A new method for semantic consistency verification of aviation radiotelephony communication based on LSTM-RNN

Lu, Yujun; Shi, Yihua; Jia, Guorui; Yang, Jinfeng

doi:10.1109/icdsp.2016.7868592

Cited by 19 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simple design of RNN is shown in Fig. 12 [10]. We consider X= (x1,x2,…,xT) as the input word sequence and Y=(y1,y2,… ,yT) as the hidden sequence.…”

Section: ( )mentioning

confidence: 99%

“…LSTM-RNN is advanced to solve the vanishing gradient problem and capture long-term dependency. So, LSTM-RNN is applied to language modeling [10]. The simple design of RNN is shown in Fig.…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

“…RNN's hidden layer is the recurrent layer. Recurrent Layer is used to capture the contextual information of sentence [10].…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

“…LSTM-RNN adds output gate, forget gate, input gate and memory cell and an important information can be store over long time duration [10]. For this model, the tanh activation function is used.…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Postalcioglu

Aktas

2020

Hittite J. Sci. Eng.

View full text Add to dashboard Cite

W ith the rapid development of social networking sites, people need to explain their opinions and thoughts. People share not only information with websites but also they begin to express their thoughts and feelings more easily [1]. They expressed these posts by tweeting them on Twitter, posting on Instagram or commenting on social media. Based on this case, the feelings and thoughts of the interpreters can be analyzed [1]. Reference 11 can be shown as an example. It is possible to analyze such comments or posts according to some criteria such as what team hits, political opinion, positive/negative/neutral content of comments. This kind of analyzes may affect future production progress by enabling the public to learn the opinions, parties, and thoughts before the political election or to make a customer analysis for the products produced. It has a wide usage area. These analyses are carried out for many different purposes. With these studies, it is tried to predict the future. These analyses are referred to as "Sentiment Analysis" in terminology [1]. Sentiment analysis is one of the techniques commonly used in Natural Language Processing (NLP). NLP helps us to understand the content of a text [2]. There are different

show abstract

“…The simple design of RNN is shown in Fig. 12 [10]. We consider X= (x1,x2,…,xT) as the input word sequence and Y=(y1,y2,… ,yT) as the hidden sequence.…”

Section: ( )mentioning

confidence: 99%

“…LSTM-RNN is advanced to solve the vanishing gradient problem and capture long-term dependency. So, LSTM-RNN is applied to language modeling [10]. The simple design of RNN is shown in Fig.…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

“…RNN's hidden layer is the recurrent layer. Recurrent Layer is used to capture the contextual information of sentence [10].…”

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

Section: Cnn (Convolutional Neural Network) Contains Three Layers As the Convolutional Pooling And Full-connectedmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Postalcioglu

Aktas

2020

Hittite J. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The RNN is more suitable for the sequence data than the basic neural network and the Statistical learning method because of its structure. In this paper, we use the classic model LSTM [Lu, Shi, Jia et al (2016)].…”

Section: Rnn Neural Networkmentioning

confidence: 99%

Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

Sun¹,

Gao²,

Du³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate).

show abstract

An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation

Shi¹,

Ma²,

Ren³

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A new method for semantic consistency verification of aviation radiotelephony communication based on LSTM-RNN

Cited by 19 publications

References 4 publications

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Comparison of Neural Network Models for Nostalgic Sentiment Analysis of YouTube Comments

Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation

Contact Info

Product

Resources

About