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long short term bidirectional gated recurrent convolutional neural network deep neural network Explore More

Unit Network sentence examples within long short term

long short term 10.3390/math9212640

Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results.

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long short term 10.1145/3475957.3484455

To this end, we utilize bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit networks (GRU) to explore high-level and low-level features from different modalities.

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Unit Network sentence examples within bidirectional gated recurrent

bidirectional gated recurrent 10.1016/j.neucom.2021.07.031

Specifically, we firstly extract features through Bidirectional Gated Recurrent Unit Network(Bi-GRU), multi-layer attention and linear layers.

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bidirectional gated recurrent 10.1186/s40249-021-00912-6

We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution.

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Unit Network sentence examples within convolutional neural network

convolutional neural network 10.1007/s41050-021-00028-8

In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images, and two recurrent neural networks, a long short time memory network and a gated recurrent unit network.

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convolutional neural network 10.1093/bib/bbaa254

First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers.

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Recurrent Unit Network Care Unit Network Linear Unit Network Explore More

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deep neural network 10.21203/RS.3.RS-554205/V1

To overcome this problem, we propose a novel speech enhancement method from fused features based on deep neural network and gated recurrent unit network.

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deep neural network 10.1109/TII.2020.3022019

A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST.

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Unit Network sentence examples within Recurrent Unit Network

Recurrent Unit Network 10.1016/J.MEASUREMENT.2021.109347

In step III, the simple recurrent unit network establishes forecasting models for each sub-series after feature selection and obtains the final forecasting results.

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Recurrent Unit Network 10.5220/0010324703510359

In this paper, we propose a psychology-inspired twin stream Gated Recurrent Unit network for action recognition based on the dynamic of 2D human body joints in RGB videos.

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Unit Network sentence examples within Care Unit Network

Care Unit Network 10.1016/j.resuscitation.2021.07.019

Among 80,716 patients who delivered to the emergency room, 935 patients treated with VA-ECMO after ROSC were included using the data from the Tokyo Cardiovascular Care Unit Network Registry between 2010 and 2017.

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Care Unit Network 10.1177/2048872618762638

Methods: Using the Tokyo Cardiovascular Care Unit network registry, we identified patients hospitalised with apical Takotsubo syndrome (n=540; 2010–2014) or anterior acute myocardial infarction (n=2,806; 2013–2014) and created 522 age and sex-matched pairs (mean age 74.

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Unit Network sentence examples within Linear Unit Network

Linear Unit Network 10.1109/TNNLS.2021.3049719

As special cases of the general results, we obtain different classes of functions that can be approximated with recitifed linear unit networks without the curse of dimensionality.

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Linear Unit Network 10.1016/j.jco.2020.101540

While it can be shown that the approximation error converges to zero if all four parameters are sent to infinity in the right order, we demonstrate in this paper that stochastic gradient descent fails to converge for rectified linear unit networks if their depth is much larger than their width and the number of random initializations does not increase to infinity fast enough.

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Unit Network sentence examples within unit network registry

unit network registry 10.1016/j.resuscitation.2021.07.019

Among 80,716 patients who delivered to the emergency room, 935 patients treated with VA-ECMO after ROSC were included using the data from the Tokyo Cardiovascular Care Unit Network Registry between 2010 and 2017.

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unit network registry 10.1177/2048872618762638

Methods: Using the Tokyo Cardiovascular Care Unit network registry, we identified patients hospitalised with apical Takotsubo syndrome (n=540; 2010–2014) or anterior acute myocardial infarction (n=2,806; 2013–2014) and created 522 age and sex-matched pairs (mean age 74.

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Unit Network sentence examples within unit network neurobehavioral

unit network neurobehavioral 10.1177/0165025418797001

Methods: Infant neurobehavior was assessed using the Neonatal Intensive Care Unit Network Neurobehavioral Scale, and SCB was measured using the Social Responsiveness Scale (SRS).

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unit network neurobehavioral 10.1038/s41390-019-0558-6

Neonates were evaluated by the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS).

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10.1007/s10854-021-05505-5

The donor and acceptor (D-A) unit network of CPs developed through the Heck–Sonogashira cross-coupling reaction mechanism.

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10.1109/tiv.2020.3039456

Firstly, the UNIT network is trained for domain adaptation and subsequently cast into an online domain adaptation module, which narrows down the domain gap.

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10.1109/AERO.2019.8742221

Our deep generative learning framework is composed of Conditional Multimodal Deep Directional-unit Networks that extend deep generative network models to enable a general equivariance learning framework with vector-valued visible and hidden units called directional units (DUs).

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10.1109/ACCESS.2019.2940443

In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL.

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10.1109/INFOCOM.2019.8737399

First, since S3 and EFS have the same unit network-traffic price and the data transfer between S3 and EFS is free of charge, we can employ EFS as a relay for the clients’ quickly downloading large files.

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Unit Network

Encyclopedia

Jul 06, 2022

What is/are Unit Network?

Unit Network - The donor and acceptor (D-A) unit network of CPs developed through the Heck–Sonogashira cross-coupling reaction mechanism. ^[1] Firstly, the UNIT network is trained for domain adaptation and subsequently cast into an online domain adaptation module, which narrows down the domain gap. ^[2] Our deep generative learning framework is composed of Conditional Multimodal Deep Directional-unit Networks that extend deep generative network models to enable a general equivariance learning framework with vector-valued visible and hidden units called directional units (DUs). ^[3] In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL. ^[4] First, since S3 and EFS have the same unit network-traffic price and the data transfer between S3 and EFS is free of charge, we can employ EFS as a relay for the clients’ quickly downloading large files. ^[5]

long short term

Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. ^[1] To this end, we utilize bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit networks (GRU) to explore high-level and low-level features from different modalities. ^[2] As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). ^[3] Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. ^[4] Extracted TF vectors are used to train long short term memory and gated recurrent unit networks. ^[5] The results showed that the sources were successfully predicted employing a hybrid long short-term memory and gated recurrent unit network in 2018 (R2 = 97. ^[6] long short-term memory networks and gated recurrent unit networks, as well as 1D convnets. ^[7] The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. ^[8]

bidirectional gated recurrent

Specifically, we firstly extract features through Bidirectional Gated Recurrent Unit Network(Bi-GRU), multi-layer attention and linear layers. ^[1] We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. ^[2] To overcome the challenge of learning deep models and for easily multi-temporal vegetation mapping and monitoring, in this paper, we propose a Bidirectional Gated Recurrent Unit Network (BGRU) model, which is based on history information and able to deal with long-term sequential data using only few parameters to extract useful features using forward and backward gates for their automatically classification. ^[3] To alleviate the influence of inappropriate word segmentation and consecutive mine information of entire sentences, word depictions and bidirectional gated recurrent unit networks are assumed in our method. ^[4] Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. ^[5] In order to make good use of the contextual information in the sentences as much as possible, we adopt the bidirectional gated recurrent unit networks (BGRU). ^[6] Specifically, a bidirectional gated recurrent unit network with residual connection (Res-BGRU) is firstly designed to improve feature representation capability. ^[7]

convolutional neural network

In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images, and two recurrent neural networks, a long short time memory network and a gated recurrent unit network. ^[1] First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. ^[2] Multi-head attention can encode the dependencies between words, capture sentiment words in text, and using Convolutional Neural Network (CNN) and Bidirectional gated recurrent unit network (Bi-GRU) to extract local features and global semantic features of text respectively, the global average pooling layer is introduced to obtain the multi-level feature representation of the text sequence more comprehensively. ^[3] Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. ^[4] Multiple machine intelligence methods including support vector machine, decision tree, random forest, extra trees, gradient boosting machine, convolutional neural network, long short-term memory network, gated recurrent unit network were employed to separate compounds based on their cell activity. ^[5] In this paper, we propose a temporal-attention CNN-GRU (Convolutional Neural Networks and Gated Recurrent Unit Networks) approach to distinguish subtle crop differences, and the temporal attention mechanism introduced in the model can achieve the effect of enhancing phenological differences and suppressing phenological similarity. ^[6]

deep neural network

To overcome this problem, we propose a novel speech enhancement method from fused features based on deep neural network and gated recurrent unit network. ^[1] A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST. ^[2] The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases. ^[3]

Recurrent Unit Network

In step III, the simple recurrent unit network establishes forecasting models for each sub-series after feature selection and obtains the final forecasting results. ^[1] In this paper, we propose a psychology-inspired twin stream Gated Recurrent Unit network for action recognition based on the dynamic of 2D human body joints in RGB videos. ^[2] In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images, and two recurrent neural networks, a long short time memory network and a gated recurrent unit network. ^[3] Specifically, we firstly extract features through Bidirectional Gated Recurrent Unit Network(Bi-GRU), multi-layer attention and linear layers. ^[4] In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. ^[5] We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. ^[6] To overcome this problem, we propose a novel speech enhancement method from fused features based on deep neural network and gated recurrent unit network. ^[7] Finally, the gated recurrent unit network is utilized to predict radar signal labels. ^[8] Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. ^[9] In addition, this paper proposes a FT-GRU model of a gated recurrent unit network incorporating N nearest neighbor text, and a more stable model structure is obtained through system optimization. ^[10] This paper employs a novel deep learning method, the gated recurrent unit network (GRU), to forecast SWH with lead times of 3, 6, 12 and 24 h. ^[11] A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST. ^[12] We introduce LIG-Doctor, an artificial neural network architecture based on two Minimal Gated Recurrent Unit networks functioning in a bidirectional parallel manner, benefiting from temporal events both forward and backward. ^[13] Based on this situation, the mixed existing model and mutual influence of multiple modes of transportation in the city are fully considered, and a multimodal deep learning model namely MC-GRU (Multimodal Convoluted Gated Recurrent Unit Network) is proposed. ^[14] The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. ^[15] First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. ^[16] To overcome the challenge of learning deep models and for easily multi-temporal vegetation mapping and monitoring, in this paper, we propose a Bidirectional Gated Recurrent Unit Network (BGRU) model, which is based on history information and able to deal with long-term sequential data using only few parameters to extract useful features using forward and backward gates for their automatically classification. ^[17] A novel network model “bidirectional depth residuals gated recurrent unit network (BDR-GRU) ” is designed and implemented for improving the effectiveness of Image Captioning. ^[18] To alleviate the influence of inappropriate word segmentation and consecutive mine information of entire sentences, word depictions and bidirectional gated recurrent unit networks are assumed in our method. ^[19] A convolution-gated recurrent unit network (i. ^[20] developed a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) Open Access Chinese Journal of Mechanical Engineering. ^[21] To this end, we utilize bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit networks (GRU) to explore high-level and low-level features from different modalities. ^[22] As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). ^[23] Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. ^[24] The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. ^[25] For predicting trust, three machine-learning algorithms – support vector machine, eXtreme Gradient Boost, gated recurrent unit network – are trained and tested on a proactive dialogue corpus. ^[26] Multi-head attention can encode the dependencies between words, capture sentiment words in text, and using Convolutional Neural Network (CNN) and Bidirectional gated recurrent unit network (Bi-GRU) to extract local features and global semantic features of text respectively, the global average pooling layer is introduced to obtain the multi-level feature representation of the text sequence more comprehensively. ^[27] Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. ^[28] Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. ^[29] In this work, we propose a novel Attention Enhanced Convolutional Gated Recurrent Unit network (AC-GRU) for HRRP recognition which improves the representation of the spatial and temporal co-occurrence in the HRRP sequences. ^[30] Then a novel gated recurrent unit network facilitates the automatic feature extraction from temporal dynamic behaviour. ^[31] The study shows that a recurrent neural network, realized with four layers of gated recurrent unit networks, achieves (with a 5-fold cross-validation) a mean F1 score of 84. ^[32] Extracted TF vectors are used to train long short term memory and gated recurrent unit networks. ^[33] The results showed that the sources were successfully predicted employing a hybrid long short-term memory and gated recurrent unit network in 2018 (R2 = 97. ^[34] long short-term memory networks and gated recurrent unit networks, as well as 1D convnets. ^[35] In order to make good use of the contextual information in the sentences as much as possible, we adopt the bidirectional gated recurrent unit networks (BGRU). ^[36] Specifically, a bidirectional gated recurrent unit network with residual connection (Res-BGRU) is firstly designed to improve feature representation capability. ^[37] In this paper, a modified health index based hierarchical gated recurrent unit network is proposed for rolling bearing health prognosis. ^[38] The gated recurrent unit network is trained by features of input and power load from the selected group. ^[39] A deep learning algorithm known as Gated Recurrent Unit network has been proposed due to the recurrent and time-series nature of dataset. ^[40] Moreover, a deep learning forecasting model based on a gated recurrent unit network was designed to fully exploit the spatial-temporal features, and the spatial clustering was adopted to extract major features, which further improved the forecasting accuracy. ^[41] The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases. ^[42] We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. ^[43] Multiple machine intelligence methods including support vector machine, decision tree, random forest, extra trees, gradient boosting machine, convolutional neural network, long short-term memory network, gated recurrent unit network were employed to separate compounds based on their cell activity. ^[44] In this paper, a wind speed prediction model based on Gated Recurrent Unit network is built under the deep learning framework of Google-Tensorflow. ^[45] Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. ^[46] Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0. ^[47] In this paper, we propose a temporal-attention CNN-GRU (Convolutional Neural Networks and Gated Recurrent Unit Networks) approach to distinguish subtle crop differences, and the temporal attention mechanism introduced in the model can achieve the effect of enhancing phenological differences and suppressing phenological similarity. ^[48]

Care Unit Network

Among 80,716 patients who delivered to the emergency room, 935 patients treated with VA-ECMO after ROSC were included using the data from the Tokyo Cardiovascular Care Unit Network Registry between 2010 and 2017. ^[1] Methods: Using the Tokyo Cardiovascular Care Unit network registry, we identified patients hospitalised with apical Takotsubo syndrome (n=540; 2010–2014) or anterior acute myocardial infarction (n=2,806; 2013–2014) and created 522 age and sex-matched pairs (mean age 74. ^[2] Methods: Infant neurobehavior was assessed using the Neonatal Intensive Care Unit Network Neurobehavioral Scale, and SCB was measured using the Social Responsiveness Scale (SRS). ^[3] Methods: Using the patient cohort in the Tokyo Cardiovascular Care Unit Network Registry from 2013 to 2015, we identified 421 eligible patients whose data on body temperature at admission were available and classified them into three groups: high body temperature group (≥37. ^[4] Neonates were evaluated by the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS). ^[5] STUDY DESIGN This single center retrospective cohort study described neonates undergoing cardiac surgery evaluated perioperatively with the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS). ^[6]

Linear Unit Network

As special cases of the general results, we obtain different classes of functions that can be approximated with recitifed linear unit networks without the curse of dimensionality. ^[1] While it can be shown that the approximation error converges to zero if all four parameters are sent to infinity in the right order, we demonstrate in this paper that stochastic gradient descent fails to converge for rectified linear unit networks if their depth is much larger than their width and the number of random initializations does not increase to infinity fast enough. ^[2]

unit network registry

unit network neurobehavioral

Methods: Infant neurobehavior was assessed using the Neonatal Intensive Care Unit Network Neurobehavioral Scale, and SCB was measured using the Social Responsiveness Scale (SRS). ^[1] Neonates were evaluated by the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS). ^[2] STUDY DESIGN This single center retrospective cohort study described neonates undergoing cardiac surgery evaluated perioperatively with the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS). ^[3]