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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

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

COVID-19 Outbreak Prediction with Machine Learning

Sina Ardabili, Amir Hosein Mosavi, Pedram Ghamisi, Ferdinánd Filip et al. • Algorithms (2020)

Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simpl...

OPENALEX cc-by DOI: https://doi.org/10.3390/a13100249

A review of the application of machine learning in water quality evaluation

Mengyuan Zhu, Jiawei Wang, Xiao Yang, Yu Zhang et al. • Eco-Environment & Health (2022)

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.eehl.2022.06.001 PMC: 10702893

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Masoud Mahdianpari, Bahram Salehi, Mohammad Rezaee, Fariba Mohammadimanesh et al. • Remote Sensing (2018)

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applicatio...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs10071119

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler et al. • Chemical Reviews (2022)

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better unde...

OPENALEX DOI: https://doi.org/10.1021/acs.chemrev.2c00061

Hybrid-Timescale Physics-Informed Neural Network for Electrical Equivalent Impedance Identification in Induction Heating Systems

Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan, Denis Navarro et al. • IEEE Open Journal of the Industrial Electronics Society (2026)

This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction...

DOAJ Open Access (DOAJ) DOI: 10.1109/OJIES.2026.3663897

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

Muhidul Islam Khan, Luca Reggiani, Muhammad Mahtab Alam, Yannick Le Moullec et al. • Sensors (2020)

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the ...

DOAJ Open Access (DOAJ) DOI: 10.3390/s20226692

Opportunities and Challenges for Machine Learning in Materials Science

Dane D. Morgan, Ryan Jacobs • Annual Review of Materials Research (2020)

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid chang...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-matsci-070218-010015

Machine learning in acoustics: Theory and applications

Michael J. Bianco, Peter Gerstoft, James A. Traer, Emma Reeves Ozanich et al. • The Journal of the Acoustical Society of America (2019)

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep lear...

OPENALEX Open Access DOI: https://doi.org/10.1121/1.5133944

Machine Learning in Healthcare

Hafsa Habehh, Suril Gohel • Current Genomics (2021)

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...

OPENALEX Open Access DOI: https://doi.org/10.2174/1389202922666210705124359 PMC: 8822225

Revealing the dynamic evolution of interfacial water during Mg anodic dissolution

Xinchen Xu, Xingpeng Liao, Gaoning Shi, Yuyang Chen et al. • Journal of Magnesium and Alloys (2026)

Though magnesium alloys possess attractive mechanical properties, their applications are severely limited by galvanic corrosion, due to the rapid anodic dissolution and anomalous hydrogen evolution. In this work, well-tempered metadynamics ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jma.2026.102217

Development of a Transfer Learning Technique for Rapid Adaptation of Thermal Compensation Models to Long-Term Machine Thermal Behavior Changes

Chia-Chin Chuang, Zheng-Wei Lin Chi, Tzu-Chien Kuo, Che-Jui Chang et al. • Machines (2026)

Structural aging and environmental changes associated with long-term operation can substantially modify the thermal behavior of machine tools, diminishing the accuracy of existing thermal compensation models. Traditional neural network appr...

DOAJ Open Access (DOAJ) DOI: 10.3390/machines14030309

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

Cheng-Hong Yang, Chih-Hsien Wu, Kuei-Hau Luo, Huang-Chih Chang et al. • Ecotoxicology and Environmental Safety (2024)

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This st...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ecoenv.2024.116885