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Academic Articles & Research Papers
Showing 18 of 58 academic articles
A.K. Jain, Jianchang Mao, K.M. Mohiuddin • Computer
(1996)
Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the arti...
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DOI: https://doi.org/10.1109/2.485891
Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, Haixu Tang • ACS in focus
(2024)
In recent years, breakthroughs in artificial intelligence (AI) have dramatically transformed many fields. Developments in neural networks primarily drive the advances. These powerful models have demonstrated unprecedented capabilities in le...
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DOI: https://doi.org/10.1021/acsinfocus.7e8012
Yaoda Xu, Maryam Vaziri-Pashkam • NeuroImage
(2022)
Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural ne...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.neuroimage.2022.119635
Ajith Abraham • Wiley eBooks
(2005)
Abstract Artificial neural networks are useful tools that have been applied successfully in a broad range of signal‐processing problems. From a signal‐processing perspective, their ability to adapt continuously to new data allows them to tr...
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DOI: https://doi.org/10.1002/0471497398.mm421
Mohamad H. Hassoun • Proceedings of the IEEE
(1996)
From the Publisher:
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Funda...
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DOI: https://doi.org/10.1109/jproc.1996.503146
Sarnovský M., Nina A., Popović L.Č., Srećković V. et al. • Serbian Astronomical Journal
(2026)
Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric
particles and thus affect electrical conductivity. Consequently,
electromagnetic waves ...
DOAJ
Open Access (DOAJ)
DOI: 10.2298/SAJ2612107S
Terrence J. Sejnowski A B • Proceedings of the National Academy of Sciences
(2020)
Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiqui...
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Open Access
DOI: https://doi.org/10.1073/pnas.1907373117
PMC: 7720171
Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Francois Fahrmann et al. • Computational and Structural Biotechnology Journal
(2020)
In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using con...
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cc-by
DOI: https://doi.org/10.1016/j.csbj.2020.09.033
PMC: 7575644
Jae Joon Hwang, Yun‐Hoa Jung, Bong‐Hae Cho, Min-Suk Heo • Imaging Science in Dentistry
(2019)
PURPOSE: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about d...
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cc-by-nc
DOI: https://doi.org/10.5624/isd.2019.49.1.1
PMC: 6444007
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 ...
DOAJ
Open Access (DOAJ)
DOI: 10.22060/eej.2020.18517.5357
Jan Kukačka, Vladimir Golkov, Daniel Cremers • arXiv (Cornell University)
(2017)
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying t...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1710.10686
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”...
DOAJ
Open Access (DOAJ)
DOI: 10.1049/ipr2.13287
Josh Patterson, Gibson Adam (2017)
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This han...
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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
Erik Gawehn, Jan A. Hiss, Gisbert Schneider • Molecular Informatics
(2015)
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...
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DOI: https://doi.org/10.1002/minf.201501008
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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cc-by
DOI: https://doi.org/10.3174/ajnr.a5543
PMC: 7410723
Laura ONAC • Studia Universitatis Babes-Bolyai: Series Informatica
(2019)
Plant recognition represents a challenging computer vision problem due to the great variations of shape and texture among plant organs, within the same species. This paper proposes a light-weight, but reasonably deep Convolutional Neural Ne...
DOAJ
Open Access (DOAJ)
DOI: 10.24193/subbi.2019.1.01
Adi Laurentiu Tarca, Vincent J. Carey, Xuewen Chen, Roberto J. Romero et al. • PLoS Computational Biology
(2007)
The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechan...
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public-domain
DOI: https://doi.org/10.1371/journal.pcbi.0030116
PMC: 1904382