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Showing 20 of 1,679 academic articles
Priya Mathur, Dheeraj Kumar, Farhan Sheth, Hammad Shaikh et al. • Scientific Reports
(2025)
Abstract Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conv...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-025-28268-z
Yanhui Xi, Weijie Zhang, Feng Zhou, Xin Tang et al. • Energy Reports
(2023)
Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current si...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.egyr.2022.12.043
Sercan Yalçın • Firat University Journal of Experimental and Computational Engineering
(2025)
Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts of the tires. It was aim to dev...
DOAJ
Open Access (DOAJ)
DOI: 10.62520/fujece.1527246
Guoqiang Zhou, Ben Li, Weifeng Zhang, Shudi Bao et al. • BMC Bioinformatics
(2026)
Abstract Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D complexes have shown strong potential for drug-target affinity prediction. However, mos...
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Open Access (DOAJ)
DOI: 10.1186/s12859-026-06481-5
Nitin Kumar Chauhan, Krishna Pal Singh • International Conference on Computing, Power and Communication Technologies (GUCON)
(2018)
In now days, deep learning has become a prominent and emerging research area in computer vision applications. Deep learning permits the multiple layers models for computation to learn representations of data by processing in their original ...
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DOI: https://doi.org/10.1109/gucon.2018.8675097
Yair Rivenson, Yichen Wu, Aydogan Özcan • Light Science & Applications
(2019)
Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of...
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DOI: https://doi.org/10.1038/s41377-019-0196-0
PMC: 6804620
Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi et al. • International Conference on Machine Learning and Applications
(2017)
Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an importan...
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Open Access
DOI: https://doi.org/10.1109/icmla.2017.0-134
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
Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan K.E. Westberg et al. • Electronics
(2019)
In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...
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DOI: https://doi.org/10.3390/electronics8030292
Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru • IEEE Access
(2025)
The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have li...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3536553
Christopher Ardohain, Songlin Fei • Science of Remote Sensing
(2025)
Deep learning (DL) is ubiquitous in remote sensing analysis with continued evolution in model architectures and advancement of model types. However, DL is still constrained by the need for extensive training datasets, which are costly and t...
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Open Access (DOAJ)
DOI: 10.1016/j.srs.2024.100185
Hao Wang, Xuan Wang, Yusheng Du, You Wang et al. • European Journal of Radiology Open
(2025)
Objectives: This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features. Methods: This study included 271 thyroid lobes...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.ejro.2025.100639
He Li, Kaoru Ota, Mianxiong Dong • IEEE Network
(2018)
Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing ...
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Open Access
DOI: https://doi.org/10.1109/mnet.2018.1700202
Yanming Yang, Xin Xia, David Lo, John Grundy • ACM Computing Surveys
(2021)
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstr...
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Open Access
DOI: https://doi.org/10.1145/3505243
Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar, Yacine Belhocine • Acta Informatica Pragensia
(2024)
Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system fo...
DOAJ
Open Access (DOAJ)
DOI: 10.18267/j.aip.246
Liyue Wu, Qinchuan Zhan, Yanjun Li • Humanities & Social Sciences Communications
(2026)
Abstract This study applies a multimodal deep learning approach to examine tourist engagement with traditional architectural heritage. The Gao Family Compound in Xi’an serves as a representative case. Social-media texts and images from Weib...
DOAJ
Open Access (DOAJ)
DOI: 10.1057/s41599-026-07879-5
Yasha Ektefaie, William Yuan, Deborah A. Dillon, Nancy U. Lin et al. • npj Breast Cancer
(2021)
Abstract Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41523-021-00357-y
Lisi Zhang, Qiancheng Yu, Beijing Zhou, Yifan Zhang et al. • IEEE Access
(2023)
The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and w...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3322429
Zhonghua Feng, Minglun Ren • Applied Sciences
(2026)
Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, wh...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app16178379
Konstantinos Keremis, Eleni Vrochidou, George A. Papakostas • Journal of Imaging
(2025)
The ability of deep learning models to maintain consistent performance under image transformations-termed invariances, is critical for reliable deployment across diverse computer vision applications. This study presents a comprehensive empi...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jimaging11090322