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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
Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain • Cognitive Computation
(2021)
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data...
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DOI: https://doi.org/10.1007/s12559-020-09773-x
PMC: 7783296
Vishwa S. Parekh, Michael A. Jacobs • Expert Review of Precision Medicine and Drug Development
(2019)
INTRODUCTION: The radiological reading room is undergoing a paradigm shift to a symbiosis of computer science and radiology using artificial intelligence integrated with machine and deep learning with radiomics to better define tissue chara...
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DOI: https://doi.org/10.1080/23808993.2019.1585805
PMC: 6508888
Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics
(2021)
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...
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DOI: https://doi.org/10.1148/rg.2021200210
Khoa Anh Tran, Olga Kondrashova, Andrew Peter Bradley, Elizabeth D. Williams et al. • Genome Medicine
(2021)
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...
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DOI: https://doi.org/10.1186/s13073-021-00968-x
PMC: 8477474
Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin et al. • Neurospine
(2019)
The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learn...
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DOI: https://doi.org/10.14245/ns.1938396.198
PMC: 6945006
Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik
(2018)
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...
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DOI: https://doi.org/10.1016/j.zemedi.2018.11.002
Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Francois Fahrmann et al. • Computational and Structural Biotechnology Journal
(2020)
In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using con...
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DOI: https://doi.org/10.1016/j.csbj.2020.09.033
PMC: 7575644
Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang et al. • Information Fusion
(2023)
Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent...
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DOI: https://doi.org/10.1016/j.inffus.2023.101859
Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou • IEEE Journal of Biomedical and Health Informatics
(2020)
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of ...
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DOI: https://doi.org/10.1109/jbhi.2020.3040225
Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi • IEEE Transactions on Games
(2019)
In this paper, we review recent deep learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requ...
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Open Access
DOI: https://doi.org/10.1109/tg.2019.2896986
Luke de Oliveira • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
(2022)
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning arch...
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DOI: https://doi.org/10.1007/jhep07%282016%29069
Roger Alves de Oliveira, Math Bollen • Electric Power Systems Research
(2022)
This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...
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DOI: https://doi.org/10.1016/j.epsr.2022.108887
Luke de Oliveira, Michael Kagan, Lester W. Mackey, Benjamin Philip Nachman et al. • Journal of High Energy Physics
(2016)
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning arch...
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DOI: https://doi.org/10.1007/jhep07(2016)069
Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering
(2019)
OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...
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DOI: https://doi.org/10.1088/1741-2552/ab0ab5
Intisar Rizwan I Haque, Jeremiah J. Neubert • Informatics in Medicine Unlocked
(2020)
The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identificat...
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DOI: https://doi.org/10.1016/j.imu.2020.100297
Jaya Gupta, Sunil Kumar Pathak, Gireesh Kumar • Journal of Physics Conference Series
(2022)
Abstract Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate differ...
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DOI: https://doi.org/10.1088/1742-6596/2273/1/012029
Shahbaz Rezaei, Xin Liu • IEEE Communications Magazine
(2019)
Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data pa...
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Open Access
DOI: https://doi.org/10.1109/mcom.2019.1800819
Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution
(2022)
Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...
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DOI: https://doi.org/10.1111/2041-210x.13901
Yohei Nishizaki, Matias Valdivia, Ryoichi Horisaki, Katsuhisa Kitaguchi et al. • Optics Express
(2019)
We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of...
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Open Access
DOI: https://doi.org/10.1364/oe.27.000240