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Showing 20 of 2,192 academic articles
Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji et al. • IEEE Access
(2024)
The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, t...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3347725
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
Madeleine J Smith, Sandra Reeder, Fiona Waugh, Shobi Sivathamboo et al. • JMIR AI
(2026)
Abstract
BackgroundAntiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning...
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Open Access (DOAJ)
DOI: 10.2196/85047
Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis
(2019)
Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...
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other-oa
DOI: https://doi.org/10.1021/acscatal.9b04321
Fahad Ahmed Khokhar, Tommaso Zoppi, Luigi Cennini, Andrea Ceccarelli et al. • Scientific Reports
(2026)
Abstract The last decade has seen a surging opportunity for developing autonomous systems (e.g., fully autonomous driving, computer-guided robotic surgery, mobile robots for inspections and surveillance, and manufacturing robots) to relieve...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-45091-2
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...
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Open Access (DOAJ)
DOI: 10.1038/s41746-022-00649-y
Joseph A. Cruz, David Scott Wishart • Cancer Informatics
(2006)
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, ...
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cc-by-nc
DOI: https://doi.org/10.1177/117693510600200030
PMC: 2675494
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...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.gsf.2015.07.003
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...
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DOI: https://doi.org/10.1515/9781683924937
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...
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DOI: https://doi.org/10.1002/9781119148739.ch4
Willian Borges De Lemos, Lucas De Angelo Martins Ribeiro, Vanessa Telles Da Silva, Alessandro De Lima Bicho et al. • IEEE Access
(2025)
Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3643796
Hilal M. El Misilmani, Tarek Naous • International Conference on High Performance Computing & Simulation (HPCS)
(2019)
With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently...
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DOI: https://doi.org/10.1109/hpcs48598.2019.9188224
El‐Ghazali Talbi • ACM Computing Surveys
(2021)
During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...
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Open Access
DOI: https://doi.org/10.1145/3459664
Adi Laurentiu Tarca, Vincent J. Carey, Xuewen Chen, Roberto J. Romero et al. • PLoS Computational Biology
(2007)
The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechan...
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public-domain
DOI: https://doi.org/10.1371/journal.pcbi.0030116
PMC: 1904382
Noémie Letellier, Caitlin G. Jones-Ngo, Michael W. Cheung, Rosana Aguilera et al. • Environment International
(2025)
Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience dispropor...
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Open Access (DOAJ)
DOI: 10.1016/j.envint.2025.109955
Yu‐Chen Lo, Stefano Rensi, Wen Torng, Russ Biagio Altman • Drug Discovery Today
(2018)
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learn...
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cc-by
DOI: https://doi.org/10.1016/j.drudis.2018.05.010
PMC: 6078794
Shai Shalev‐Shwartz, Shai Ben-David • Cambridge University Press eBooks
(2014)
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book prov...
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DOI: https://doi.org/10.1017/cbo9781107298019
David Barber • Cambridge University Press eBooks
(2012)
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot ...
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DOI: https://doi.org/10.1017/cbo9780511804779
Lei Zhou, Yuntong Song, Weiqi Ji, Haiqiao Wei • Energy and AI
(2021)
Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicle...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.egyai.2021.100128
Harry Surden • Colorado Law Scholarly Commons (University of Colorado Colorado Springs)
(2014)
This Article explores the application of machine learning techniques within the practice of law. Broadly speaking “machine learning” refers to computer algorithms that have the ability to “learn” or improve in performance over time on some ...
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Open Access