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Academic Articles & Research Papers
Showing 20 of 1,954 academic articles
Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface
(2018)
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...
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DOI: https://doi.org/10.1098/rsif.2017.0387
PMC: 5938574
Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety
(2019)
Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...
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DOI: https://doi.org/10.1111/1541-4337.12492
Hideyuki Ogura, Masaaki Ikehara • IEEE Access
(2025)
Image colorization is a fundamental task in computer vision that aims to predict the missing color channels from grayscale images. In recent years, fully automatic approaches based on deep learning have become the dominant paradigm. However...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3646876
WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao
(2023)
[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...
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Open Access (DOAJ)
DOI: 10.13998/j.cnki.issn1002-1248.23-0251
Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin et al. • Light Science & Applications
(2022)
With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...
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DOI: https://doi.org/10.1038/s41377-022-00714-x
PMC: 8866517
Kevin Warburton • International Journal of Sustainability in Higher Education
(2003)
Deep learning is a key strategy by which students extract meaning and understanding from course materials and experiences. Because of the range and interconnectedness of environmental, social and economic issues, and the importance of inter...
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DOI: https://doi.org/10.1108/14676370310455332
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
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...
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Open Access (DOAJ)
DOI: 10.1016/j.pacs.2023.100452
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...
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Open Access (DOAJ)
DOI: 10.19229/2464-9309/12162022
Kalaivani Sundararajan, Damon L. Woodard • ACM Computing Surveys
(2018)
In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate ...
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DOI: https://doi.org/10.1145/3190618
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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DOI: https://doi.org/10.15252/msb.20156651
PMC: 4965871
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
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
Esraa Khatab, Ahmed Onsy, Ahmed Abouelfarag • Sensors
(2022)
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a g...
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Open Access (DOAJ)
DOI: 10.3390/s22041663
Aishat I. Lepshakova, Oleg V. Kurskikh • Актуальные проблемы филологии и педагогической лингвистики
(2025)
The article analyses different translation versions of science fiction films directed by Denis Villeneuve with the aim of determining their semantic similarity through NLP analysis. There are four groups of metrics used in text analysis: se...
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Open Access (DOAJ)
DOI: 10.29025/2079-6021-2025-1-132-150
Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher et al. • Supply Chain Analytics
(2024)
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering...
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Open Access (DOAJ)
DOI: 10.1016/j.sca.2024.100087
Younggill Son, Woongchul Choi • Batteries
(2024)
With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical...
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Open Access (DOAJ)
DOI: 10.3390/batteries10060191
Chirag Gupta, Venkategowda Ramegowda, Supratim Basu, Andy Pereira • Frontiers in Genetics
(2021)
Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRA...
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Open Access (DOAJ)
DOI: 10.3389/fgene.2021.652189
Mathieu Calvat, Chris Bean, Dhruv Anjaria, Hyoungryul Park et al. • npj Computational Materials
(2025)
Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-b...
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Open Access (DOAJ)
DOI: 10.1038/s41524-025-01770-8