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Showing 20 of 896 academic articles
Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC)
(2016)
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...
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DOI: https://doi.org/10.1109/yac.2016.7804882
Siwei Yu, Jianwei Ma, Wenlong Wang • Geophysics
(2019)
ABSTRACT Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the ...
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Open Access
DOI: https://doi.org/10.1190/geo2018-0668.1
Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data
(2019)
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...
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cc-by
DOI: https://doi.org/10.1186/s40537-019-0192-5
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
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DOI: https://doi.org/10.1148/rg.2017170077
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu • Journal of Computational Chemistry
(2017)
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...
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Open Access
DOI: https://doi.org/10.1002/jcc.24764
Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang et al. • Applied Sciences
(2024)
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...
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Open Access (DOAJ)
DOI: 10.3390/app14020774
Natasha Dropka, Stefan Ecklebe, Martin Holena • Crystals
(2021)
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs)...
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Open Access (DOAJ)
DOI: 10.3390/cryst11020138
Hao Yan, Xingao Li, Zhongliang Shi, Shuyuan Wang • IEEE Access
(2024)
Motion intent recognition research is one of the key challenges in achieving human-robot collaboration in rehabilitation robots. In the traditional method, intention recognition is performed based on the complete sEMG, however, due to the m...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3447275
Younggill Son, Woongchul Choi • Batteries
(2024)
With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical...
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Open Access (DOAJ)
DOI: 10.3390/batteries10060191
Vidya Baiju, Akshitha J, Namasiyava Naveen S • European Medical Journal
(2024)
Brain tumours are caused by the abnormal growth of cells in the brain. This occurs mainly due to genetic changes or exposure to X-ray radiation. When the tumours are detected early, they can be removed via surgery. The tumour can be removed...
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Open Access (DOAJ)
DOI: 10.33590/emj/NUUC2196
Drew Conway, J.M. White • CERN Document Server (European Organization for Nuclear Research)
(2012)
If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John...
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Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An • Energy and AI
(2021)
The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...
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DOI: https://doi.org/10.1016/j.egyai.2021.100049
Sergio Muñoz, Carlos Á. Iglesias • Big Data and Cognitive Computing
(2024)
The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...
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Open Access (DOAJ)
DOI: 10.3390/bdcc8100129
Yiming Mao, Ting Li, Wenqiang Lu, Chunhong Li et al. • Discover Oncology
(2025)
Abstract Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic i...
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Open Access (DOAJ)
DOI: 10.1007/s12672-025-03557-1
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...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.eehl.2022.06.001
PMC: 10702893
Stephen Marsland (2009)
Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is r...
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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...
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Open Access (DOAJ)
DOI: 10.1016/j.jrmge.2021.09.010
Ady Suwardi, Fuke Wang, Kun Xue, Ming‐Yong Han et al. • Advanced Materials
(2021)
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been ham...
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DOI: https://doi.org/10.1002/adma.202102703
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...
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DOI: https://doi.org/10.1136/heartjnl-2017-311198
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...
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Open Access (DOAJ)
DOI: 10.1088/3049-4753/ae0b70