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Academic Articles & Research Papers
Showing 20 of 26 academic articles
Ahmad Heidary-Sharifabad, Mohsen Sardari Zarchi, Sima Emadi, Gholamreza Zarei • Data in Brief
(2021)
This paper contains datasets related to the “Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild” (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often a...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.dib.2021.107478
Wei Chen, Yu Liu, Weiping Wang, Erwin M. Bakker et al. • IEEE Transactions on Pattern Analysis and Machine Intelligence
(2022)
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, par...
OPENALEX
Open Access
DOI: https://doi.org/10.1109/tpami.2022.3218591
Francesco Maria Cianciaruso • Discover Artificial Intelligence
(2026)
Abstract The increasingly rapid development of deep learning algorithms is radically transforming society. The potential of neural models is paving the way for the automation of areas previously unrelated to AI, from medical diagnostics to ...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-01111-8
Charlotte Caucheteux, Jean-Rémi King • Communications Biology
(2022)
Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, w...
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DOI: https://doi.org/10.1038/s42003-022-03036-1
PMC: 8850612
Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica • Discover Artificial Intelligence
(2024)
Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-024-00114-7
Dang Minh Tan, Le-Minh Kieu • IATSS Research
(2023)
This paper introduces a new visual dataset and framework to facilitate computer-vision-based traffic monitoring in high density, mixed and lane-free traffic (TRAMON). While there are advanced deep learning algorithms that can detect and tra...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.iatssr.2023.10.001
B. Valarmathi, N. Srinivasa Gupta, G. Prakash, R. Hemadri Reddy et al. • IEEE Access
(2023)
Deep learning and computer vision algorithms will be applied to find the breed of the dog from an image. The goal is to have the user submit an image of a dog, and the model will choose one of the 120 breeds stated in the dataset to determi...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3297440
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...
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Tianjiao Zeng, Yanmin Zhu, Edmund Y. M. Lam • Optics Express
(2021)
Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities ...
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Open Access
DOI: https://doi.org/10.1364/oe.443367
Sidrah Liaqat, Kia Dashtipour, Adnan Zahid, Khaled Assaleh et al. • Information
(2020)
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocar...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/info11120549
Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang et al. • Land
(2023)
Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/land12061125
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
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
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
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
Prot Piyakawanich, Pattarapong Phasukkit • Drones
(2024)
In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/drones8120718
Usman Manzo Gidado, Haruna Chiroma, Nahla Aljojo, Saidu Abubakar et al. • IEEE Access
(2020)
Steering angle prediction is critical in the control of Autonomous Vehicles (AVs) and has attracted the attention of researchers, manufacturers, and insurance companies in the automotive industry. Different Deep Learning (DL) architectures ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2020.3017883