RNN-LSTM-GRU based language transformation

Khan, Arshad; Sarfaraz, Aaliya

doi:10.1007/s00500-019-04281-z

Cited by 26 publications

(9 citation statements)

References 28 publications

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“…e former involves the tree search algorithm, and the connection part of the two modules is the attention force mechanism. While the latter often applies important technologies such as word vectors and long-and short-term memory [4] networks. e encoder transforms the input source language text into vector representation in vector space through a neural network.…”

Section: Analysis Of Traditional Neural Machine Translation Methodsmentioning

confidence: 99%

“…e rise and development of machine translation after World War II mainly benefited from the invention and application of the first batch of computers [4]. At the same time, the development of cryptography during World War II and the interest in language research led people to realize that machine translation can be seen as a process of coding in one language and decoding in another.…”

Section: Introductionmentioning

confidence: 99%

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Research on Transfer Learning-Based English-Chinese Machine Translation

Peiting

2022

Mobile Information Systems

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In order to solve the problems of time-consuming and frequent mistranslations in traditional translation methods, this study designed an English-Chinese machine translation method based on transfer learning. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning. On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The benefit of the proposed method includes a short translation time and fewer mistranslations.

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Section: Analysis Of Traditional Neural Machine Translation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Transfer Learning-Based English-Chinese Machine Translation

Peiting

2022

Mobile Information Systems

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“…The LSTM-RNN is one of the most powerful neural network models that is used in cyber security due to its ability to accurately model temporal sequences and their long-term dependencies [44]. However, LSTM usually takes a longer time for model training and high computation cost [45].…”

Section: Proposed Modelmentioning

confidence: 99%

Towards a Hybrid Machine Learning Model for Intelligent Cyber Threat Identification in Smart City Environments

Al-Taleb

Saqib

2022

Applied Sciences

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The concept of a smart city requires the integration of information and communication technologies and devices over a network for the better provision of services to citizens. As a result, the quality of living is improved by continuous analyses of data to improve service delivery by governments and other organizations. Due to the presence of extensive devices and data flow over networks, the probability of cyber attacks and intrusion detection has increased. The monitoring of this huge amount of data traffic is very difficult, though machine learning algorithms have huge potential to support this task. In this study, we compared different machine learning models used for cyber threat classification. Our comparison was focused on the analyzed cyber threats, algorithms, and performance of these models. We have identified that real-time classification, accuracy, and false-positive rates are still the major issues in the performance of existing models. Accordingly, we have proposed a hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve threat classification performance. Our model was based on a convolutional neural network (CNN) and quasi-recurrent neural network (QRNN). The use of QRNN not only resulted in improved accuracy but also enabled real-time classification. The model was tested on BoT-IoT and TON_IoT datasets, and the results showed that the proposed model outperformed the other models. Due to this improved performance, we emphasize that the application of this model in the real-time environment of a smart system network will help in reducing threats in a reasonable time.

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“…The reset gate is used to decide how much of the past information should be forgotten or remembered. The update gate determines how much of the information from the previous time steps should be transferred to the future (Khan and Sarfaraz, 2019). The network structure of the GRU consists of blocks of gated recurrent units for memory reset and updating control.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

Arifoğlu

Kandemir

2022

Mehmet Akif Ersoy Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Day-Ahead Market offers electricity market participants the opportunity to trade electricity one day ahead of real-time. For each hour, a separate Market Clearing Price is created in Day-Ahead Market. This study aims to predict the hourly Market Clearing Price using deep learning techniques. In this context, 24-hour Market Clearing Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had the best average forecasting performance with an 8.15 MAPE value, according to the results obtained. MLP followed the LSTM with 8.44 MAPE, GRU with 8.72 MAPE, and CNN with 9.27 MAPE. In the study, the provinces where the power plants producing with renewable resources are dense were selected for meteorological variables. It is expected that the trend towards electricity generation with renewable resources will increase in the future. In this context, it is thought important for market participants to consider the factors that may affect the production with these resources in the electricity price forecasting.

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RNN-LSTM-GRU based language transformation

Cited by 26 publications

References 28 publications

Research on Transfer Learning-Based English-Chinese Machine Translation

Research on Transfer Learning-Based English-Chinese Machine Translation

Towards a Hybrid Machine Learning Model for Intelligent Cyber Threat Identification in Smart City Environments

Electricity Price Forecasting in Turkish Day-Ahead Market via Deep Learning Techniques

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