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Academic Articles & Research Papers
Showing 20 of 1,021 academic articles
Sebastian Macaluso, David Shih • Journal of High Energy Physics
(2018)
Abstract We apply computer vision with deep learning — in the form of a convolutional neural network (CNN) — to build a highly effective boosted top tagger. Previous work (the “DeepTop” tagger of Kasieczka et al) has shown that a CNN-based ...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/JHEP10(2018)121
Christopher D. Manning • Computational Linguistics
(2015)
Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...
OPENALEX
Open Access
DOI: https://doi.org/10.1162/coli_a_00239
Xue-Wen Chen, Xiaotong Lin • IEEE Access
(2014)
Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language...
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Open Access
DOI: https://doi.org/10.1109/access.2014.2325029
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science
(2016)
The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...
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DOI: https://doi.org/10.3389/fenvs.2015.00080
Andrea Steuwe, Birte Valentin, Oliver T. Bethge, Alexandra Ljimani et al. • Diagnostics
(2022)
Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenu...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/diagnostics12071627
Ajantha Vijayakumar, Joseph Abraham Sundar Koilraj, Muthaiah Rajappa • International Journal of Computational Intelligence Systems
(2025)
Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy,...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44196-025-00986-2
Ajay Shrestha, Ausif Mahmood • IEEE Access
(2019)
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The pain...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/access.2019.2912200
Cyprien Alexandre, Rodolphe Devillers, David Mouillot, Raphael Seguin et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
(2024)
Detecting and tracking ships remotely is now required in a wide range of contexts, from military security to illegal immigration control, as well as the management of fisheries and marine protected areas. Among the available methods, radar ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/JSTARS.2024.3437187
Erik Gawehn, Jan A. Hiss, Gisbert Schneider • Molecular Informatics
(2015)
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...
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DOI: https://doi.org/10.1002/minf.201501008
Khawja Imran Masud, Md Yasin Zihad, Mehedi Hasan Shuvo, Mst Raonik Jannat et al. • PLoS ONE
(2026)
Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computa...
DOAJ
Open Access (DOAJ)
DOI: 10.1371/journal.pone.0356383
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs14081877
Sylvain Christin, Éric Hervet, Nicolas Lecomte • Methods in Ecology and Evolution
(2019)
Abstract A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized ...
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Open Access
DOI: https://doi.org/10.1111/2041-210x.13256
Shaorong Zhang, Shaorong Zhang, Qihui Wang, Benxin Zhang et al. • Frontiers in Neuroscience
(2023)
IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...
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Open Access (DOAJ)
DOI: 10.3389/fnins.2023.1292724
Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics
(2016)
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...
OPENALEX
Open Access
DOI: https://doi.org/10.1093/bib/bbw068
Dimitrios Papamartzivanos, Felix Gomez Marmol, Georgios Kambourakis • IEEE Access
(2019)
The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2019.2893871
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...
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DOI: https://doi.org/10.1145/3616865
Jeremy M. Watt, Reza Borhani, Aggelos K. Katsaggelos • Cambridge University Press eBooks
(2020)
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuiti...
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DOI: https://doi.org/10.1017/9781108690935
Gengke Lai, Yongguang Zhang • Geophysical Research Letters
(2025)
Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiome...
DOAJ
Open Access (DOAJ)
DOI: 10.1029/2024GL114492
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...
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DOI: https://doi.org/10.1038/s41467-017-00839-3
PMC: 5636838
Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, Lily H. Peng • JAMA
(2019)
In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and e...
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DOI: https://doi.org/10.1001/jama.2019.16489