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Showing 20 of 1,215 academic articles
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
Jishi Zheng, Junhui Li, Zhigang Ding, Linghua Kong et al. • Connection Science
(2023)
To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method. One of the key problems of this recognition method is to recognise text under complex package pat...
DOAJ
Open Access (DOAJ)
DOI: 10.1080/09540091.2023.2202363
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
Mengnan Du, Fan Yang, Na Zou, Xia Hu • IEEE Intelligent Systems
(2020)
Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. We provide a review covering recent progresses to ta...
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Open Access
DOI: https://doi.org/10.1109/mis.2020.3000681
Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang et al. • IEEE Access
(2019)
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of ...
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DOI: https://doi.org/10.1109/access.2019.2939201
M. Hassaballah, Ali Ismail Awad • CRC Press eBooks
(2020)
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained u ......
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DOI: https://doi.org/10.1201/9781351003827
LIU Zhaoyang, PAN Bitao • Guangtongxin yanjiu
(2024)
【Objective】Recent research efforts on Routing and Wavelength Assignment (RWA) for all optical networks are focused on Deep Reinforcement Learning (DRL) based algorithms. The DRL based RWA algorithms are mostly rely on the K Shortest Paths (...
DOAJ
Open Access (DOAJ)
Chunlong Shao, Ning Zhang, Wei Du, Han Li et al. • Journal of Radiation Research and Applied Sciences
(2026)
Objective: This study was designed to examine the association between preoperative abdominal MRI features derived using a deep learning-based reconstruction (DL-Recon) algorithm and postoperative surgical incision-healing outcomes, while ev...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.jrras.2026.102395
Basudev Saha, Bidyut Das, Mukta Majumder • Nanotechnology and Precision Engineering
(2023)
Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. How...
DOAJ
Open Access (DOAJ)
DOI: 10.1063/10.0017350
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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cc-by-nc
DOI: https://doi.org/10.14245/ns.1938396.198
PMC: 6945006
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
Adam Coates, Brody Huval, Tao Wang, David J. Wu et al. (2013)
Scaling up deep learning algorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent efforts to train extremely large networks (with over 1 billion parameters)...
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Maryam Mousaarab Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya et al. • Journal Of Big Data
(2015)
Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can con...
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DOI: https://doi.org/10.1186/s40537-014-0007-7
Yundie Guan, Xiangyu Zhang, Zheming Liang, Tao Chen • IET Renewable Power Generation
(2025)
Abstract The intermittency and volatility of renewable energy have been major challenges in modern power systems. This paper proposes a self‐adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewab...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/rpg2.13192
S. Chinnadurai, S. Selvakumar • Scientific Reports
(2025)
Abstract Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts produc...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-025-28549-7
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
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
Shervin Minaee, Yuri Y. Boykov, Fatih Porikli, Antonio Plaza et al. • IEEE Transactions on Pattern Analysis and Machine Intelligence
(2021)
Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, amon...
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Open Access
DOI: https://doi.org/10.1109/tpami.2021.3059968
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
Lijian Zhang, Guangfu Liu • Advances in Mathematical Physics
(2021)
Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and sha...
DOAJ
Open Access (DOAJ)
DOI: 10.1155/2021/4343255