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Showing 20 of 45 academic articles
Faisal Mehmood, Shabir Ahmad, Taeg Keun Whangbo • Mathematics
(2023)
Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has pl...
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Open Access (DOAJ)
DOI: 10.3390/math11061360
Vladyslav Yavtukhovskyi, Violeta Tretynyk • Кібернетика та комп'ютерні технології
(2025)
Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, on...
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Open Access (DOAJ)
DOI: 10.34229/2707-451X.25.2.6
Xia Zhao, Limin Wang, Yufei Zhang, Xuming Han et al. • Artificial Intelligence Review
(2024)
Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of dee...
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DOI: https://doi.org/10.1007/s10462-024-10721-6
Yuchao Feng, Jianwei Zheng, Mengjie Qin, Cong Bai et al. • Remote Sensing
(2021)
Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...
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Open Access (DOAJ)
DOI: 10.3390/rs13214407
Jin Liu, Yi Feng Pan, Min Li, Ziyue Chen et al. • Big Data Mining and Analytics
(2018)
Deep learning provides exciting solutions in many fields, such as image analysis, natural language processing, and expert system, and is seen as a key method for various future applications. On account of its non-invasive and good soft tiss...
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DOI: https://doi.org/10.26599/bdma.2018.9020001
Zetian Zhou, Heqing Zhang, Mehdi Effatparvar • Scientific Reports
(2024)
Abstract Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the de...
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Open Access (DOAJ)
DOI: 10.1038/s41598-024-64826-7
Myeongsuk Pak, Sang-Hoon Kim • International Conference on Computer Applications and Information Processing Technology (CAIPT)
(2017)
Deep learning has been the core topic in machine learning and convolutional neural network is one of the most prominent approaches. Convolutional neural network has won numerous competitions in recent years. It has outstanding results in im...
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DOI: https://doi.org/10.1109/caipt.2017.8320684
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
Xiaofei Yang, Yunming Ye, Xutao Li, Raymond Y.K. Lau et al. • IEEE Transactions on Geoscience and Remote Sensing
(2018)
Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision task...
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DOI: https://doi.org/10.1109/tgrs.2018.2815613
Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish et al. • ACM SIGMOD Record
(2016)
Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven application...
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Open Access
DOI: https://doi.org/10.1145/3003665.3003669
N. Kannaiya Raja, E. Laxmi Lydia, Thumpala Archana Acharya, K. Radhika et al. • IEEE Access
(2023)
In recent years, drones or Unmanned Aerial Vehicles (UAVs) got significant attention among researchers because of their extensive application in commercial applications, border surveillance, etc. As the conventional terrestrial communicatio...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3324068
Wang, Dayong, Aditya Khosla, Rishab Gargeya, Humayun Irshad et al. • arXiv (Cornell University)
(2016)
The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won bo...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1606.05718
Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis • IEEE Open Journal of Engineering in Medicine and Biology
(2023)
Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the n...
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Open Access (DOAJ)
DOI: 10.1109/OJEMB.2023.3266135
Sai Sri Hemantha Konala, Srinivas Koppu • Frontiers in Medicine
(2026)
IntroductionKidney-related disorders are one of the global health concerns that require timely detection to prevent severe health complications. The use of computed tomography (CT) images for accurate classification of kidney diseases is im...
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Open Access (DOAJ)
DOI: 10.3389/fmed.2026.1893419
Hao Shi, Guo Cao, Youqiang Zhang, Zixian Ge et al. • Remote Sensing
(2022)
Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods ...
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Open Access (DOAJ)
DOI: 10.3390/rs14174235
Shutao Li A, Weiwei Song, Leyuan Fang, Yushi Chen et al. • IEEE Transactions on Geoscience and Remote Sensing
(2019)
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine ...
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Open Access
DOI: https://doi.org/10.1109/tgrs.2019.2907932
Ajeet Ram Pathak, Manjusha Pandey, Siddharth Swarup Rautaray • Procedia Computer Science
(2018)
The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applicatio...
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DOI: https://doi.org/10.1016/j.procs.2018.05.144
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
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs14081877
Neil Thompson, Kristjan H. Greenewald, Keeheon Lee, Gabriel F. Manso (2023)
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks.But this progress has come with ...
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DOI: https://doi.org/10.21428/bf6fb269.1f033948