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Showing 20 of 4,934 academic articles
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
Yu Lyu, Xiaolin Tian • Bioengineering
(2023)
Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression abili...
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Open Access (DOAJ)
DOI: 10.3390/bioengineering10091091
Miguel Marquez, Yingming Lai, Miao Liu, Elahe Memari et al. • PhotoniX
(2026)
Abstract Real-time dynamic imaging of microbubbles is crucial for understanding their microscale biophysical interactions and advancing ultrasound therapy. Despite progress in time-resolved optical imaging, existing techniques still face tr...
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Open Access (DOAJ)
DOI: 10.1186/s43074-026-00232-8
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,...
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Open Access (DOAJ)
DOI: 10.1007/s44196-025-00986-2
Shuo Han, Bo Mo, Junwei Xu, Shizun Sun et al. • International Journal of Computational Intelligence Systems
(2024)
Abstract Infrared images of sensitive targets are difficult to obtain and cannot meet the design and training needs of target detection and tracking algorithms for mobile platforms such as aircraft. This paper proposes an image translation ...
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Open Access (DOAJ)
DOI: 10.1007/s44196-024-00674-7
Jiantao Qiu, Chao Yu, Weiling Liu, Tianxiang Yang et al. • IEEE Access
(2021)
In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fideli...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3085328
Haiquan Kang, Haiquan Kang, Ziling Wang, Jingfang Sun et al. • Frontiers in Microbiology
(2024)
Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can ...
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Open Access (DOAJ)
DOI: 10.3389/fmicb.2024.1428304
Hairu Luo, Peng Jia, Anmin Zhou, Yuying Liu et al. • Applied Sciences
(2022)
In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities ...
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Open Access (DOAJ)
DOI: 10.3390/app122010337
Jeffrey C. Kimmel, Andrew D. Mcdole, Mahmoud Abdelsalam, Maanak Gupta et al. • IEEE Access
(2021)
Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key clou...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3077498
Sergey Plis, Devon Hjelm, Ruslan Salakhutdinov, Elena A. Allen et al. • Frontiers in Neuroscience
(2014)
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neu...
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DOI: https://doi.org/10.3389/fnins.2014.00229
PMC: 4138493
Ruud J. G. van Sloun, Regev Cohen, Yonina C. Eldar • Proceedings of the IEEE
(2019)
In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...
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Open Access
DOI: https://doi.org/10.1109/jproc.2019.2932116
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...
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Open Access
DOI: https://doi.org/10.1109/access.2019.2912200
Yoshua Bengio, Yann LeCun, Geoffrey E. Hinton • Communications of the ACM
(2021)
How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?...
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DOI: https://doi.org/10.1145/3448250
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht • Journal Of Big Data
(2021)
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augme...
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DOI: https://doi.org/10.1186/s40537-021-00492-0
PMC: 8287113
Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup et al. • Neural Networks
(2022)
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and w...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.neunet.2022.03.037
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
Haibo MEI, Kun YANG, Xinyu FAN • 物联网学报
(2021)
Fog radio access network (F-RAN) is suitable for Internet of things applications of national important industries, such as pipeline network monitoring in wide area.However, the performance of the F-RAN based on the territorial fog access po...
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Open Access (DOAJ)
Yuchen Luan, Fukun Sun, Jiaen Zhou • Sensors
(2024)
Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that user...
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Open Access (DOAJ)
DOI: 10.3390/s24113370
Hideyuki Ogura, Masaaki Ikehara • IEEE Access
(2025)
Image colorization is a fundamental task in computer vision that aims to predict the missing color channels from grayscale images. In recent years, fully automatic approaches based on deep learning have become the dominant paradigm. However...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3646876