◽  
Carlos Lamarche
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Environmental Economics ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Annual Review ◽  
Publication Date ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Media Data

This article reviews recent endeavors to incorporate big data and machine learning techniques into energy and environmental economics research. We find that novel datasets, from high frequency smart meter data to satellite images and social media data, are already used by researchers. At the same time most of the analyses rely on traditional econometric techniques. Nevertheless, we find applications of machine learning models that address the high dimensionality of the data and seek out new and better strategies for estimating heterogenous treatment effects. We provide an introduction to the main themes in machine learning, which are likely to be of use to economists in energy and environmental economics, and illustrate them using a real data example derived from an energy efficiency program evaluation. We provide the data and code in order to stimulate further research in this area. Expected final online publication date for the Annual Review of Resource Economics, Volume 13 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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