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Machine learning for real-time aggregated prediction of hospital admission for emergency patients

Zella King, Joseph Farrington, Martin Utley, Enoch Kung et al. • npj Digital Medicine (2022)

Abstract Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabi...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41746-022-00649-y

Evaluation of Predictive Maintenance by Marginal Utility: A Case Study on Offshore Wind

Rasmus Dovnborg Frederiksen, Grzegorz Radzki, Grzegorz Bocewicz, Peter Nielsen et al. • Applied Sciences (2026)

This paper investigates the relationship between the benefits achieved by predictive maintenance implementation and the related costs incurred intentionally or not. Specifically, we seek to establish an evaluation method for predictive main...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16178708

Non-linear and probabilistic state discretization functions for enhanced discrete reinforcement learning: Application on wind turbine pitch control

Alberto Gil-Maciá, J. Enrique Sierra-García, Matilde Santos • Machine Learning with Applications (2026)

Discrete reinforcement learning (RL) offers interpretability advantages compared to continuous reinforcement learning. Neural networks used in continuous RL behave like black boxes that do not show an obvious relationship between the system...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2026.100919

Machine learning in geosciences and remote sensing

David John Lary, Amir H. Alavi, Amir H. Gandomi, Annette L. Walker • Geoscience Frontiers (2015)

Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.gsf.2015.07.003

A Review on Fairness in Machine Learning

Dana Pessach, Erez Shmueli • ACM Computing Surveys (2022)

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college ad...

OPENALEX DOI: https://doi.org/10.1145/3494672

Python 3 for Machine Learning

Oswald Campesato • De Gruyter eBooks (2020)

This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fun...

OPENALEX DOI: https://doi.org/10.1515/9781683924937

Foundations of Machine Learning

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar • The MIT Press eBooks (2018)

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference fo...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/10290.001.0001

MATLAB Machine Learning

Michael Paluszek, Stephanie J. Thomas • Apress eBooks (2016)

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It prov...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-2250-8

What is Machine Learning? A Primer for the Epidemiologist

Qifang Bi, Katherine E Goodman, Joshua Kaminsky, Justin Lessler • American Journal of Epidemiology (2019)

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...

OPENALEX DOI: https://doi.org/10.1093/aje/kwz189

Machine Learning in Materials Science

Tim Mueller, Aaron Gilad Kusne, Rampi Ramprasad • Reviews in computational chemistry (2016)

This chapter addresses the role that data-driven approaches, especially machine learning methods, are expected to play in materials research in the immediate future. Machine learning, an important part of artificial intelligence, has made m...

OPENALEX DOI: https://doi.org/10.1002/9781119148739.ch4

Perspectives in machine learning for wildlife conservation

Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe et al. • Nature Communications (2022)

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently d...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-022-27980-y PMC: 8828720