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Academic Articles & Research Papers
Showing 20 of 423 academic articles
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
Khawja Imran Masud, Md Yasin Zihad, Mehedi Hasan Shuvo, Mst Raonik Jannat et al. • PLoS ONE
(2026)
Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computa...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0356383
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
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Open Access (DOAJ)
DOI: 10.3390/rs14081877
Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC)
(2016)
In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...
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DOI: https://doi.org/10.1109/yac.2016.7804882
Sylvain Christin, Éric Hervet, Nicolas Lecomte • Methods in Ecology and Evolution
(2019)
Abstract A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized ...
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Open Access
DOI: https://doi.org/10.1111/2041-210x.13256
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
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
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
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)...
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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 ...
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Open Access (DOAJ)
DOI: 10.1186/s12880-025-01643-y
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
John D. Kelleher • The MIT Press eBooks
(2019)
An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vi...
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DOI: https://doi.org/10.7551/mitpress/11171.001.0001
William W. Hsieh • Cambridge University Press eBooks
(2009)
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and th...
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DOI: https://doi.org/10.1017/cbo9780511627217
Ioannis Kavakiotis, O. Tsave, Athanasios Salifoglou, Nicos Maglaveras et al. • Computational and Structural Biotechnology Journal
(2017)
The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, ap...
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DOI: https://doi.org/10.1016/j.csbj.2016.12.005
PMC: 5257026
Sergio Muñoz, Carlos Á. Iglesias • Big Data and Cognitive Computing
(2024)
The proliferation of fake news threatens the integrity of information ecosystems, creating a pressing need for effective and interpretable detection mechanisms. Recent advances in machine learning, particularly with transformer-based models...
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Open Access (DOAJ)
DOI: 10.3390/bdcc8100129
Orges Koci, Richard K. Russell, M. Guftar Shaikh, Christine Edwards et al. • Microbiome
(2024)
Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...
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Open Access (DOAJ)
DOI: 10.1186/s40168-024-01834-9
S. Vijayan, Chiranji Lal Chowdhary • Scientific Reports
(2025)
Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy cr...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-92646-w