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Showing 20 of 608 academic articles
Judith E. Dayhoff, James M. DeLeo • Cancer
(2001)
Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...
OPENALEX
Open Access
DOI: https://doi.org/10.1002/1097-0142(20010415)91:8+<1615::aid-cncr1175>3.0.co;2-l
Martin Anthony, Peter L. Bartlett • Cambridge University Press eBooks
(1999)
First published in 1999, this book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. R...
OPENALEX
DOI: https://doi.org/10.1017/cbo9780511624216
Jiří Beneš • Czech digital mathematics library
(1990)
JIŘÍ BENEŠTypical neural network models representing a broad survey of actual trends are considered in an unifying way as Complexes subject to specific control systems, called Formators which may have a two-level arrangement and incorporate...
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Open Access
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...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs13214407
Yao He, Jing Yang, Shaobo Li, Jianjun Hu et al. • Complex & Intelligent Systems
(2024)
Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s40747-024-01350-1
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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cc-by
DOI: https://doi.org/10.26599/bdma.2018.9020001
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
Ning Zhang, Enxu Zhang, Fei Li • Applied Sciences
(2023)
Soybean is a type of food crop with economic benefits. Whether they are damaged or not directly affects the survival and nutritional value of soybean plants. In machine learning, unbalanced data represent a major factor affecting machine le...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app13116425
Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar et al. • Technologies
(2023)
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily li...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/technologies11020055
P. V. Chandrika, Sandeep Kelkar • Advances in medical diagnosis, treatment, and care (AMDTC) book series
(2024)
This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...
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DOI: https://doi.org/10.4018/979-8-3693-1281-0.ch001
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad-Khasmakhi et al. • ACM Computing Surveys
(2021)
Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this art...
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Open Access
DOI: https://doi.org/10.1145/3439726
Lucas Prado Osco, José Marcato, Ana Paula Marques Ramos, Lúcio André de Castro Jorge et al. • International Journal of Applied Earth Observation and Geoinformation
(2021)
Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, ...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.jag.2021.102456
Roberto De Fazio, Matteo Paiano, Ramiro Velazquez, Carolina Del-Valle-Soto et al. • Applied Sciences
(2026)
Automatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with compu...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app16168164
Bang Zhu, Wenkai Cheng, Shiting Wen, Yuran Ma et al. • Scientific Reports
(2025)
Abstract The application of the $$\Phi$$ -OTDR (Phase-Optical Time Domain Reflectometry) system in real-time monitoring of power grid infrastructure has been proven effective in identifying and classifying various anomalies, such as digging...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-025-29691-y
Xuening Lyu, Rimsa Goperma, Dandan Wang, Chunling Wan et al. • BMC Psychiatry
(2025)
Abstract Background Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional s...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12888-025-07196-2
Daniel Veltri, Uday Kamath, Amarda Shehu • Bioinformatics
(2018)
Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...
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cc-by-nc
DOI: https://doi.org/10.1093/bioinformatics/bty179
PMC: 6084614
YANG Qi-fan, SUN Xue-yang, WANG Yan-bin, TIAN Zhi-ling et al. • Fayixue Zazhi
(2022)
ObjectiveTo apply the convolutional neural network （CNN） Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology...
DOAJ
Open Access (DOAJ)
DOI: 10.12116/j.issn.1004-5619.2021.410923
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...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-024-64826-7
Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu et al. • IEEE Transactions on Neural Networks and Learning Systems
(2017)
Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...
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DOI: https://doi.org/10.1109/tnnls.2017.2766168
• De Gruyter eBooks
(2020)
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...
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DOI: https://doi.org/10.1515/9783110670905