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Fairness in Machine Learning: A Survey

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...

OPENALEX cc-by DOI: https://doi.org/10.1145/3616865

Availability-Aware Multitask Learning for Robust Clinical Prediction from Incomplete Electronic Health Records

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

Machine learning in aerodynamic shape optimization

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...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.paerosci.2022.100849

Data-driven performance prediction of polymer solar cells using multi-layer perceptron with Bayesian hyperparameter tuning

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

New Advances in Machine Learning

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...

OPENALEX Open Access DOI: https://doi.org/10.5772/225

Machine Learning for Hackers

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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Key factors and serial mediation analysis of anxiety and depression among university students: a cross-sectional study

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

A synergistic enhancement of the Ivy algorithm for GAN-based imbalanced classification

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

Machine learning unifies the modeling of materials and molecules

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...

OPENALEX cc-by-nc DOI: https://doi.org/10.1126/sciadv.1701816 PMC: 5729016

MARKETING ACTIVITIES OF IT COMPANIES: INFORMATION AND ORGANISATIONAL CAPABILITIES FOR DIGITAL PRODUCT DEVELOPMENT

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

A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients

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

Introduction to Machine Learning

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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Application of Machine Learning in Microbiology

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...

OPENALEX cc-by DOI: https://doi.org/10.3389/fmicb.2019.00827 PMC: 6482238

Bayesian machine learning-based method for prediction of slope failure time

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