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Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

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ENTERTAINMENT COMPUTING 2024年 50卷

作者： Xian, Hui Wu, Shukun Tangshan Normal Univ Sch Foreign Languages Tangshan 063000 Hebei Peoples R China

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

关键词： Word bag model recurrent neural network algorithm English vocabulary Learning recommendation system

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Classification of Fraud Websites using Linear regression algorithm and recurrent neural network algorithm with Improved Accuracy

Classification of Fraud Websites using Linear regression Alg...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Roshan, M D.Raaquib Loganayagi, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350309140

The purpose of this work is to improve the detection of fraud websites using Novel Linear Regression algorithm and recurrent neural network algorithm. Materials and Methods: Novel Linear regression algorithm and recurrent neural network algorithm is executed with varying training and testing splits for predicting the detection of fraud websites. This research utilizes Novel Linear regression algorithm and recurrent neural network algorithm with various grounding categories to predict the detection of extremist reviews. The data set in this article utilizes kaggle website data csv file repositories and number of samples (N=11053) The Gpower test used is about 85% (g power setting parameters: α=0.05 and power=0.85). Results: Novel Linear regression algorithm (98.15%) has the increased accuracy over recurrent neural network algorithm (97.22%) with an independent samples-t-test significance value of p=0.01 (p © 2024 IEEE.

关键词： Deception Fraud Detection Novel Linear regression algorithm recurrent neural network algorithm Social Protection Swindling

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Exploration of English speech translation recognition based on the LSTM RNN algorithm

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neural COMPUTING & APPLICATIONS 2023年第36期35卷 24961-24970页

作者： Yuan, Qiwei Dai, Yu Li, Guangming Shaoyang Univ Coll Foreign Language Shaoyang 422000 Hunan Peoples R China Hunan Univ Med Coll Int Educ Huaihua 418000 Hunan Peoples R China Shaoyang Univ Dept Mech & Energy Engn Shaoyang 422000 Hunan Peoples R China

In today's information society, the demand for intelligence is increasing daily. English speech translation recognition technology based on the LSTM (long short-term memory) recurrent neural network (RNN) algorithm is an important manifestations of computer intelligence. In recent years, many scholars have conducted research on speech translation recognition technology, including template matching and statistical pattern recognition. Each of these methods has its drawbacks. This paper discusses English speech recognition techniques by utilizing the basic RNN principles. Moreover, its application and construction in practice, which can provide some useful reference for future researchers, are analysed. LSTM RNN is an intelligent system that is different from traditional pattern recognition methods. The greatest difference is that it simulates the information processing of the human brain and realizes the intelligent information processing in a distributed manner. It has a variety of automatic recognition and extraction functions, such as storage, association, and retrieval, especially for speech translation and recognition problems with high perception ability. This new neural network recognition system has a strong scientific nature and can store sound information in a decentralized manner, similar to the human brain. The LSTM RNN has been widely used in the speech recognition field due to its excellent performance in extraction and classification. The study found that the recognition accuracy of the original RNN was generally maintained between 48 and 54%, and the data loss rate was relatively high. The accuracy rate of speech recognition based on LSTM RNN was as high as 94%, and the information storage efficiency was high, which greatly avoided repetitive processes. The voice data processing speed can be completed in 4.5 s at the fastest, which plays an important role in terms of mass satisfaction and social development needs.

关键词： LSTM (long short-term memory) recurrent neural network algorithm English voice translator Translation recognition system

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