Zobrazeno 1 - 10
of 6 474
pro vyhledávání: '"time-series data"'
Publikováno v:
BioData Mining, Vol 17, Iss 1, Pp 1-30 (2024)
Abstract Background Timely identification of deteriorating patients is crucial to prevent the progression to cardiac arrest. However, current methods predicting emergency department cardiac arrest are primarily static, rule-based with limited precisi
Externí odkaz:
https://doaj.org/article/97640fd7f02c4404b84a6eac19422f24
Autor:
Subramanian Deepan, Murugan Saravanan
Publikováno v:
ETRI Journal, Vol 46, Iss 5, Pp 915-927 (2024)
We obtain the air quality index (AQI) for a descriptive system aimed to communicate pollution risks to the population. The AQI is calculated based on major air pollutants including O3, CO, SO2, NO, NO2, benzene, and particulate matter PM2.5 that shou
Externí odkaz:
https://doaj.org/article/fb880beab6d14836bf59bf2350dc736e
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract We proposed a deep learning method using a convolutional neural network on time-series (TS) images to detect and differentiate affected body parts in people with Parkinson’s disease (PD) and freezing of gait (FOG) during 360° turning task
Externí odkaz:
https://doaj.org/article/2e701bb2472d4ae4a7f25d557580e2bd
Autor:
Liu, Zengkun, Hui, Justine
Publikováno v:
Sensor Review, 2024, Vol. 44, Issue 5, pp. 563-574.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/SR-03-2024-0183
Publikováno v:
大数据, Vol 10, Pp 34-50 (2024)
The compression strategy plays an important role in the performance of IoT time series data storage system.However, the current compression strategies can not adapt to the characteristics of NVM and IoT time series data.This paper proposes a polymorp
Externí odkaz:
https://doaj.org/article/96c162386e4644f1969ba40b8d45a66e
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract In order to augment the efficacy of the intelligent evaluation model for assessing the suitability of ice and snow tourism, this study refines the model by incorporating the Long Short-Term Memory (LSTM) network within the framework of the I
Externí odkaz:
https://doaj.org/article/730446ce4872468db8c5647099a04374
Autor:
黄文琦(HUANG Wenqi), 梁凌宇(LIANG Lingyu), 王鑫(WANG Xin), 赵翔宇(ZHAO Xiangyu), 宗珂(ZONG Ke), 孙凌云(SUN Lingyun)
Publikováno v:
Zhejiang Daxue xuebao. Lixue ban, Vol 51, Iss 4, Pp 483-491 (2024)
Accurate and effective load forecasting is very important for real-time operation and dispatching of power systems. In this paper, a prediction model that incorporates variable selection and sparse Transformer is proposed. Static and temporal variabl
Externí odkaz:
https://doaj.org/article/a02dc40040054c6fb9569a2fbf8bc7cd
Autor:
Shixin GONG
Publikováno v:
Meitan kexue jishu, Vol 52, Iss S1, Pp 1-12 (2024)
Accurate prediction of hydraulic support load plays an important role in improving the adaptability of support and the stability of surrounding rock control, where high-quality and large-scale time series data and effective prediction methods are nee
Externí odkaz:
https://doaj.org/article/d90d8b575fa542ac8cad47755cec8eea
Publikováno v:
Natural Hazards Research, Vol 4, Iss 2, Pp 194-220 (2024)
The global community is continuously working to minimize the impact of disasters through various actions, including earth surveying. For example, flood-prone areas must be identified appropriately, predicted, understood, and socialized. In that case,
Externí odkaz:
https://doaj.org/article/a05e7e8ed1d141a29b2a49279c7978b3
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 4, Pp 4989-5006 (2024)
Due to irregular sampling or device failure, the data collected from sensor network has missing value, that is, missing time-series data occurs. To address this issue, many methods have been proposed to impute random or non-random missing data. Howev
Externí odkaz:
https://doaj.org/article/d4bd62fec1864f429ea1498affcca648