Zobrazeno 1 - 10
of 1 642
pro vyhledávání: '"Data imbalance"'
Publikováno v:
Alexandria Engineering Journal, Vol 107, Iss , Pp 770-785 (2024)
This study addresses the challenges of data scarcity and class imbalance in structural health monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from non-destructive evaluation (NDE) are used in various composite struct
Externí odkaz:
https://doaj.org/article/7c8f7c22c3cb4017810764cb6a200d99
Publikováno v:
Alexandria Engineering Journal, Vol 106, Iss , Pp 25-41 (2024)
The scale of white foreign fibers in bobbin yarn is small, resulting in multiple types of data imbalance in the dataset. These imbalances include a severe imbalance of foreign fiber pixels compared to background pixels and an imbalance in the size ta
Externí odkaz:
https://doaj.org/article/8e70cdd17070416d9e58149a46057102
Publikováno v:
Blockchain: Research and Applications, Vol 5, Iss 3, Pp 100207- (2024)
As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the
Externí odkaz:
https://doaj.org/article/6ca7a0d1d2ec4497b682cddb81fe18af
Publikováno v:
Preventive Medicine Reports, Vol 45, Iss , Pp 102841- (2024)
Background: Early and accurate diagnoses of sepsis patients are essential to reduce the mortality. However, the sepsis is still diagnosed in a traditional way in China despite the increasing number of related studies, which may to some extent lead to
Externí odkaz:
https://doaj.org/article/cd2dae4bd47142949942d51827a06f69
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-21 (2024)
Abstract Background DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DN
Externí odkaz:
https://doaj.org/article/5eadbf22f21d45f9a3fac6411b90e950
Autor:
Ali Fazli, Javad Poshtan
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 3, Pp 1174-1186 (2024)
Abstract Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attent
Externí odkaz:
https://doaj.org/article/d105d5f23ee143ddb9e32f61173ea052
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-21 (2024)
Abstract In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing acc
Externí odkaz:
https://doaj.org/article/042d6eed51fe4837b3676ab9b06d0666
Publikováno v:
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 8, Iss 1, Pp 54-61 (2024)
Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that favor the majority class. This issue is especially pronounced in the classification of financial distress, where data imbalance is com
Externí odkaz:
https://doaj.org/article/f8a9bb9d8dab47359f8a68a6106bb195
Autor:
Y. Qiang Sun, Hamid A. Pahlavan, Ashesh Chattopadhyay, Pedram Hassanzadeh, Sandro W. Lubis, M. Joan Alexander, Edwin P. Gerber, Aditi Sheshadri, Yifei Guan
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 7, Pp n/a-n/a (2024)
Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challeng
Externí odkaz:
https://doaj.org/article/890df17483a04e0384f4632b2962bbe7
Autor:
Yadong Zhou, Wen Li, Xiaoyu Cao, Boayin He, Qi Feng, Fan Yang, Hui Liu, Tiit Kutser, Min Xu, Fei Xiao, Xueer Geng, kai Yu, Yun Du
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 131, Iss , Pp 103959- (2024)
Supervised machine learning (SML) has become a crucial tool for estimating water quality parameters (WQPs) from satellite images. Its effectiveness relies heavily on synchronised in-situ datasets covering diverse water bodies. However, collecting suc
Externí odkaz:
https://doaj.org/article/5ab38f4da93e46e5b2f5816edebacda7