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Academic Articles & Research Papers
Showing 20 of 404 academic articles
Jianjing Zhang, Robert X. Gao • Chinese Journal of Mechanical Engineering
(2021)
Abstract Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production b...
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Open Access (DOAJ)
DOI: 10.1186/s10033-021-00587-y
Petryshyna Olga, Boyko Mariya • SHS Web of Conferences
(2021)
In modern academic community not only communication management plays a great role. Monitoring of expectations, awareness of communicative needs, the course and results of communicative interaction, barriers hindering rational managerial dec...
DOAJ
Open Access (DOAJ)
DOI: 10.1051/shsconf/202110402012
Yair Rivenson, Yichen Wu, Aydogan Özcan • Light Science & Applications
(2019)
Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of...
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DOI: https://doi.org/10.1038/s41377-019-0196-0
PMC: 6804620
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
Wan Zhu, Longxiang Xie, Jianye Han, Xiangqian Guo • Cancers
(2020)
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis...
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DOI: https://doi.org/10.3390/cancers12030603
PMC: 7139576
Ammar Mohammed, Rania Kora • Journal of King Saud University - Computer and Information Sciences
(2023)
In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms t...
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DOI: https://doi.org/10.1016/j.jksuci.2023.01.014
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
Toke T. Høye, Johanna Ärje, Kim Bjerge, Oskar Liset Pryds Hansen et al. • Proceedings of the National Academy of Sciences
(2021)
ABSTRACT Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phe...
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DOI: https://doi.org/10.1073/pnas.2002545117
PMC: 7812775
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
Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli • IEEE Transactions on Neural Networks and Learning Systems
(2018)
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2018.2790388
Michele Avanzo, Lise Wei, Joseph Stancanello, Martin Vallières et al. • Medical Physics
(2020)
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...
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DOI: https://doi.org/10.1002/mp.13678
PMC: 8965689
Jason Riordon, Dušan Sovilj, Scott Sanner, David A. Sinton et al. • Trends in biotechnology
(2018)
Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural...
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DOI: https://doi.org/10.1016/j.tibtech.2018.08.005
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
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
Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun et al. • IET Image Processing
(2024)
Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory”...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/ipr2.13287
Jiasi Chen, Xukan Ran • Proceedings of the IEEE
(2019)
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in...
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DOI: https://doi.org/10.1109/jproc.2019.2921977
Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel • ACM Computing Surveys
(2021)
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require ...
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Open Access
DOI: https://doi.org/10.1145/3439950
S. Mostafa Mousavi, Gregory C. Beroza • Science
(2022)
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processin...
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DOI: https://doi.org/10.1126/science.abm4470
Artur M. Schweidtmann, Erik Esche, Asja Fischer, Marius Kloft et al. • Chemie Ingenieur Technik
(2021)
Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...
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DOI: https://doi.org/10.1002/cite.202100083