LSTM-Based Attentional Embedding for English Machine Translation

Jian, Lihua; Xiang, Huiqun; Le, Guobin

doi:10.1155/2022/3909726

Cited by 20 publications

(4 citation statements)

References 27 publications

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“…The following method is the statistical method for the amount of thinking: the proportion of the average number of Chinese/English words in the average total number of words (the total number of Chinese and English words); the distribution of Chinese in each thinking activity (the average number of Chinese words in a thinking activity/the average total number of Chinese words); the proportion of the average number of Chinese/English words in the total amount of thinking in each thinking activity (mother tongue and autonomous English thinking). The following statistics are the average number of 15 people [ 12 – 14 ].…”

Section: Empirical Researchmentioning

confidence: 99%

[Retracted] Semantic Construal Mechanism of the Linguistic Environment Parameter Theory in Business English Translation

2022

Journal of Environmental and Public Health

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The theory of linguistic environment parameters has an important influence on English translation. Based on this parameter, it can provide guidance for the semantic generation of English translation. In the actual translation process, people’s native language thinking amount accounts for 56.2% and English thinking amount accounts for 43.8%. Translation is mostly used in texts, accounting for 40.6%, and thinking based on the context parameter theory accounts for only 3.2%. To a certain extent, it can be seen that people’s thinking about business English translation will be affected by different amounts of thinking. By mapping context parameters to corresponding factors, this paper analyzes the interpretation mechanism of translation semantics, to a certain extent, more intuitively expounds the semantic generation of business English translation, puts forward higher requirements for people’s translation ability, and further obtains new knowledge, so as to provide practical guidance for English translation.

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Section: Empirical Researchmentioning

confidence: 99%

[Retracted] Semantic Construal Mechanism of the Linguistic Environment Parameter Theory in Business English Translation

2022

Journal of Environmental and Public Health

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“…The Process of Optimizing BiLSTM Based on APPSO. Jian et al [27] put forward the variant recurrent neural network short-term memory network, which introduced a gating mechanism to simply and effectively solve the problem of gradient explosion or disappearance of the traditional recurrent neural network. The LSTM controls the information transmission between each cell through the gating mechanism.…”

Section: Data Preprocessingmentioning

confidence: 99%

Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization

Jiang,

Ji,

et al. 2023

Scientific Programming

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Aiming at the problems of large data dimension, more redundant data, and low accuracy in network traffic anomaly detection, a network traffic anomaly detection model (FR-APPSO BiLSTM) based on feature reduction and bidirectional long short-term memory (LSTM) neural network optimization is proposed. First, the feature dimensions are divided by hierarchical clustering according to the similarity distance between data features, and the features with high correlation are divided into the same feature subset. Second, an automatic encoder is used to reduce each feature subset, eliminating redundant information, and reducing the computational complexity of the detection data. Then, a particle swarm optimization algorithm based on adaptive updating of variables and dynamic adjustment of parameters (APPSO) is proposed, which is used to optimize the parameters of the bidirectional LSTM neural network (BiLSTM). Finally, the optimized BiLSTM is used as a classifier to model network traffic anomaly detection using the reduced feature data. Experiments based on NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets show that the proposed FR-APPSO-BiLSTM model can effectively reduce data features, improve the accuracy of detection, and the performance of network traffic anomaly detection.

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“…There are quite a lot of ways to use machine learning models. The field of application and experimentation is mostly image processing (Jian et al, 2022). In the economic sphere, this is an analysis of financial markets and quotations of various assets (Kaminsky et al, 2020;Kerr et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Applicability of machine learning models using a neural network for predicting the parameters of the development of food markets

Dubovitski¹,

Klimentova²,

ROGOV³

2022

J Process Man & New Technol

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Forecasting the parameters of the food market is a difficult task due to the volatility of demand, which depends on many factors. In this study, the authors attempted to implement a machine learning model based on multiple data on the food market. A boxed recurrent neural network was chosen as a prediction technique. The information basis was made up of data from 3,200 US cities for 2010-2012, reflecting characteristics that may be directly or indirectly related to the price of dairy products. The following models were used for data preprocessing, anomaly search, dimensionality reduction: AdaBoost, LogisticRegression, SVM. As a result of analytical actions, a neural network architecture has been formed for use in market forecasting: two competitive neural networks. First: 2 layers with Bidirectional GRU+Dropout. Second: 3 layers of LSTM+Dropout + Attention with skip-layers. Its use makes it possible to obtain a prediction model of the desired parameters with qualitative indicators of the validation sample - R^= 0.86. The applicability of the constructed machine learning model is considered on the example of classical agricultural production with the presentation of the stages of deployment of such a model at the enterprise level.

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LSTM-Based Attentional Embedding for English Machine Translation

Cited by 20 publications

References 27 publications

[Retracted] Semantic Construal Mechanism of the Linguistic Environment Parameter Theory in Business English Translation

[Retracted] Semantic Construal Mechanism of the Linguistic Environment Parameter Theory in Business English Translation

Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization

Applicability of machine learning models using a neural network for predicting the parameters of the development of food markets

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