A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

2018 ◽  
Vol 11 (3) ◽  
pp. 1114-1124 ◽  
Author(s):  
Micheal Omotayo Alabi ◽  
Ken Nixon ◽  
Ionel Botef
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Additive Manufacturing ◽  
Manufacturing Industry ◽  
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 Role of Machine Learning in Manufacturing Sector

2019 ◽  
Vol 8 (4) ◽  
pp. 2320-2328
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Relationship Of ◽  
The Impact ◽  
Maintenance Process

This paper discusses the basic concept Machine learning and its techniques, algorithms as well the impact of Machine Learning in Manufacturing processes and Industrial Production. There has been an unprecedented increase in the data available in the last couple of decades. This has enabled machine learning to be applied in various fields. Machine learning is field of study which enables the computer system to learn automatically as well as improve from experience and perform various tasks without explicit instructions. The primary aim of machine learning is to allow the computers learn automatically without human assistance or intervention and adjust actions accordingly It is being employed in many fields. Manufacturing one area where machine learning is very useful. This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among others. This can be done by employing various machine learning techniques and algorithms using concepts such as deep learning, neural networks, supervised, unsupervised and reinforcement learning. The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed. Industry 4.0 or fourth revolution that has occurred in manufacturing which deals with advent of automation in manufacturing industry and its significance is discussed. We take a look at the various benefits and applications of machine learning in the field of manufacturing engineering. This paper also discusses the various challenges and future scope of employing machine learning in the manufacturing

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 Indonesia Network Infrastructures and Workforce Adequacy to Implement Machine Learning for Large-Scale Manufacturing

2021 ◽  
Vol 8 (1) ◽  
pp. 1-16
Author(s):  
Steven Anderson ◽  
Ansarullah Lawi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Technological Development ◽  
Learning Technology ◽  
Monitoring And Control ◽  
Manufacturing Activity ◽  
Industrial Monitoring

Technological development prior to industrial revolution 4.0 incentivized manufacturing industries to invest into digital industry with the aim of increasing the capability and efficiency in manufacturing activity. Major manufacturing industry has begun implementing cyber-physical system in industrial monitoring and control. The system itself will generate large volumes of data. The ability to process those big data requires algorithm called machine learning because of its ability to read patterns of big data for producing useful information. This study conducted on premises of Indonesia’s current network infrastructure and workforce capability on supporting the implementation of machine learning especially in large-scale manufacture. That will be compared with countries that have a positive stance in implementing machine learning in manufacturing. The conclusions that can be drawn from this research are Indonesia current infrastructure and workforce is still unable to fully support the implementation of machine learning technology in manufacturing industry and improvements are needed.

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.

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