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Academic Articles & Research Papers
Showing 20 of 621 academic articles
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 ...
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Open Access
DOI: https://doi.org/10.1146/annurev-nucl-101917-021019
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...
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DOI: https://doi.org/10.1371/journal.pcbi.1004845
PMC: 4809617
Aziz Siyaev, Geun-Sik Jo • IEEE Access
(2021)
In the emerging world of metaverses, it is essential for speech communication systems to be aware of context to interact with virtual assets in the 3D world. This paper proposes the metaverse for aircraft maintenance training and education ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3128616
Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang et al. • Land
(2023)
Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/land12061125
Liyan Luo, Liujun Zhang, Mei Wang, Zhenghong Liu et al. • IEEE Access
(2021)
In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event fr...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3123970
Nitin Kumar Chauhan, Krishna Pal Singh • International Conference on Computing, Power and Communication Technologies (GUCON)
(2018)
In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...
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DOI: https://doi.org/10.1109/gucon.2018.8675097
Berkman Sahiner, Aria X. Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang et al. • Medical Physics
(2018)
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to addr...
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Open Access
DOI: https://doi.org/10.1002/mp.13264
PMC: 9560030
Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan K.E. Westberg et al. • Electronics
(2019)
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...
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DOI: https://doi.org/10.3390/electronics8030292
Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru • IEEE Access
(2025)
The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have li...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3536553
Xiaozheng Li, Huazhen Wang, Huixin He, Jixiang Du et al. • BMC Bioinformatics
(2019)
Abstract Background Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural n...
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Open Access (DOAJ)
DOI: 10.1186/s12859-019-2617-8
Li Deng • APSIPA Transactions on Signal and Information Processing
(2014)
In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent develop...
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DOI: https://doi.org/10.1017/atsip.2013.9
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...
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Open Access
DOI: https://doi.org/10.1093/eurheartj/ehz056
PMC: 6600129
Yanming Yang, Xin Xia, David Lo, John Grundy • ACM Computing Surveys
(2021)
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstr...
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Open Access
DOI: https://doi.org/10.1145/3505243
Daniel W. Otter, Julian Richard Medina, Jugal Kumar Kalita • IEEE Transactions on Neural Networks and Learning Systems
(2020)
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learni...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2020.2979670
Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine Belhocine • Acta Informatica Pragensia
(2024)
Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system fo...
DOAJ
Open Access (DOAJ)
DOI: 10.18267/j.aip.246
Lisi Zhang, Qiancheng Yu, Beijing Zhou, Yifan Zhang et al. • IEEE Access
(2023)
The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and w...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3322429
Konstantinos Keremis, Eleni Vrochidou, George A. Papakostas • Journal of Imaging
(2025)
The ability of deep learning models to maintain consistent performance under image transformations-termed invariances, is critical for reliable deployment across diverse computer vision applications. This study presents a comprehensive empi...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jimaging11090322
Guangyu Zhu, Xueqi Luo, Tingting Yang, Li Cai et al. • Frontiers in Physiology
(2022)
The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studi...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fphys.2022.1084202
Andre Esteva, Katherine Chou, Serena Y. Yeung, Nikhil Naik et al. • npj Digital Medicine
(2021)
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in th...
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DOI: https://doi.org/10.1038/s41746-020-00376-2
PMC: 7794558
Swarnendu Ghosh, Nibaran Das, Ishita Das, Ujjwal Maulik • ACM Computing Surveys
(2019)
The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained envi...
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Open Access
DOI: https://doi.org/10.1145/3329784