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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
Haotian Wu, Yan Guo, Juliang Cao, Zhiming Xiong et al. • Aerospace
(2025)
In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for air...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/aerospace12080670
Emanuele Aiello, Mirko Agarla, Diego Valsesia, Paolo Napoletano et al. • IEEE Access
(2024)
Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2024.3396990
Shiyao Duan, Jiaojiao Li, Rui Song, Yunsong Li et al. • Remote Sensing
(2023)
Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models excl...
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Open Access (DOAJ)
DOI: 10.3390/rs15102619
Elisabetta Doria • Agathón
(2022)
The research proposal reports the outcomes of a research track concerning the automation of the architectural census of technological elements in urban environments, aiming at the development of a monitoring and management system for the bu...
DOAJ
Open Access (DOAJ)
DOI: 10.19229/2464-9309/12162022
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu • Journal of Computational Chemistry
(2017)
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...
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Open Access
DOI: https://doi.org/10.1002/jcc.24764
Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics
(2016)
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...
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DOI: https://doi.org/10.1109/jbhi.2016.2636665
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, Oliver Stegle • Molecular Systems Biology
(2016)
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventiona...
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other-oa
DOI: https://doi.org/10.15252/msb.20156651
PMC: 4965871
Terrence J. Sejnowski A B • The MIT Press eBooks
(2018)
How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.The deep learning revolution has brought us driverless cars, the greatly improved...
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DOI: https://doi.org/10.7551/mitpress/11474.001.0001
Anna Teresa Seiche, Lucas Wittstruck, Thomas Jarmer • Sensors
(2024)
In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effect...
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Open Access (DOAJ)
DOI: 10.3390/s24051544
Shiv Singh, Neelu Jyothi Ahuja • Discover Computing
(2026)
Abstract The application of deep learning models in resource-constrained environments requires a trade-off among accuracy, efficiency, and interpretability a trilemma frequently neglected in conventional knowledge distillation (KD) approach...
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Open Access (DOAJ)
DOI: 10.1007/s10791-026-10123-y
Sandipan Sikdar, Rachneet Sachdeva, Johannes Wachs, Johannes Wachs et al. • Frontiers in Big Data
(2022)
This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are...
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Open Access (DOAJ)
DOI: 10.3389/fdata.2021.771404
Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang et al. • Applied Sciences
(2024)
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...
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Open Access (DOAJ)
DOI: 10.3390/app14020774
Shaorong Zhang, Shaorong Zhang, Qihui Wang, Benxin Zhang et al. • Frontiers in Neuroscience
(2023)
IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...
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Open Access (DOAJ)
DOI: 10.3389/fnins.2023.1292724
Natasha Dropka, Stefan Ecklebe, Martin Holena • Crystals
(2021)
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs)...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/cryst11020138
Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn • BMC Medical Imaging
(2025)
Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several ...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12880-025-01643-y
Hu Dong, Xin Tong, Gang Liu • Fractal and Fractional
(2026)
For high-intensity focused ultrasound (HIFU) thermal ablation to be safe and effective, real-time, high-precision monitoring of tissue coagulative necrosis is essential. However, decoding the ultra-long, non-stationary radio frequency (RF) ...
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Open Access (DOAJ)
DOI: 10.3390/fractalfract10090609
Adam C. Mater, Michelle L. Coote • Journal of Chemical Information and Modeling
(2019)
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...
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DOI: https://doi.org/10.1021/acs.jcim.9b00266
S. Mostafa Mousavi, Gregory C. Beroza • Science
(2022)
Seismic waves from earthquakes and other sources are used to infer the structure and properties of Earth's interior. The availability of large-scale seismic datasets and the suitability of deep-learning techniques for seismic data processin...
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DOI: https://doi.org/10.1126/science.abm4470
Hao Xing, Guigang Zhang, Shang Ma • International Journal of Semantic Computing
(2016)
Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and th...
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DOI: https://doi.org/10.1142/s1793351x16500045