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Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks

Santu Mondal, Sneha Ray, Aritra Acharyya, Rudra Sankar Dhar et al. • IEEE Access (2025)

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regim...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3567410

Machine Learning in Earthquake Seismology

S. Mostafa Mousavi, Gregory C. Beroza • Annual Review of Earth and Planetary Sciences (2022)

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...

OPENALEX cc-by DOI: https://doi.org/10.1146/annurev-earth-071822-100323

Machine learning and medical education

Vijaya B. Kolachalama, Priya S. Garg • npj Digital Medicine (2018)

Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and othe...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0061-1 PMC: 6550167

A Study of Machine Learning in Healthcare

Rohan Bhardwaj, Ankita R. Nambiar, Debojyoti Dutta • IEEE Annual International Computer Software and Applications Conference (COMPSAC) (2017)

In the past few years, there has been significant developments in how machine learning can be used in various industries and research. This paper discusses the potential of utilizing machine learning technologies in healthcare and outlines ...

OPENALEX DOI: https://doi.org/10.1109/compsac.2017.164

Introduction to Machine Learning, Neural Networks, and Deep Learning.

Rene Y Choi, Aaron S. Coyner, Jayashree Kalpathy–Cramer, Michael F Chiang et al. • PubMed Central (2020)

Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods: A systematic literature search in PubMed was ...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1167/tvst.9.2.14 PMC: 7347027

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

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 Medicine

Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane • New England Journal of Medicine (2019)

Interview with Dr. Isaac Kohane on machine learning in medicine. (16:31)Download In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar pati...

OPENALEX DOI: https://doi.org/10.1056/nejmra1814259

Machine Learning in Medicine

Rahul C. Deo • Circulation (2015)

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular va...

OPENALEX Open Access DOI: https://doi.org/10.1161/circulationaha.115.001593 PMC: 5831252