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Academic Articles & Research Papers
Showing 10 of 30 academic articles
Liyan Luo, Liujun Zhang, Mei Wang, Zhenghong Liu et al. • IEEE Access
(2021)
In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event fr...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3123970
Konstantinos Keremis, Eleni Vrochidou, George A. Papakostas • Journal of Imaging
(2025)
The ability of deep learning models to maintain consistent performance under image transformations-termed invariances, is critical for reliable deployment across diverse computer vision applications. This study presents a comprehensive empi...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jimaging11090322
Andrew J. Reader, Guillaume Corda-D’Incan, Abolfazl Mehranian, Casper O. da Costa-Luis et al. • IEEE Transactions on Radiation and Plasma Medical Sciences
(2020)
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventio...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/trpms.2020.3014786
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
Haiquan Kang, Haiquan Kang, Ziling Wang, Jingfang Sun et al. • Frontiers in Microbiology
(2024)
Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can ...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fmicb.2024.1428304
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
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
OPENALEX
DOI: https://doi.org/10.1148/rg.2017170077
Masoud Mahdianpari, Bahram Salehi, Mohammad Rezaee, Fariba Mohammadimanesh et al. • Remote Sensing
(2018)
Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applicatio...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs10071119
Ł. Wilk, Ł. Wilk, D. Mielczarek, W. Ostrowski et al. • The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
(2022)
The use of deep machine learning methods for semantic classification of city mesh models is one of the current trends in geoscience development. Thanks to the thriving development of Convolutional Neural Networks (CNNs) it is now achievable...
DOAJ
Open Access (DOAJ)
DOI: 10.5194/isprs-archives-XLIII-B2-2022-485-2022
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, Salim Nasar Faraj Mursal et al. • IEEE Access
(2023)
Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3332243