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Showing 20 of 1,495 academic articles

A machine learning and clustering-based approach for county-level COVID-19 analysis.

Charles Nicholson, Lex Beattie, Matthew Beattie, Talayeh Razzaghi et al. • PLoS ONE (2022)

COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pone.0267558

Integrative multiomics analysis of platelet-related genes unveils molecular subtypes and prognostic signatures in acute myeloid leukemia

Fangmin Zhong, Fangyi Yao, Zihao Wang, Bo Huang et al. • npj Precision Oncology (2025)

Abstract Acute myeloid leukemia (AML) remains challenging due to molecular heterogeneity and limited prognostic models integrating tumor microenvironment dynamics. While thrombocytopenia correlates with poor outcomes, the roles of platelet-...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41698-025-01137-8

Machine learning for renewable energy materials

Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung • Journal of Materials Chemistry A (2019)

Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accel...

OPENALEX DOI: https://doi.org/10.1039/c9ta02356a

Machine Learning and AI for Risk Management

Saqib Aziz, Michael Dowling • Palgrave studies in digital business & enabling technologies (2018)

We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk managementRisk management...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1007/978-3-030-02330-0_3

Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features

Mathieu Calvat, Chris Bean, Dhruv Anjaria, Hyoungryul Park et al. • npj Computational Materials (2025)

Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-b...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41524-025-01770-8

A machine-learning approach for predicting the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome

Dong-Dong Wang, Ya-Feng Li, Yi-Zhen Mao, Su-Mei He et al. • Frontiers in Nutrition (2022)

The present study aimed to explore the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predict an appropriate dosage schedule using a machine-learning approach. Data were obtained fro...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnut.2022.851275

Machine learning in breast MRI

Beatriu Reig, Laura C. Heacock, Krzysztof J. Geras, Linda P. Moy • Journal of Magnetic Resonance Imaging (2019)

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmenta...

OPENALEX DOI: https://doi.org/10.1002/jmri.26852 PMC: 7085409

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

Orges Koci, Richard K. Russell, M. Guftar Shaikh, Christine Edwards et al. • Microbiome (2024)

Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40168-024-01834-9

Bypassing the Kohn-Sham equations with machine learning

Felix Brockherde, Leslie Vogt-Maranto, Li Li, Mark E. Tuckerman et al. • Nature Communications (2017)

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy fun...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-017-00839-3 PMC: 5636838

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

Efficient covid19 disease diagnosis based on cough signal processing and supervised machine learning

Khaled Bensid, Abdelhai Lati, Azeddine Benlamoudi, Brahim Elkhalil Ghouar et al. • Diagnostyka (2022)

The spread of the coronavirus has claimed the lives of millions around the world, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of th...

DOAJ Open Access (DOAJ) DOI: 10.29354/diag/156751

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

RDDA: Rectified Discriminatory Delta-Adjust

Ziad F. Doughan, Sari S. Itani • IEEE Access (2025)

This paper introduces Rectified Discriminatory Delta-Adjust (RDDA), a novel methodology that enhances interpolation-based predictive modeling through adaptive sensitivity parameters. Building upon the foundational Delta-Adjust algorithm, RD...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3641537

Machine Learning for Cultural Heritage: A Survey

Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia et al. • Pattern Recognition Letters (2020)

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the unde...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.patrec.2020.02.017

Machine learning in cardiovascular medicine: are we there yet?

Khader Shameer, Kipp W. Johnson, Benjamin Scott Glicksberg, Joel T. Dudley et al. • Heart (2018)

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

OPENALEX DOI: https://doi.org/10.1136/heartjnl-2017-311198

Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity

Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He et al. • Machine Learning. Earth (2025)

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges fo...

DOAJ Open Access (DOAJ) DOI: 10.1088/3049-4753/ae0b70