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Academic Articles & Research Papers
Showing 20 of 2,713 academic articles
Chang Z, Xu Z, Tang Q, Du M et al. • Advanced science (Weinheim, Baden-Wurttemberg, Germany)
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1002/advs.78666
PMC: PMC13735436
Pitzalis M, Larcher V, Sanchez MGB, Giurfa M et al. • The Journal of comparative neurology
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1002/cne.70221
PMC: PMC13737438
Mortaza Aghbashlo, Wanxi Peng, Meisam Tabatabaei, Soteris A. Kalogirou et al. • Progress in Energy and Combustion Science
(2021)
OPENALEX
Open Access
DOI: https://doi.org/10.1016/j.pecs.2021.100904
Simon J. Caton, Christian Stefan Haas • ACM Computing Surveys
(2023)
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or pe...
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cc-by
DOI: https://doi.org/10.1145/3616865
Du Z, Xi M, Tang T, Yang K et al. • PLOS One
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1371/journal.pone.0361713
PMC: PMC13739232
Zhainagul Khamitova, Gulmira Omarova, Akerke Akanova, Aliya Abdukarimova et al. • Computers
(2026)
This study presents NORIA, an availability-aware multitask learning framework for clinical prediction from incomplete electronic health records (EHRs). We analyzed a public dataset of 77,724 patients and 107 numerical features. To prevent t...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/computers15100723
Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins • Progress in Aerospace Sciences
(2022)
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.paerosci.2022.100849
José Padarian, Budiman Minasny, Alex B. McBratney • SOIL
(2020)
Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proxi...
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DOI: https://doi.org/10.5194/soil-6-35-2020
Sun Ping • Materials Research Express
(2026)
Polymer solar cells (PSCs) rely on labor-intensive trial-and-error experiments to optimize power conversion efficiency (PCE), due to complex coupling of molecular electronic properties, blend morphology and fabrication parameters. This work...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/2053-1591/aeaacc
Yagang Zhang • InTech eBooks
(2010)
The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks...
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Open Access
DOI: https://doi.org/10.5772/225
Drew Conway, J.M. White • CERN Document Server (European Organization for Nuclear Research)
(2012)
If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John...
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Tan Jiang, Shijia Zhang, Shijia Zhang, Deyou Jiang • Frontiers in Psychiatry
(2026)
BackgroundAnxiety and depressive symptoms are prevalent among university students; however, their risk factors are unlikely to operate in isolation. Instead, they may be organized in a multilevel structure that progressively approaches emot...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fpsyt.2026.1809332
Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou et al. • Scientific Reports
(2025)
Abstract The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundam...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-025-32510-z
Albert P. Bartók, Sandip De, Carl R. Poelking, Noam Bernstein et al. • Science Advances
(2017)
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a...
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cc-by-nc
DOI: https://doi.org/10.1126/sciadv.1701816
PMC: 5729016
Nkusi EM, Schneider JV, Wiegand M, Bengler K • Human factors
(2026)
PMC
PMC Public Access (View-Only)
DOI: 10.1177/00187208261460544
PMC: PMC13736686
Kostiantyn Fuks • Green, Blue and Digital Economy Journal
(2024)
The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...
DOAJ
Open Access (DOAJ)
DOI: 10.30525/2661-5169/2024-1-4
Claire Tardiveau, Claire Tardiveau, Guillaume Monneret, Guillaume Monneret et al. • Frontiers in Immunology
(2022)
Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed th...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fimmu.2022.1022750
Yves Kodratoff • CERN Document Server (European Organization for Nuclear Research)
(1988)
1 Why Machine Learning and AI: The Contributions of AI to Learning Techniques 2 Theoretical Foundations for Machine Learning 3 Representation of Complex Knowledge by Clauses 4 Representation of Knowledge about Actions and the Addition of Ne...
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Kaiyang Qu, Fei Guo, Xiangrong Liu, Yuan Lin et al. • Frontiers in Microbiology
(2019)
Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but...
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DOI: https://doi.org/10.3389/fmicb.2019.00827
PMC: 6482238
Jie Zhang, Zipeng Wang, Jinzheng Hu, Shihao Xiao et al. • Journal of Rock Mechanics and Geotechnical Engineering
(2022)
The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomen...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jrmge.2021.09.010