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Academic Articles & Research Papers
Showing 20 of 190 academic articles
Sheng Zhang, Zhenghao Bei, Tonghua Ling, Qianqian Chen et al. • Scientific Reports
(2024)
Abstract Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex ...
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Open Access (DOAJ)
DOI: 10.1038/s41598-024-77173-4
A. Geiss, S. J. Silva, S. J. Silva, J. C. Hardin et al. • Geoscientific Model Development
(2022)
Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrat...
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Open Access (DOAJ)
DOI: 10.5194/gmd-15-6677-2022
saeedeh zebhi, Seyed Mohammad Taghi AlModaressi, Vahid Abootalebi • AUT Journal of Electrical Engineering
(2021)
In this paper, two different methods of human activity recognition based on video signals are introduced. The first method explores the effectiveness of combining feature descriptors obtained by local descriptors and artificial neural network ...
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Open Access (DOAJ)
DOI: 10.22060/eej.2020.18517.5357
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...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/OJEMB.2023.3266135
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
Athanasios S. Voulodimos, Nikolaos D. Doulamis, Anastasios D. Doulamis, Eftychios E. Protopapadakis • Computational Intelligence and Neuroscience
(2018)
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...
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DOI: https://doi.org/10.1155/2018/7068349
PMC: 5816885
Miguel Marquez, Yingming Lai, Miao Liu, Elahe Memari et al. • PhotoniX
(2026)
Abstract Real-time dynamic imaging of microbubbles is crucial for understanding their microscale biophysical interactions and advancing ultrasound therapy. Despite progress in time-resolved optical imaging, existing techniques still face tr...
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Open Access (DOAJ)
DOI: 10.1186/s43074-026-00232-8
Ajantha Vijayakumar, Joseph Abraham Sundar Koilraj, Muthaiah Rajappa • International Journal of Computational Intelligence Systems
(2025)
Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy,...
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Open Access (DOAJ)
DOI: 10.1007/s44196-025-00986-2
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
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”...
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Open Access (DOAJ)
DOI: 10.1049/ipr2.13287
Zhong‐Qiu Zhao, Peng Zheng, Shou-Tao Xu, Xindong Wu • IEEE Transactions on Neural Networks and Learning Systems
(2019)
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2018.2876865
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
Siwei Yu, Jianwei Ma, Wenlong Wang • Geophysics
(2019)
ABSTRACT Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the ...
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Open Access
DOI: https://doi.org/10.1190/geo2018-0668.1
Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data
(2019)
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...
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DOI: https://doi.org/10.1186/s40537-019-0192-5
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...
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DOI: https://doi.org/10.1148/rg.2017170077
Ko-Tsung Hsu, Steven Guan, Parag V. Chitnis • Photoacoustics
(2023)
Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-craft...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.pacs.2023.100452
Yuwei Yin, Zhixian Tang, Huachun Weng • BioMedical Engineering OnLine
(2024)
Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with th...
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Open Access (DOAJ)
DOI: 10.1186/s12938-024-01209-z
Greg Zaharchuk, Enhao Gong, Max Wintermark, Daniel L. Rubin et al. • American Journal of Neuroradiology
(2018)
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applic...
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DOI: https://doi.org/10.3174/ajnr.a5543
PMC: 7410723
Samira Pouyanfar, Saad B Sadiq, Yilin Yan, Haiman Tian et al. • ACM Computing Surveys
(2018)
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler d...
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DOI: https://doi.org/10.1145/3234150
Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics
(2016)
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...
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Open Access
DOI: https://doi.org/10.1093/bib/bbw068