Zobrazeno 1 - 10
of 461
pro vyhledávání: '"Small dataset"'
Publikováno v:
Royal Society Open Science, Vol 11, Iss 5 (2024)
The perovskite crystal structure represents a semiconductor material poised for widespread application, underpinned by attributes encompassing heightened efficiency, cost-effectiveness and remarkable flexibility. Notably, strontium titanate (SrTiO3)-
Externí odkaz:
https://doaj.org/article/2a77515c35a5448a9e5e3287e2556062
Autor:
Wenxing Chen, Chuxiang Zhou, Hao Zhang, Liwei Yan, Shengtai Zhou, Yang Chen, Zhengguang Heng, Huawei Zou, Mei Liang
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 8007 (2024)
The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning,
Externí odkaz:
https://doaj.org/article/3f8784a42c384bc6be7635bc5d9fd3e3
Publikováno v:
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Vol 11, Iss 2 (2024)
This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from
Externí odkaz:
https://doaj.org/article/e8862aac72774d9eac77981f2b798313
Publikováno v:
Electronic Research Archive, Vol 31, Iss 8, Pp 4753-4772 (2023)
Machine learning (ML) techniques are extensively applied to practical maritime transportation issues. Due to the difficulty and high cost of collecting large volumes of data in the maritime industry, in many maritime studies, ML models are trained wi
Externí odkaz:
https://doaj.org/article/06a6b208726a43e5845426c1e51150fb
Publikováno v:
Journal of Open Innovation: Technology, Market and Complexity, Vol 9, Iss 4, Pp 100156- (2023)
This empirical work aims to determine whether the government expenditure shocks on consumption have impacts on macroeconomic factors. This study prepares four quarterly period datasets associated with macroeconomic activities in Indonesia with 24 dat
Externí odkaz:
https://doaj.org/article/74efbce24f16441da05eb14691af0cd4
Publikováno v:
Scientific Journal of Silesian University of Technology. Series Transport, Vol 118, Pp 207-217 (2023)
Data-driven predictive aircraft maintenance approach typically results in lower maintenance costs, avoiding unnecessary preventive maintenance actions and reducing unexpected failures. Information provided by a reliability analysis of aircraft compon
Externí odkaz:
https://doaj.org/article/74be2e43b2cb44a6b15d503b6a112d2c
Autor:
Mohd Shariff, Khairul Khaizi, Abdullah, Noor Ezan, Al-Misreb, Ali Abd, Jahidin, Aisyah Hartini, Megat Ali, Megat Syahirul Amin, Mohd Yassin, Ahmad Ihsan
Publikováno v:
TEM Journal. 12(2):883-889
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1123073
Publikováno v:
Information, Vol 15, Iss 4, p 198 (2024)
Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the
Externí odkaz:
https://doaj.org/article/e4cf5b2541b24504b745d28bc05c83bf
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 11 (2023)
The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlati
Externí odkaz:
https://doaj.org/article/e653d3a3df99477a87ff342aa9c45a34
Publikováno v:
IEEE Access, Vol 11, Pp 136166-136178 (2023)
Does formula-driven supervised learning (FDSL) work effectively with fine-tuning on small datasets? Additionally, how many natural images do a network pre-trained with FDSL require to acquire sufficient image features? FDSL is a pre-training method t
Externí odkaz:
https://doaj.org/article/fd5b25a4ba32476391ddd53beaf9caa6