966: Continuous Risk of Desaturation Within the Next Hour Prediction Using a Recurrent Neural Network

Ehrlich, Lili; Ledbetter, David; Aczon, Melissa; Laksana, Eugene; Wetzel, Randall C.

doi:10.1097/01.ccm.0000729752.70034.e3

Cited by 4 publications

(2 citation statements)

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“…A word-based statistical translation system begins with a lexical correspondence between languages and extracts parts of the translated language from the data as the basic unit of machine translation because a translated phrase pair can directly record a rich translation phenomenon compared to the lexical level. e machine translation system and translation performance have improved signi cantly [2]. However, phrase-based translation systems also face some problems.…”

Section: Introductionmentioning

confidence: 99%

Design of English Translation Model Based on Recurrent Neural Network

Wang

2022

Mathematical Problems in Engineering

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In order to improve the accuracy and stability of English translation, this paper proposes an English translation model based on recurrent neural network. Based on the end-to-end encoder-decoder architecture, a recursive neural network (RNN) English machine translation model is designed to promote machine autonomous learning features, transform the distributed corpus data into word vectors, and directly map the source language and target language through the recurrent neural network. Selecting semantic errors to construct the objective function during training can well balance the influence of each part of the semantics and fully consider the alignment information, providing a strong guidance for the training of deep recurrent neural networks. The experimental results show that the English translation model based on recurrent neural network has high effectiveness and stability. Compared with the baseline system, it has improved about 1.51–1.86 BLEU scores. Conclusion. The model improves the performance and quality of English machine translation model, and the translation effect is better.

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Section: Introductionmentioning

confidence: 99%

Design of English Translation Model Based on Recurrent Neural Network

Wang

2022

Mathematical Problems in Engineering

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“…(6) ZB6 is used to represent the intensity of environmental protection, and the investment in environmental pollution control is expressed as the proportion of industrial GDP, for which the unit is % [28].…”

Section: Creation Of Carbon Emission Information Indicatorsmentioning

confidence: 99%

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang

Cheng

2021

Land

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This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

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The Security Situation Prediction of Network Mathematical Neural Model Based on Neural Network

Sun¹

2021

2021 International Conference on Applications and Techniques in Cyber Intelligence

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966: Continuous Risk of Desaturation Within the Next Hour Prediction Using a Recurrent Neural Network

Cited by 4 publications

References 0 publications

Design of English Translation Model Based on Recurrent Neural Network

Design of English Translation Model Based on Recurrent Neural Network

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

The Security Situation Prediction of Network Mathematical Neural Model Based on Neural Network

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