Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification

He, Lin; Guo, Jiaqi; Lin, Jiaxin

doi:10.1155/2022/3576419

Cited by 3 publications

(4 citation statements)

References 26 publications

(27 reference statements)

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“…The application of IOT technology to foreign language teaching is mainly done with the help of its video and audio sensors [20]. Among them, the video sensor records the data of foreign language learners' mouth patterns when practicing pronunciation, as shown in Equation ( 1), compares this video with the standard pronunciation video data already stored in the system, and measures the pronouncer's level by calculating the difference between them, and then achieves the function of correcting pronunciation.…”

Section: Methodsmentioning

confidence: 99%

A Study on the Quality Improvement of Language Teaching in the IoT Environment

Ma¹

2022

Wireless Communications and Mobile Computing

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IoT networks and Teaching System have attracted much attention from researchers of industry and academia. As a typical application of IoT tworks and Teaching System, the system studied in this paper is a three-way platform for IoT technology, language teaching methods, and language teaching quality is incorporated into the framework of language teaching system. Based on the current stage of language teaching methods in China and its digital needs, the role of IoT-based language teaching models on language teaching quality improvement is investigated by establishing an IoT language teaching model and using conventional comparison methods to compare conventional language teaching quality improvement models with language teaching models based on IoT platforms to consider the impact of IoT technology on language teaching quality improvement. The results show that in the educational context where digitalization and intelligence are the trends, the intelligent language teaching model integrating IoT technology is much better than the language teaching model under the conventional model in terms of teaching quality, and through IoT technology, more language enthusiasts can become language learners and improve their learning ability. The work in this paper provides an important application guidance to IoT networks and Teaching System.

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Section: Methodsmentioning

confidence: 99%

A Study on the Quality Improvement of Language Teaching in the IoT Environment

Ma¹

2022

Wireless Communications and Mobile Computing

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“…He et al [24] have presented suggested the shared IoT system focused on teaching English translation with a DL-based TC. The goal was to further the reform of English translation instruction while adjusting to the present state of social improvement.…”

Section: Literature Reviewmentioning

confidence: 99%

Socialized Teaching of English Learning in Mobile Internet Technology

Jing Tang

2024

jes

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Distractions and multitasking can hinder the effectiveness of language learning, requiring innovative strategies to engage and maintain learners focus. Equitable access to mobile internet technology and ensuring accurate and culturally appropriate language learning materials are challenges in the socialized teaching of English learning in mobile internet technology. To overcome this issue, present for Socialized Teaching of English Learning in Mobile Internet Technology (STEL-MIT) is proposed. Initially, the data is collected from English education database. In pre-processing segment; it removes the noise and enhances the input data’s utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).Afterward, the data’s are fed to pre-processing. In pre-processing segment; it eliminates the missing data and enhances the input data’s utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).The outcome from the pre-processing data is transferred to the ITFCZNN. The listening, speaking, reading, writing are successfully classified by using ITFCZNN. The MCOA is used to optimize the weight parameter of ITFCZNN. The proposed method is implemented using MATLAB, and several performance metrics, including accuracy, precision, recall, sensitivity, F1 score, and calculation time, are used to determine the proposed STEL-MIT method's efficiency. Proposed STEL-MIT method attains higher accuracy 16.65%, 18.85% and 16.45%; higher sensitivity 16.34%, 12.23%, and 19.12%; higher precision 14.89%, 16.89% and 20.67%,higher recall 16.34%, 12.23%, and 18.54% analysed to the existing methods, such as Access to Online Learning: Machine Learning Analysis from a Social Justice Perspective (AOL-ML-SJP), Design of Online Intelligent English Teaching Platform Based on Artificial Intelligence Techniques (OIET-AI), and Novel Attack Finding System for Investigating Pedagogical Challenges of Mobile Technology to English Teaching (IPC-MT-ET), respectively.

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“…The equation is the gradient of the weights between the 𝑖 𝑡ℎ input layer neuron and the 𝑗 𝑡ℎ hidden layer of the translation strategies for English language movies and 𝑡 𝑖 is the input of the 𝑖 𝑡ℎ neuron as presented in Equation (12). The weights all the connections will be updated based on the gradients of the weight as presented in the Equation (13).…”

Section: Figure 1: English Language Movie Translation Modelmentioning

confidence: 99%

“…Today's educators need to be flexible in their approach to teaching, familiar with cuttingedge information technology, and able to swiftly adapt to different contexts. The four main ways in which cloud computing is making a difference in college English classrooms are by raising students' awareness of the importance of active participation; removing physical barriers to learning; providing access to a wealth of educational and instructional resources; and fostering a multiplicity of interactions between students and teachers [12]. This is mostly because of how college-aged students' perspectives have shifted as a result of learning English in the cloud.…”

Section: Introductionmentioning

confidence: 99%

Translation Strategies for English-Language Movies Under the Application of Mobile Cloud Computing Combined with Deep Learning

Liu

2022

Preprint

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Translation strategies make the individual feel convenient in visualising movies in different languages. The challenge in this translation is in mapping the language words in relation to the language presented in the movie. It is also necessary to consider the mode utilised for viewing the movie. In this research, individuals are considered to make use of mobile devices to visualise the movie with the choice of language translation. In this perspective, Software-as-a-Service (SaaS) cloud computing technology is utilised for storing the data in the cloud environment and making it available as on-demand data. To make an effective translation of the English movie, Multi-Layer Perceptron (MLP), which is a basic deep learning model, is considered for training the translator system for the video data available in the cloud. The proposed model of SaaS with MLP is compared with the regular Recurrent Neural Network (RNN) model for accuracy in translation of the language. The comparative analysis shows that the proposed model has obtained an accuracy of 83%, which is higher than the RNN model.

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Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification

Cited by 3 publications

References 26 publications

A Study on the Quality Improvement of Language Teaching in the IoT Environment

A Study on the Quality Improvement of Language Teaching in the IoT Environment

Socialized Teaching of English Learning in Mobile Internet Technology

Translation Strategies for English-Language Movies Under the Application of Mobile Cloud Computing Combined with Deep Learning

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