Showing 20 of 2,239 academic articles
Gustau Camps‐Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein • Wiley eBooks
(2021)
Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only re...
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Open Access
DOI: https://doi.org/10.1002/9781119646181
ZHANG Bin1, YIN Haoyu2, ZHANG Tengfei2, WANG Jiangyu1, LU Di1, WANG Lianjie1 • Yuanzineng kexue jishu
(2026)
To meet the computational efficiency requirements for multi-physics simulations in the design and development of advanced reactor cores, a neural network-based acceleration method was investigated for multi-physics coupling calculations in ...
DOAJ
Open Access (DOAJ)
DOI: 10.7538/yzk.2025.youxian.0668
Marco Paracchini, Marco Marcon, Federica Villa, Franco Zappa et al. • Sensors
(2020)
The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon av...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s20216102
Jing Liu, Xuesong Hai, Keqin Li • Future Internet
(2023)
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/fi16010006
Jürgen Schmidhuber • Neural Networks
(2014)
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Open Access
DOI: https://doi.org/10.1016/j.neunet.2014.09.003
Jeroen A. W. M. van der Laak, Geert J. S. Litjens, Francesco Ciompi • Nature Medicine
(2021)
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Open Access
DOI: https://doi.org/10.1038/s41591-021-01343-4
Ge Wang, Jong Chul Ye, Bruno De Man • Nature Machine Intelligence
(2020)
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DOI: https://doi.org/10.1038/s42256-020-00273-z
Zhi He, Chengle Zhou, Liwei Zou, Suhong Zhou et al. • International Journal of Digital Earth
(2025)
Accurately monitoring disaster effects is a crucial task in relief efforts (e.g. typhoon rescue). Social media data plays a vital role in disaster management, while deep learning-based methods gain more attention in typhoon disaster researc...
DOAJ
Open Access (DOAJ)
DOI: 10.1080/17538947.2025.2517790
Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org
(2018)
Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...
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DOI: https://doi.org/10.20944/preprints201810.0218.v1
Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface
(2018)
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...
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DOI: https://doi.org/10.1098/rsif.2017.0387
PMC: 5938574
Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety
(2019)
Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...
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DOI: https://doi.org/10.1111/1541-4337.12492
P. Meyer, Vincent Noblet, C. Mazzara, Alex Lallement • Computers in Biology and Medicine
(2018)
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DOI: https://doi.org/10.1016/j.compbiomed.2018.05.018
Geert J. S. Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud A. A. Setio et al. • Medical Image Analysis
(2017)
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Open Access
DOI: https://doi.org/10.1016/j.media.2017.07.005
Heang‐Ping Chan, Ravi K. Samala, Lubomir M. Hadjiiski, Chuan Zhou • Advances in experimental medicine and biology
(2020)
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Open Access
DOI: https://doi.org/10.1007/978-3-030-33128-3_1
PMC: 7442218
Chensi Cao, Feng Liu, Hai Tan, Deshou Song et al. • Genomics Proteomics & Bioinformatics
(2018)
Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...
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DOI: https://doi.org/10.1016/j.gpb.2017.07.003
PMC: 6000200
Wenjun Zhang, Guosheng Yang, Yingzi Lin, Chunli Ji et al. • World Automation Congress (WAC)
(2018)
Deep learning has become a buzz word recently. However, there is a lack of unified definition to deep learning in literature. The goal of this paper is to overview various definitions to deep learning and to show their limitations. Finally,...
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DOI: https://doi.org/10.23919/wac.2018.8430387
Kenji Suzuki • Radiological Physics and Technology
(2017)
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DOI: https://doi.org/10.1007/s12194-017-0406-5
Paschalis Bizopoulos, Dimitris Koutsouris • IEEE Reviews in Biomedical Engineering
(2018)
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data....
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Open Access
DOI: https://doi.org/10.1109/rbme.2018.2885714
Pramila P. Shinde, Seema Shah • International Conference on Computing Communication Control and automation
(2018)
Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well...
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DOI: https://doi.org/10.1109/iccubea.2018.8697857
Josh Patterson, Gibson Adam (2017)
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This han...
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