Zobrazeno 1 - 10
of 41 679
pro vyhledávání: '"failure prediction"'
Many existing models struggle to predict nonlinear behavior during extreme weather conditions. This study proposes a multi-scale temporal analysis for failure prediction in energy systems using PMU data. The model integrates multi-scale analysis with
Externí odkaz:
http://arxiv.org/abs/2411.02857
Prediction of failures in real-world robotic systems either requires accurate model information or extensive testing. Partial knowledge of the system model makes simulation-based failure prediction unreliable. Moreover, obtaining such demonstrations
Externí odkaz:
http://arxiv.org/abs/2410.09249
Autor:
Boll, Heloisa Oss, Amirahmadi, Ali, Soliman, Amira, Byttner, Stefan, Recamonde-Mendoza, Mariana
Objective: In modern healthcare, accurately predicting diseases is a crucial matter. This study introduces a novel approach using graph neural networks (GNNs) and a Graph Transformer (GT) to predict the incidence of heart failure (HF) on a patient si
Externí odkaz:
http://arxiv.org/abs/2411.19742
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This
Externí odkaz:
http://arxiv.org/abs/2410.19179
The accuracy and understandability of bank failure prediction models are crucial. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learni
Externí odkaz:
http://arxiv.org/abs/2407.11089
Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is critical for ensuring seamless communication and meeting the stringent requirements of high data rates, low latency, and improved reliability in 5G networks. However, weath
Externí odkaz:
http://arxiv.org/abs/2407.05197
Autor:
Zhong, Xian, Salahuddin, Zohaib, Chen, Yi, Woodruff, Henry C, Long, Haiyi, Peng, Jianyun, Udawatte, Nuwan, Casale, Roberto, Mokhtari, Ayoub, Zhang, Xiaoer, Huang, Jiayao, Wu, Qingyu, Tan, Li, Chen, Lili, Li, Dongming, Xie, Xiaoyan, Lin, Manxia, Lambin, Philippe
Artificial intelligence (AI)-based decision support systems have demonstrated value in predicting post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC). However, they often lack transparency, and the impact of model explanations on
Externí odkaz:
http://arxiv.org/abs/2408.03771
Autor:
Yu, Qiao, Zhang, Wengui, Zhou, Min, Yu, Jialiang, Sheng, Zhenli, Bogatinovski, Jasmin, Cardoso, Jorge, Kao, Odej
Large-scale datacenters often experience memory failures, where Uncorrectable Errors (UEs) highlight critical malfunction in Dual Inline Memory Modules (DIMMs). Existing approaches primarily utilize Correctable Errors (CEs) to predict UEs, yet they t
Externí odkaz:
http://arxiv.org/abs/2406.05354
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and clas
Externí odkaz:
https://doaj.org/article/3a7a64bb976d4347ac86ef53bc2b0fec
