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Academic Articles & Research Papers
Showing 20 of 29 academic articles
Benchen YANG, Yuhang JIA, Haibo JIN • Jisuanji gongcheng
(2023)
The overcomplete convolutional structure for biological images and volume segmentation is an excellent solution to the problem in which traditional codec methods cannot accurately segment the boundary regions. Although such methods perform ...
DOAJ
Open Access (DOAJ)
DOI: 10.19678/j.issn.1000-3428.0066125
Yinlin Cheng, Mengnan Ma, Xingyu Li, Yi Zhou • BMC Medical Informatics and Decision Making
(2021)
Abstract Background Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12911-021-01424-x
Amna Ikram, Azhar Imran • Informatics in Medicine Unlocked
(2026)
Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accura...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.imu.2026.101744
Binh‐Son Hua, Minh-Khoi Tran, Sai-Kit Yeung • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2018)
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored....
OPENALEX
Open Access
DOI: https://doi.org/10.1109/cvpr.2018.00109
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji et al. • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for comp...
OPENALEX
Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33013558
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
Jinyi Wang, Congyuan Xu, Jun Yang • Entropy
(2026)
Low-rate Denial-of-Service (LDoS) attacks exploit periodic traffic pulses to trigger congestion while maintaining a low average rate, making them highly stealthy and difficult to distinguish from legitimate bursty traffic using threshold-ba...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e28030328
Yuxi Wang, Yue Wang, Sha Han • Discover Artificial Intelligence
(2026)
Abstract The secondary virtual circuits in smart substations enable digital and networked information exchange between devices. However, their extensive logical connections, complex structure, and implicit interdependencies pose significant...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-02248-2
Yoav Goldberg • Journal of Artificial Intelligence Research
(2016)
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be ap...
OPENALEX
cc-by
DOI: https://doi.org/10.1613/jair.4992
Huiyan Han, Hongwei Zeng, Liqun Kuang, Xie Han et al. • Scientific Reports
(2024)
Abstract Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human–computer intelligent interaction. It has emerged as a significant research area in computer vision...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-024-65850-3
Hairui Zhu, Shanhong Guo, Weixing Sheng, Lei Xiao • Remote Sensing
(2023)
Ship classification technology using synthetic aperture radar (SAR) has become a research hotspot. Many deep-learning-based methods have been proposed with handcrafted models or using transplanted computer vision networks. However, most of ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs15112904
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens et al. • arXiv (Cornell University)
(2015)
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1512.00567
Kota Suzuki, Abu Saleh Musa Miah, Jungpil Shin • Sensors
(2025)
Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/s25206325
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens et al. • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2016)
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...
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DOI: https://doi.org/10.1109/cvpr.2016.308
Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad • Journal of Engineering
(2020)
Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios re...
DOAJ
Open Access (DOAJ)
DOI: 10.1155/2020/7286187
Yong Xu, Chaoda Zheng, Ruotao Xu, Yuhui Quan • IEEE Access
(2019)
Recognition of 3D shapes is a fundamental task in computer vision. In recent years, view-based deep learning has emerged as an effective approach for 3D shape recognition. Most existing view-based methods treat the views of an object as an ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2019.2934650
Jialiang Wang, Jianxu Luo, Bin Liu, Rui Feng et al. • IET Computer Vision
(2020)
In this work, we develop a computer‐aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images. We propose a modified object‐detection method for this t...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/iet-cvi.2018.5508
Ze Si, Dongmei Fu, Yang Liu, Zhicheng Huang • IET Image Processing
(2021)
Abstract Diabetic retinopathy (DR) is the main reason that causes preventable blindness. Hard exudate is one of the earliest signs of diabetic retinopathy. Precise detection of hard exudate is helpful for the early diagnosis of diabetic ret...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/ipr2.12007
Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow et al. • International Conference on Machine Learning
(2011)
The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to dev...
OPENALEX
Umara Zafar, Mubeen Ghafoor, Tehseen Zia, Ghufran Ahmed et al. • EURASIP Journal on Image and Video Processing
(2019)
Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method st...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s13640-019-0406-y