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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...
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DOI: https://doi.org/10.1146/annurev-earth-071822-100323
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
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 ...
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DOI: https://doi.org/10.1167/tvst.9.2.14
PMC: 7347027
Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton et al. • BMC Bioinformatics
(2018)
Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D stru...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12859-018-2381-1
Nora Alhazmi, Rana Almazroa, Muhannad Alshehri, Saad Bin Shabib et al. • Journal of Orthodontic Science
(2026)
OBJECTIVES:
This study aimed to evaluate facial soft-tissue features in adults with skeletal Class I relationships using three-dimensional (3D) imaging and machine learning to identify sex-specific anthropometric patterns and provide refere...
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Open Access (DOAJ)
DOI: 10.4103/jos.jos_111_25
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...
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Open Access (DOAJ)
DOI: 10.1016/j.mlwa.2026.100919
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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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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DOI: https://doi.org/10.1016/j.gsf.2015.07.003
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...
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DOI: https://doi.org/10.7551/mitpress/10290.001.0001
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...
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DOI: https://doi.org/10.1093/aje/kwz189
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
Sumesh Kharnotia, Bhavna Arora, Ravdeep Kour • ICT Express
(2026)
The extensive embrace of Android has amplified malware risks, resulting in a need for better detection methods. This article investigates the area of static analysis, which analyses applications without execution by examining code and manif...
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Open Access (DOAJ)
DOI: 10.1016/j.icte.2026.01.005
Aleksanteri Hamalainen, Aku Karhinen, Jesse Miettinen, Zacharias Dahl et al. • IEEE Access
(2025)
Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3627885
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
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren • The Springer series on challenges in machine learning
(2019)
This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge ...
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DOI: https://doi.org/10.1007/978-3-030-05318-5
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
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich et al. • eNeuro
(2020)
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve deco...
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DOI: https://doi.org/10.1523/eneuro.0506-19.2020
PMC: 7470933
Ziyi Yin, Guowei Huang, Rui Zhao, Sen Wang et al. • Financial Innovation
(2024)
Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project f...
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Open Access (DOAJ)
DOI: 10.1186/s40854-024-00666-8
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
Subhabrata Sengupta, Rupayan Das, Satyajit Chakrabarti • Emitter: International Journal of Engineering Technology
(2024)
Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, iden...
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Open Access (DOAJ)
DOI: 10.24003/emitter.v12i2.855