Zobrazeno 1 - 10
of 1 252
pro vyhledávání: '"long short-term memory networks"'
Autor:
Mohammad A. Al‑Zubi, Mahmood Ahmad, Shahriar Abdullah, Beenish Jehan Khan, Wajeeha Qamar, Gamil M. S. Abdullah, Roberto Alonso González-Lezcano, Sonjoy Paul, N. S. Abd EL-Gawaad, Tariq Ouahbi, Muhammad Kashif
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-28 (2024)
Abstract The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, a
Externí odkaz:
https://doaj.org/article/35ef40ca2d854158b060cc62ebaa8798
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs), aiming to enhance the accurate and automated identification of various act
Externí odkaz:
https://doaj.org/article/24f3b597878a4635a3512e146cff08da
Publikováno v:
Zhihui kongzhi yu fangzhen, Vol 46, Iss 3, Pp 123-129 (2024)
Addressing the issue of relatively low recognition rates for named entities in domain-specific short texts under resource-constrained computational environments, a novel hybrid model combining a Dual BiLSTM_CRF architecture with a fully connected net
Externí odkaz:
https://doaj.org/article/c390e3438340473daab8255dd38fe992
Publikováno v:
Electronic Research Archive, Vol 32, Iss 5, Pp 3145-3161 (2024)
In order to grasp the degradation of rolling bearings and prevent the failure of mechanical equipment, a remaining useful life (RUL) prediction method of rolling bearings based on degradation detection and deep bidirectional long short-term memory ne
Externí odkaz:
https://doaj.org/article/1f6caf50734e46e2adcbe891ef476069
Publikováno v:
Discover Water, Vol 4, Iss 1, Pp 1-20 (2024)
Abstract Developing precise groundwater level (GWL) forecast models is essential for the optimal usage of limited groundwater resources and sustainable planning and management of water resources. In this study, an improved forecasting accuracy for up
Externí odkaz:
https://doaj.org/article/2e6205c546494a7489a8964ec144d96d
Publikováno v:
Systems Science & Control Engineering, Vol 12, Iss 1 (2024)
Cardiovascular diseases (CVDs) account for about 32% of global deaths. While digital stethoscopes can record heart sounds, expert analysis is often lacking. To address this, we propose LightCardiacNet, an interpretable, lightweight ensemble neural ne
Externí odkaz:
https://doaj.org/article/841ffe9c6c5a4c54a29a1c4474912e4c
Publikováno v:
IEEE Access, Vol 12, Pp 140932-140947 (2024)
Detecting semantic similarity between textual requirements is a crucial task for various natural language processing (NLP)-based requirements engineering (RE) applications. It is also challenging due to the nature of these requirements, which are wri
Externí odkaz:
https://doaj.org/article/b9f5d0b7dfdb4ada9acecaef463388cc
Publikováno v:
IEEE Access, Vol 12, Pp 115219-115236 (2024)
Recurrent Neural Networks (RNNs), including the distinguished Long Short-Term Memory Networks (LSTMs), have been shown to be effective in a wide range of sequential data problems. However, their ability to model very long-term dependencies still pose
Externí odkaz:
https://doaj.org/article/6a26aaa3b4b64cac9182debd99915649
Publikováno v:
IEEE Access, Vol 12, Pp 115895-115904 (2024)
This paper introduces the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT), a state-of-the-art deep learning framework designed to enhance multi-variate time series forecasting. The MDSTFT diverges from traditional models by integrating
Externí odkaz:
https://doaj.org/article/ca154757873f4087a228ba02f0baaa16
Autor:
Xiaobin Hu
Publikováno v:
IEEE Access, Vol 12, Pp 108134-108144 (2024)
The high-dimensional data fitting modeling based on hybrid neural network models has been a hot topic in data mining research in recent years. However, due to the curse of dimensionality and limited number of trainable samples, the model’s training
Externí odkaz:
https://doaj.org/article/3d94ed7367294d3da0fcf1218c32a179