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Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau

Junpo Yu, Yajun Si, Wen Zhao, Zeyu Zhou et al. • Plants (2025)

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and...

DOAJ Open Access (DOAJ) DOI: 10.3390/plants14152391

Deep Learning for Spatio-Temporal Data Mining: A Survey

Senzhang Wang, Jiannong Cao, Philip S. Yu • IEEE Transactions on Knowledge and Data Engineering (2020)

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2020.3025580

Deep Learning and Its Application to LHC Physics

D. Guest, Kyle Stuart Cranmer, Daniel Whiteson • Annual Review of Nuclear and Particle Science (2018)

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex ...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-nucl-101917-021019

A General Survey on Attention Mechanisms in Deep Learning

Gianni Brauwers, Flavius Frăsincar • IEEE Transactions on Knowledge and Data Engineering (2021)

Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. ...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2021.3126456

Deep Learning: The Good, the Bad, and the Ugly

Thomas Serre • Annual Review of Vision Science (2019)

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains...

OPENALEX DOI: https://doi.org/10.1146/annurev-vision-091718-014951

Deep Learning for Population Genetic Inference

Sara Sheehan, Yun S. Song • PLoS Computational Biology (2016)

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep...

OPENALEX cc-by DOI: https://doi.org/10.1371/journal.pcbi.1004845 PMC: 4809617

Machine learning and deep learning applications in microbiome research

Ricardo Hernández Medina, Svetlana Kutuzova, Knud Nor Nielsen, Joachim Johansen et al. • ISME Communications (2022)

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and bio...

OPENALEX cc-by DOI: https://doi.org/10.1038/s43705-022-00182-9 PMC: 9723725

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Fatima Rauf, Muhammad Attique Khan, Ali Kashif Bashir, Kiran Jabeen et al. • Frontiers in Medicine (2023)

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmed.2023.1330218

DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity

Guoqiang Zhou, Ben Li, Weifeng Zhang, Shudi Bao et al. • BMC Bioinformatics (2026)

Abstract Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D complexes have shown strong potential for drug-target affinity prediction. However, mos...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-026-06481-5

A comprehensive review on ensemble deep learning: Opportunities and challenges

Ammar Mohammed, Rania Kora • Journal of King Saud University - Computer and Information Sciences (2023)

In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms t...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.jksuci.2023.01.014

Deep learning for cardiovascular medicine: a practical primer

Chayakrit Krittanawong, Kipp W. Johnson, Robert S. Rosenson, Zhen Wang et al. • European Heart Journal (2019)

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...

OPENALEX Open Access DOI: https://doi.org/10.1093/eurheartj/ehz056 PMC: 6600129

Understanding tourist engagement with traditional dwellings through multimodal deep learning: a case study of the Gao Family Compound, Xi’an

Liyue Wu, Qinchuan Zhan, Yanjun Li • Humanities & Social Sciences Communications (2026)

Abstract This study applies a multimodal deep learning approach to examine tourist engagement with traditional architectural heritage. The Gao Family Compound in Xi’an serves as a representative case. Social-media texts and images from Weib...

DOAJ Open Access (DOAJ) DOI: 10.1057/s41599-026-07879-5